Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Miller, Kurtis Henry
Master of Applied Science - MASc
University of British Columbia
This thesis presents a study of SPT hammer efficiency measurement methods in a set of innovative underground test chambers. The standard stress wave method of hammer efficiency measurement was used alongside hammer impact velocity measurement to create a new database of 1,263 automatic hammer SPT blows. These data were collected with drill rod lengths of 8.5, 16.2 and 32 m. Previous researchers have concluded that estimating SPT hammer efficiency by measuring impact velocity gives equal or slightly better uncertainty in N₆₀ at a lower cost than direct stress wave measurement. The collected dataset confirms this conclusion. Impact velocity is shown to be a reliable indicator of stress wave energy. Measurement of impact velocity is shown to be an attractive complement (if not full replacement) to direct stress wave measurement due to lower measurement uncertainty and the method’s relative simplicity. The hammer impact velocity is measured with a string potentiometer, a high-speed camera, and proprietary sensors integral to a commercially available automatic SPT hammer (the eSPT from MARL Technologies). The stress wave energy is measured using two different devices, the SPT Analyzer (PDI Inc.) and another from Northwoods Instruments. Using two different stress wave devices shows the inherent uncertainty in SPT stress wave measurement. This uncertainty is best seen with the 16.2 m drill rod, where the two devices produced a 5.2% difference in average ETR. The entire dataset has an average post-impact efficiency (e2, stress wave energy divided by impact kinetic energy) of 96.4% (1,263 blows, standard deviation = 4.0%). With this hammer, applying a 96.4% correction to the measured impact velocity will provide a better estimate of ETR than direct measurement. The same logic should apply to all automatic hammers if a tool for impact velocity measurement is available. The innovative test site used to collect these data has several benefits to traditional research methods. Three closed, cased boreholes were installed to different depths of interest, and a basket of energy-dissipating media was placed at the bottom of each borehole. Without further drilling, the test holes can now be used for testing with different hammers, drill rods, or LPT-type tests.
AN INVESTIGATION OF STANDARD PENETRATION TEST HAMMER EFFICIENCY THROUGH MEASUREMENT OF IMPACT VELOCITY AND STRESS WAVE ENERGY by Kurtis Henry Miller B.A.Sc., The University of British Columbia, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) May 2022 © Kurtis Henry Miller, 2022 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: AN INVESTIGATION OF STANDARD PENETRATION TEST HAMMER EFFICIENCY THROUGH MEASUREMENT OF IMPACT VELOCITY AND STRESS WAVE ENERGY submitted by KURTIS HENRY MILLER in partial fulfillment of the requirements of the degree of Master of Applied Science. Dr. Dwayne Tannant, School of Engineering Supervisor Dr. Christopher Ryan Daniel, BC Hydro Co-Supervisor Dr. Sumi Siddiqua, School of Engineering Supervisory Committee Member Dr. John A. Howie, Department of Civil Engineering University Examiner iii Abstract This thesis presents a study of SPT hammer efficiency measurement methods in a set of innovative underground test chambers. The standard stress wave method of hammer efficiency measurement was used alongside hammer impact velocity measurement to create a new database of 1,263 automatic hammer SPT blows. These data were collected with drill rod lengths of 8.5, 16.2 and 32 m. Previous researchers have concluded that estimating SPT hammer efficiency by measuring impact velocity gives equal or slightly better uncertainty in N60 at a lower cost than direct stress wave measurement. The collected dataset confirms this conclusion. Impact velocity is shown to be a reliable indicator of stress wave energy. Measurement of impact velocity is shown to be an attractive complement (if not full replacement) to direct stress wave measurement due to lower measurement uncertainty and the method’s relative simplicity. The hammer impact velocity is measured with a string potentiometer, a high-speed camera, and proprietary sensors integral to a commercially available automatic SPT hammer (the eSPT from MARL Technologies). The stress wave energy is measured using two different devices, the SPT Analyzer (PDI Inc.) and another from Northwoods Instruments. Using two different stress wave devices shows the inherent uncertainty in SPT stress wave measurement. This uncertainty is best seen with the 16.2 m drill rod, where the two devices produced a 5.2% difference in average ETR. The entire dataset has an average post-impact efficiency (e2, stress wave energy divided by impact kinetic energy) of 96.4% (1,263 blows, standard deviation = 4.0%). With this hammer, applying a 96.4% correction to the measured impact velocity will provide a better estimate of ETR than direct measurement. The same logic should apply to all automatic hammers if a tool for impact velocity measurement is available. The innovative test site used to collect these data has several benefits to traditional research methods. Three closed, cased boreholes were installed to different depths of interest, and a basket of energy-dissipating media was placed at the bottom of each borehole. Without further drilling, the test holes can now be used for testing with different hammers, drill rods, or LPT-type tests. iv Lay Summary The Standard Penetration Test is used globally as the most common in-situ soil characterization technique for foundation design and ground stability assessment. However, there is still potential for further standardization of the SPT to increase the value of SPT data. The research performed for this thesis aimed to confirm the conclusions of a recent study that proposed an updated method of SPT hammer efficiency measurement. This thesis presents a new database filling a significant gap in the SPT research literature. Data were collected at a novel test site at MARL Technologies in Edmonton, Alberta. The data show that measurement of impact velocity can either complement or partially replace stress wave measurement while also improving the understanding of the precision and bias of the final N60 value. Hopefully, these results will contribute to an updated standard test methodology. v Acknowledgements Thank you to my parents, Bruce and Tracy, and their parents for their invaluable and valuable support throughout my education. Thank you to Dr. Chris Daniel for being the most recent and critical link in the SPT research tradition leading to this thesis. Thank you to Drs. Tannant and Daniel for their counsel and expertise as my research supervisors. Thank you to MARL for their innovative work in SPT research and development, which inspired this research topic, and their hospitality while I used their test facility in Edmonton. Thank you especially to Scott Hughes at MARL for laying the groundwork for the eSPT verification and his readiness to clear his schedule and assist the data collection and analysis process. Thank you to Dr. Tannant and the UBC School of Engineering for generously providing me with a stipend while I took my courses for this master’s program. Glory be to God for all things. Amen. vi Preface The research was performed under the supervision of Dr. Dwayne Tannant. The test site was designed in partnership between MARL Technologies and Dr. Chris Daniel. The eSPT hammer used to collect impact velocity data was developed by MARL Technologies. The general design of the research program was developed by Dr. Chris Daniel. The author of this thesis was responsible for the detailed planning of the data collection and quality control program and the data analysis. Ethics approval from the UBC Research Ethics Board was not required. The conference paper publications related to this research are listed below: Miller, K., Tannant, D., Innes, M., Hughes, S., Daniel, C. (2021). Verification of electronic SPT hammer accuracy and reliability. Canadian Geotechnical Society 2021 Annual Conference, September 27-29, 2021, Niagara Falls, ON. Miller, K., Daniel, C., Innes, M., Hughes, S., Tannant, D. (2021). Measuring the efficiency of the standard penetration test (SPT): A novel facility for comparing measurement methods. Canadian Institute of Mining and Metallurgy 2021 Annual Conference. (Abstract and presentation) vii Table of Contents Abstract .................................................................................................................................. iii Lay Summary ............................................................................................................................... iv Acknowledgements ....................................................................................................................... v Preface .................................................................................................................................. vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ ix List of Figures ................................................................................................................................ x List of Supplementary Materials ............................................................................................... xii List of Equations ........................................................................................................................ xiii List of Symbols ........................................................................................................................... xiv Chapter 1: Introduction ............................................................................................................. 1 1.1 Standard Penetration Test ................................................................................................. 1 1.2 Estimation of SPT Hammer Efficiency ............................................................................ 2 1.3 Research Objectives ......................................................................................................... 4 1.4 Thesis Organization .......................................................................................................... 5 Chapter 2: Literature Review .................................................................................................... 7 2.1 Stress Wave Measurement ............................................................................................... 7 2.2 Relationship Between Impact and Stress Wave Energies .............................................. 11 2.3 SPT Hammer Impact Velocity Measurement ................................................................ 13 2.4 Test Holes or Chambers ................................................................................................. 15 2.5 Effect of SPT Drill Rods on Energy Transfer Efficiency .............................................. 15 Chapter 3: Testing Equipment and Methodology .................................................................. 18 3.1 Overview and Definition of Terms ................................................................................ 18 3.2 Testing Facility ............................................................................................................... 19 3.2.1 Drill Rig .................................................................................................................. 19 3.2.2 Drill Rods and Equipment ...................................................................................... 20 3.2.3 Cased Test Holes and Test Medium ....................................................................... 23 3.3 eSPT Verification Method ............................................................................................. 26 3.3.1 String Potentiometer ............................................................................................... 27 3.3.2 High-Speed Camera ................................................................................................ 29 3.3.3 Penetration Distance and Blow Count Measurement ............................................. 33 3.4 Stress Wave Energy Measurement ................................................................................. 34 3.4.1 Pile Driving Analyzer ............................................................................................. 36 3.4.2 Northwoods Instruments ......................................................................................... 46 3.4.3 Data Processing ....................................................................................................... 51 Chapter 4: Results of Verification of MARL’s Electronic SPT ............................................ 53 4.1 String Potentiometer eSPT Verification ......................................................................... 53 viii 4.2 High-Speed Camera eSPT Verification ......................................................................... 59 4.3 Penetration Distance and Blow Count Verification ....................................................... 66 4.4 Summary of eSPT Verification ...................................................................................... 67 Chapter 5: Analysis and Interpretation of the Impact and Stress Wave Energy Data ...... 69 5.1 Overview of Impact Energy and Stress Wave Energy Data .......................................... 69 5.1.1 Box and Whisker Plots of Impact Energy and Stress Wave Energy ...................... 71 5.2 Breakdown of Collected Data by Rod Length ............................................................... 74 5.2.1 8.5 m Rod Length ................................................................................................... 74 5.2.2 16.2 m Rod Length ................................................................................................. 76 5.2.3 32 m Rod Length .................................................................................................... 78 5.3 Statistical Analysis of the Collected Data ...................................................................... 79 5.4 Comparison to Other Results: e2 .................................................................................... 84 5.5 Secondary Discussion of Results ................................................................................... 88 5.5.1 Effect of Initial Sampler Penetration ...................................................................... 88 5.5.2 Short Rod Effect ..................................................................................................... 90 5.5.3 Drill Rod Joint Tightness ........................................................................................ 92 5.5.4 F-squared Energy Discrepancy ............................................................................... 92 5.6 Summary of Data Interpretation ..................................................................................... 93 Chapter 6: Suggestions for Future Research .......................................................................... 95 6.1 Impact Velocity Tools .................................................................................................... 95 6.2 Use the MARL Test Site for Future Research ............................................................... 95 6.3 Test With Shorter Drill Rods ......................................................................................... 96 6.4 Investigate the Effect of Tape on Drill Rod Joints ......................................................... 96 6.5 Use of Different Media .................................................................................................. 96 Chapter 7: Conclusion .............................................................................................................. 98 7.1 Limitations ................................................................................................................... 100 References ............................................................................................................................... 101 Appendices ............................................................................................................................... 107 Appendix A: Supplementary Data .......................................................................................... 107 Appendix B: Tabulated Database ........................................................................................... 111 ix List of Tables Table 3-1. SPT stress wave measurement devices used in this thesis .......................................... 35 Table 3-2. Force and velocity data comparison: PDA and Northwoods ...................................... 52 Table 4-1. Summary of the eSPT verification testing program .................................................... 53 Table 4-2. Maximum velocity: eSPT and string potentiometer .................................................... 58 Table 4-3. Maximum velocity: eSPT and high-speed camera ...................................................... 64 Table 5-1. Size of ETR and impact velocity data sets for each stress wave device and rod length....................................................................................................................................................... 70 Table 5-2. PDA and eSPT summary data with 8.5 m rod length .................................................. 75 Table 5-3. Northwoods and eSPT summary data with 8.5 m rod length ...................................... 75 Table 5-4. PDA and eSPT summary data with 16.2 m rod length ................................................ 77 Table 5-5. Northwoods and eSPT summary data with 16.2 m rod length .................................... 77 Table 5-6. PDA summary data with 32 m rod length ................................................................... 78 Table 5-7. Northwoods and eSPT summary data with 32 m rod length ....................................... 79 Table 5-8. Variance in ETR and e1 ................................................................................................ 80 Table 5-9. Comparison of σ(ETR) results to literature ................................................................. 81 Table 5-10. ETR and e1 statistics: mean, standard deviation, and confidence interval ................. 82 Table 5-11. ETR relationship to normal distribution .................................................................... 83 Table 5-12. ETR and e1 statistics: median and 75th percentile ...................................................... 84 Table 5-13. e2 values for each rod length and device ................................................................... 85 Table 5-14. e2 values for each combination of rod length and device .......................................... 85 Table 5-15. Comparison of e2 results to literature ........................................................................ 88 Table 5-16. Average blow count (N) per test for each rod length ................................................ 89 Table 5-17. Time to max FVenergy vs. rod length with Northwoods device ............................... 91 Table 5-18. Comparison of F-squared and FV energies ............................................................... 93 x List of Figures Figure 1. SPT driving shoe, sampler, AW coupler ....................................................................... 20 Figure 2. eSPT anvil with AW coupler ......................................................................................... 21 Figure 3. eSPT Automatic hammer, copyright MARL Technologies .......................................... 22 Figure 4. Three cased test holes at the test site in Edmonton ....................................................... 23 Figure 5. Clean steel shot before use ............................................................................................ 24 Figure 6. Media basket with couplers for the lift cable ................................................................ 25 Figure 7. String potentiometer mounted on eSPT hammer: transparent guide tube, copyright MARL Technologies .................................................................................................................... 28 Figure 8. Frame from a close-up video just before impact ........................................................... 30 Figure 9. Curve matching example (Figure 28: Drop 1: eSPT and camera impact velocity)....... 32 Figure 10. Manual penetration depth measurement ...................................................................... 34 Figure 11. PDA instrumented subassembly .................................................................................. 37 Figure 12. New taped AW coupler below the PDA instrumented subassembly .......................... 38 Figure 13. PDA ETR vs blow number: complete chronological sequence ................................... 40 Figure 14. PDA acceleration traces with 8.5 m rod length: entire 150 ms record not shown ...... 41 Figure 15. PDA FFT of acceleration with 8.5 m rod length ......................................................... 42 Figure 16. PDA 8.5 m typical blow: quality assessment of energy data at three scales ............... 44 Figure 17. PDA 16.2 m typical blow: quality assessment of energy data at three scales ............. 45 Figure 18. Northwoods ETR complete chronological sequence ................................................... 46 Figure 19. Northwoods acceleration trace with 8.5 m rod length: entire 80 ms record not shown....................................................................................................................................................... 47 Figure 20. Northwoods FFT of acceleration with 8.5 m rod length ............................................. 47 Figure 21. Northwoods 8.5 m typical blow: quality assessment of energy data at three scales ... 49 Figure 22. Northwoods 16.2 m typical blow: quality assessment of energy data at three scales . 50 Figure 23. String potentiometer and eSPT displacement and velocity: Drop 1 ............................ 54 Figure 24. String potentiometer and eSPT displacement and velocity: Drop 2 ............................ 55 Figure 25. String potentiometer and eSPT impact velocity: Drop 1 ............................................. 56 Figure 26. String potentiometer and eSPT impact velocity: Drop 2 ............................................. 56 Figure 27. Close-up string potentiometer and eSPT impact velocity: Drop 1 .............................. 57 Figure 28. Close-up string potentiometer and eSPT impact velocity: Drop 2 .............................. 57 Figure 29. String potentiometer and eSPT squared displacement residual: Drop 1 ..................... 59 Figure 30. Drop 1: eSPT and camera impact velocity, 960 fps .................................................... 60 xi Figure 31. Drop 2: eSPT and camera impact velocity, 480 fps .................................................... 61 Figure 32. Drop 3: eSPT and camera impact velocity, 480 fps .................................................... 61 Figure 33. Drop 4: eSPT and camera impact velocity, 480 fps .................................................... 62 Figure 34. Drop 5: eSPT and camera impact velocity, 480 fps .................................................... 62 Figure 35. Drop 2: eSPT and camera impact velocity close-up, 480 fps...................................... 63 Figure 36. Drop 4: eSPT and camera impact velocity closeup, 480 fps. ...................................... 63 Figure 37. Triangulation of eSPT impact velocity ........................................................................ 65 Figure 38. Overview histograms of ETR and e1 data. ................................................................... 71 Figure 39. Box plots of ETR (blue) and e1 (orange) ..................................................................... 73 Figure 40. Histograms of ETR and e1 with 8.5 m rod length ........................................................ 75 Figure 41. PDA ETR complete chronological sequence (shown again) ....................................... 76 Figure 42. Histograms of ETR and e1 with 16.2 m rod length ...................................................... 77 Figure 43. Histograms of ETR and e1 with 32 m rod length ......................................................... 78 Figure 44. Standard normal distribution: confidence interval for a z-score of 1 .......................... 81 Figure 45. e2 histograms for each device separately, and all data ................................................ 86 Figure 46. e2 histograms for each device with separate rod lengths ............................................. 86 Figure 47. String potentiometer datasheet, Posital Fraba ........................................................... 108 Figure 48. PDI accelerometer calibration sheet one ................................................................... 108 Figure 49. PDI accelerometer calibration sheet two ................................................................... 109 Figure 50. PDI force calibration sheet ........................................................................................ 110 Figure 51. Northwoods calibration sheet .................................................................................... 111 xii List of Supplementary Materials A spreadsheet containing all the data collected for this thesis is supplied separately. xiii List of Equations Eq. 1 𝑁60 =𝑁×ETR%60% Eq. 2 𝐸𝑇𝑅 =𝐹𝑉 𝑒𝑛𝑒𝑟𝑔𝑦𝐸∗, where 𝐸 ∗ is the standard SPT energy (475 J) Eq. 3 𝐹𝑉𝑒𝑛𝑒𝑟𝑔𝑦 = max {∫ (𝑓 × 𝑣) 𝑑𝑡∞0} Eq. 4 𝑒2 =𝐹𝑉 𝑒𝑛𝑒𝑟𝑔𝑦hammer′s impact KE Eq. 5 𝑒1 =𝐾𝐸E∗ Eq. 6 𝐸𝑇𝑅 = 𝑒1 ∗ 𝑒2 =𝐾𝐸𝐸∗∗𝐹𝑉𝐾𝐸=𝐹𝑉E∗, where E ∗= 475 J Eq. 7 KE = 1 2⁄ 𝑚𝑣𝑖𝑚𝑝𝑎𝑐𝑡2 Eq. 8 𝑣𝑖𝑚𝑝𝑎𝑐𝑡 = max (𝑣 = (𝜕𝑥𝜕𝑡)) Eq. 9 𝑒2 =𝐹𝑉𝑒𝑛𝑒𝑟𝑔𝑦12⁄ 𝑚𝑣𝑖𝑚𝑝𝑎𝑐𝑡2 Eq. 10 velocity = −(positiont−positiont−1)(timet−tt−1) Eq. 11 Scaled velocity = velocity ∗ 𝑍 , where 𝑍 =𝐸𝑚∗𝐴𝑟𝑜𝑑𝑐 Eq. 12 Max. bias error = 1 −3.87m/s−9.81m/s2∗7.5ms3.87m/s= 1 −3.79m/s3.87m/s= 1.9% Given: 12∗ 63.5kg ∗ (3.87m/s)2 = 475 Joules = E ∗ Eq. 13 𝑍 =𝑥−μσ Eq. 14 Confidence Interval = 𝑃(−𝑍 < 𝑥 < 𝑍) Eq. 15 Percentile Confidence Interval = (𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑟𝑎𝑛𝑘 − 50%) ∗ 2 Eq. 16 𝑒2𝑚 =𝐸𝑇𝑅𝑒1+𝐸𝑅ℎ𝑠 where 𝐸𝑅ℎ𝑠 =0.4𝑁 Eq. 17 𝐹2𝑒𝑛𝑒𝑟𝑔𝑦 = ∫ (𝑓 × 𝑓) 𝑑𝑡2L/c0 xiv List of Symbols μ – average (mean) σ – sample standard deviation (also referred to as SD) 2L/c – theoretical time for stress reflections to return to the instruments from the sampler c – speed of stress wave propagation in steel (5,123 m/s or 16,800 ft/s) CV – coefficient of variation (sample standard deviation divided by mean) E* - standard SPT energy (475 Joules, 63.5 kg (140 lb) hammer dropping 762 mm (30-inches)) e1 – pre-impact efficiency e2 – post-impact efficiency (ETR divided by e1) Em – Young’s modulus (assumed as 30,000 ksi in steel) eSPT – Automatic SPT hammer manufactured by MARL technologies which measures the impact velocity, drop height, and penetration distance for each blow ETR – energy transfer ratio (stress wave (FV) energy divided by the standard SPT energy) ERHS –Hammer set energy ratio (approximated as 0.4/N for the SPT) f - force FFT – Fast Fourier Transform (from time to frequency domain) FMX – maximum force FVenergy – maximum stress wave energy transferred to the SPT drill rods during impact (measured by the standard force-velocity method) F2energy - maximum stress wave energy transferred to the SPT drill rods during impact (measured by the old force-squared method) g –gravitational constant (9.81 m/s2) h – height impact velocity – the maximum velocity of the hammer at (or just before) the hammer-anvil impact. KE – kinetic energy (kinetic energy of the hammer at impact) xv L – length of the drill rods from instruments to the bottom of the sampler m – mass MARL (Technologies) – Mobile Augers and Research Limited, manufacturer of the eSPT N – SPT blow count from 150 to 450 mm penetration (6 to 18 inches) Northwoods (Instruments) – SPT stress wave energy measurement device manufacturer PDA – Pile Driving Analyzer (technically, the model used is called the SPT Analyzer) SPT – Standard Penetration Test t0 – initial time v – velocity VMX – maximum velocity 1 Chapter 1: Introduction 1.1 Standard Penetration Test The Standard Penetration Test (SPT) is prevalent worldwide for subsurface exploration and soil characterization. Its popularity is due to its simplicity and low expense, despite the difficulty of measuring soil’s in-situ properties without disturbance. A steel sample tube is hammered into the undisturbed ground at the bottom of a borehole (ASTM, 2018). The number of blows required to advance the sample tube a standard distance into the soil is recorded as the SPT blow count (N). Engineers use the blow count in empirically derived relationships to infer soil properties such as friction angle, shear strength, compressive strength, and liquefaction potential (Das & Sivakugan, 2016). The inferred soil properties are used to design foundations and assess ground stability. The SPT uses a split spoon sampler of 50 mm (2 inches) in diameter and between 0.482 and 0.812 m (19 and 32 inches) in length (ASTM D1586, 2018). The split spoon sampler is split open after the test to recover what is considered a “highly disturbed” soil sample (Das & Sivakugan, 2016). The SPT blow count (N) is the number of blows required to drive the sampler through the final 300 of 450 mm (12 of 18 inches). The blow count for the first 150 mm (6 inches) is recorded but not included in N. The SPT standards (ASTM D1586, 2018) specify the mass of the hammer as 63.5 kg (140 lb) and the drop height as 762 mm (30 inches), giving a standard potential energy of 475 Joules. The standards also specify acceptable tolerances, but the geometries of the hammer, anvil and drill rods are not specified. Different regions and companies have different methods for raising and dropping the weight, hammer and anvil geometries, and drill rods to connect the anvil to the soil sample tube. The SPT is generally inexpensive and simple, and unlike many tests used to estimate soil properties, it also yields a soil sample. The SPT can be performed below the water table or under drilling mud and with a range of borehole sizes. Corrections should be applied to the blow counts in those situations (ASTM D1586, 2018) before use in empirical correlations. The SPT is most useful in sands and less useful in clays and gravels. As the size of gravel particles increases, the test becomes less useful. There are larger split spoon samplers available that are more appropriate for use in gravelly soils (cf. Daniel, 2008). These larger diameter split spoon samplers can be used to collect a large soil sample from the bottom of a borehole, and the blow count can also be 2 recorded to estimate the soil’s engineering properties. When larger samplers are used, the test is called by various names but can generally be referred to as a Large Penetration Test (LPT). Different correlations have been developed for converting LPT blow counts into equivalent SPT blow counts. The resultant SPT blow count (N) is typically used in empirical correlations to estimate the engineering properties of the soil. The blow count is corrected to a standard 60% hammer efficiency for use in these correlations. This energy-corrected blow count is called the N60 (Eq. 1). Even with the standard hammer weight and drop height, differences in SPT test conditions result in variable energy losses/hammer efficiencies. The hammer efficiency is referred to as the energy transfer ratio (ETR) for an SPT hammer. ETR is defined by ASTM D4633 (2016) as “the ratio of the measured energy transferred to the drill rods [FVenergy] to the theoretical potential energy” (Eq. 2). The standard theoretical potential SPT energy (E*) is 475 J (ASTM, 2018). Eq. 1 𝑁60 =𝑁×𝐸𝑇𝑅%60% Eq. 2 𝐸𝑇𝑅 =𝐹𝑉𝐸𝑛𝑒𝑟𝑔𝑦E∗, where E*is the standard SPT energy (475 J) The ETR should be below 100% because the hammer velocity at impact is usually less than the theoretical free-fall velocity, and there is some loss of energy during and after impact to heat, sound, and other phenomena. Impact velocity here means the maximum hammer velocity at (or just before) the initial hammer-anvil impact. 1.2 Estimation of SPT Hammer Efficiency Although the test is simple, many researchers have identified irregularities in its performance that affect the results (cf. Schmertmann & Palacios, 1979; Seed et al., 1985). First, SPT hammers do not deliver a consistent standard drop height. Almost all SPT hammers have some operator dependence on the drop height. Some SPT hammers rely on the operator to lift or even throw the hammer to the correct height each time, and even if an internal mechanism is used to lift the hammer, these systems are rarely fully automated. However, the eSPT from MARL Technologies, used in this thesis, is a fully automated system. Second, potential energy from the hammer must be transferred to the anvil and then through the steel drill rods down to the sample tube. Energy is lost in various ways (e.g. heat, sound, damping), which affects the blow count without any relation 3 to the soil properties. Thus, the energy that reaches the sampler at the bottom of the borehole can vary significantly. The standard method for measuring hammer efficiency is the stress wave method. The stress wave method uses accelerometers and strain gauges to measure the stress wave energy passing through the drill rod below the anvil (ASTM, 2016). The maximum stress wave energy is converted to Joules and compared to the standard energy. Despite its high cost, complexity, and low reliability, this method is widely used. Furthermore, this method is not verifiable and contains an unknown bias (systematic error) (Daniel, 2008). In theory, the advantage of the stress wave method is its ability to measure post-impact energy loss. Some research (cf. Howie et al., 2003) has shown that post-impact factors such as the shape of the anvil, the type of drill rod, or the length of the drill rod affect the transfer of energy from the hammer to the drill rods. Early in the history of SPT research, it was recognized that pre-impact energy loss is significant with most hammer types. Hammers connected to a rope and pulley system are especially susceptible to pre-impact energy loss. In hindsight, measuring hammer impact velocity as an indicator of hammer efficiency should have been a natural course of action for standard SPT methods. However, various circumstances impeded impact velocity measurement from becoming widespread in commercial SPT programs. Importantly, Kovacs et al. (1983) concluded that the relationship between hammer impact velocity and stress wave energy was “important, variable, and unpredictable.” Since then, updated technology has shown the significant limitations of this study and others of its era. Then Morgano and Liang (1992) showed a significant relationship between SPT rod length and efficiency. This finding further impeded impact velocity measurement because post-impact energy losses due to factors such as rod length cannot be detected in the impact energy. More recent research has shown that the relationship between hammer impact velocity and stress wave energy is not so unpredictable (cf. Daniel, 2008). A new commercial automatic SPT hammer promises to make impact velocity measurement easy and reliable. The eSPT is an automatic SPT hammer manufactured and sold by MARL Technologies. The eSPT uses a pair of linear position sensors which measure the hammer’s position and velocity throughout the test. One linear position sensor is in the center of the hammer and is used for measuring the hammer’s displacement and velocity. The other is in the elevator 4 assembly that raises and lowers the hammer assembly. Together, the two linear position sensors measure the cumulative penetration distance of the test. Another key feature of the eSPT is that it hovers over the anvil, not resting on it (ASTM, 2018), and the hammer automatically follows the anvil downwards after each blow. 1.3 Research Objectives The objectives of this thesis are: 1) To improve the estimation of SPT hammer efficiency by comparing and correlating stress wave and impact velocity measurements. 2) To demonstrate that impact velocity measurement can provide a reliable estimate of ETR (for calculation of N60). 3) To assess the innovative features of the eSPT for precision and bias. The primary objective of this thesis is to improve the standardization of the SPT by comparing and correlating stress wave and impact velocity measurements. The precision and bias of each measurement will be assessed, following Daniel (2008). This comparison is essential because impact velocity measurement is relatively simple but has not been rigorously proven as a reliable indicator of ETR since it was initially discredited using methods that have since become outdated. On the other hand, stress wave measurement is costly and requires expertise to produce high-quality data and interpret it (Howie et al., 2003). The ASTM (2020) definition of precision and bias is used in this thesis. Precision is defined as “the closeness of agreement between independent test results obtained under stipulated conditions.” Bias is defined as “the difference between the expectation of the test results and an accepted reference value.” Imprecision is due to random errors, while bias is the total systematic error. The goal is to demonstrate that impact velocity measurement can provide a reliable estimate of the ETR and increase confidence in the resultant energy corrected blow count (N60) at a lower cost than direct stress wave measurement. Daniel (2008) showed that carefully measuring impact velocity instead of stress waves would result in roughly equivalent uncertainty in N60 for safety 5 hammers. However, the historical database available to Daniel (2008) had multiple flaws and did not include automatic SPT hammer data. A significant part of this thesis is dedicated to assessing the automated electronic features of the eSPT for precision and bias. The eSPT from MARL Technologies (Patent #2998280, issued in 2020) has several innovative functions that significantly benefit SPT testing and research programs. The eSPT is mentioned six times in the ASTM standard for SPT testing (2018). The most important of these functions for the present research is the automatic measurement of hammer impact velocity. This verification is essential, for once the eSPT functions are satisfactorily verified, the eSPT can be relied upon to produce high-quality data for other research goals. The impact velocity, drop height, and penetration distance measurements made by the eSPT are valuable for this thesis because the eSPT produces these data automatically and continuously, thereby enabling the collection of large, high-quality data sets. As part of the activities to address the research objectives, three cased test holes were installed at the MARL test site to depths of 6, 14.2, and 29.5 m. These depths result in drill rod lengths of 8.5, 16.2, and 32 m. Having three test holes allows the drill string length to be a control variable affecting the hammer efficiency. These three depths represent a typical range of depths used for SPT testing. There is a commonly held doubt about the value of this type of research based on the assumption that the SPT is only a rough tool for obtaining preliminary data. Improving SPT data collection quality control might seem insignificant when the empirical correlations used to estimate soil properties were not developed with the same precision. However, with an improved understanding of the precision and bias of the SPT, new empirical correlations can be developed using improved SPT equipment and methods. Furthermore, there are many different correlations between the SPT blow count and different soil properties. Each of these correlations has its own level of uncertainty, some much lower than others. 1.4 Thesis Organization Chapter 2 is a literature review of SPT energy measurement research and standards. Chapter 3 describes the testing equipment and methodology. Chapter 4 presents the test results. Chapter 5 6 presents the analysis and interpretation of the data. During the research for this thesis, some ideas for future SPT research were developed. A permanent test facility was constructed at MARL Technologies in Edmonton, which was shown to have distinct advantages as a location for future SPT research. The advantages of this test facility and other suggestions for future research are discussed in Chapter 6. The conclusions are summarized in Chapter 7. 7 Chapter 2: Literature Review This chapter reviews the necessary background knowledge for understanding the problem being addressed and the method of addressing it. It also refers to other sources for more detailed histories and explanations of penetration testing, split spoon testing, and the SPT. 2.1 Stress Wave Measurement Researchers have conducted laboratory-scale SPT tests since the 1970s to simplify the system and isolate causes and effects. The researchers of this era brought attention to various aspects of the SPT that had not yet been standardized. It became known that these differences could also lead to significant changes in the amount of energy transferred from the hammer weight down into the anvil and to the sampler. Therefore, correction factors began to be introduced to account for the significant differences. It was recognized in the 1970s that the most significant energy losses occurred before the hammer-anvil impact. The pre-impact energy losses are due to friction, which impedes the weight from reaching freefall velocity. The efficiency of different SPT hammers varies from as low as 40% for donut hammers to as high as 95% for automatic hammers. The method of hammer weight release has a significant effect on efficiency because a hammer weight that falls freely will have a much different impact velocity than one that stays connected to a rope wrapped around an overhead pulley. The age of the rope used to drop the weight can also affect the hammer efficiency because a stiff new rope has less friction with the pulley (ASTM D1586, 2018). The rope and pulley (cathead) systems used with donut and safety hammers are no longer widely used in North America but remain prevalent in many other countries. See Daniel (2008), who presented a historical database of safety hammer energy transfer efficiency measurements. It has been shown consistently that one-dimensional stress wave theory is adequate for modelling the SPT system, beginning with Adam (1971, as cited in Howie et al., 2003). A spreadsheet program that uses 1-D stress wave theory to predict the force and velocity in SPT drill rods was developed by Daniel et al. (2001) and further improved by (Howie et al., 2003). Daniel et al. (2001) showed that the number of reflected and transmitted waves in the SPT system increases exponentially after impact. When modelling a simplified hammer on AW rods, after only 2.5 ms, 8 there were 32,000 waves in the system (Howie et al., 2003). Most of the reflected waves are generated at the couplers (joints) between each section of the drill rod. This use of 1-D wave theory was adapted to the SPT after earlier being applied to the modelling of pile driving. Schmertmann and Palacios (1979) used 1-D wave theory to analyze SPT stress wave measurements. They established the “force-squared” method wherein the energy transferred into the anvil and the drill string is measured using force transducers because, at that time, accelerometers could not withstand the shock environment of SPT testing. They showed that the stress wave energy is inversely proportional to the SPT blow count (N) for counts at least as high as N=50. Subsequently, a lower limit was defined as 10 (cf. ASTM D4633, 2016). Using similar methods, Kovacs et al. (1983) concluded that the difference between the hammer’s impact energy and the stress wave energy was significant and unpredictable. The introduction of the N60 by Seed et al. (1985) was helpful for the comparison of data using different equipment and remains in use today. The 60 is derived from the average efficiency (ETR) of the safety hammer, which was ubiquitous. Most empirical correlations used to estimate soil properties are based on N60. The blow count is first multiplied by the hammer's efficiency and then divided by 60% (see Equation 1). Sy and Campanella (1991) successfully used an accelerometer and an oscilloscope to measure the drill rod’s velocity after impact. Morgano & Liang (1992) adapted accelerometers and strain gauges from PDI’s Pile Driving Analyzer for use on the SPT. Despite advances in accelerometer technology, they still needed to reduce the drop height to 24 inches (600 mm) when using drill rods less than 6 m long to prevent the accelerometers from overloading with high frequencies. Theoretically, since they measured impact velocity and stress wave (FV) energy, the reduced drop height did not affect their conclusions. Once again, the conclusions of Morgano and Liang (1992) led hammer impact velocity measurement to fall out of favour. They observed a significant difference between hammer impact energy and stress wave energy related to the length of the drill rod, thus promoting the benefits of measuring stress waves and lessening the importance of impact velocity. The ASTM standard describing the FV method is titled “Energy Measurement for Dynamic Penetrometers” (ASTM D4633, 2016). The FV method uses strain gauges and accelerometers to 9 calculate the drill rod’s force and velocity (F and V). The product of force and velocity is integrated for as long as the rod is in motion. The maximum value is divided by the standard energy (475 J) and referred to as ETR, the energy transfer ratio. In subsequent chapters, the FV method will be referred to simply as the stress wave method. The stress wave method is also found in similar standards worldwide (e.g., ISO 22476-3, 2005). It is commonly used for periodically checking the efficiency of SPT hammers or for critical applications such as liquefaction assessment, where energy measurement is desired for each blow (ASTM D6066, 2011). Since 2005, the ISO standard 22476-3 has required that a calibration certificate be available for every SPT hammer that shows ETR measurements have been done in the past six months or since the hammer was last repaired. The energy measurement systems calculate the force and velocity in the drill rod from the moment of impact until the energy is dissipated. These two signals are multiplied together and integrated over the whole period to calculate the total stress wave energy transferred to the drill string at the point of measurement. However, the standard practice is to use the maximum energy value found during integration rather than the final value. This maximum energy will be called the FVenergy (force-velocity). Eq. 3 𝐹𝑉 𝑒𝑛𝑒𝑟𝑔𝑦 = max {∫ (𝑓 × 𝑣) 𝑑𝑡∞0} Howie et al. (2003) published a thorough report on energy methods in the SPT, which provided a guide to assessing the importance of some lesser-known aspects of the SPT that affect the hammer efficiency. These aspects include loose drill rod joints, the type of drill rods used, the anvil geometry, and the hammer geometry (which sometimes varies even among hammers of the same class, such as “automatic hammers”). Howie et al. (2003) also present a comprehensive guide for assessing the quality of stress wave measurement data. The important points are described here, and the process is demonstrated for the data collected for this thesis in Chapter 3. First, the force and velocity signals are checked to see if they are reasonably proportional. The two signals should theoretically be equal (once converted to the same units) until tensile reflections return to the transducers from below. However, reflections from the coupler at the bottom of the transducer subassembly return very 10 quickly, even before the first force/velocity peak. Another essential check is that the force and velocity traces do not drift away from zero at the end of the record, which would indicate error accumulation during signal processing. If the velocity does not return to zero at the end of the record, then the blow should be excluded. For systems with two accelerometers, like the PDA, the velocity traces should be compared for similarity. If the curves are very different, one or both accelerometers are malfunctioning. One accelerometer may be taken away if there is good reason to believe it was malfunctioning, but at least one accelerometer is necessary. Using only the force signal to calculate ETR is called the force-squared method, which has been proven to be incorrect. The PDA accelerometers are not stiffly mounted on the drill rod, which causes damping of most accelerations above 15,000 Hz frequency. The loose mount also causes the velocity signal to be slightly delayed relative to the force signal, and then the velocity peak will overshoot the force peak. Experience will inform the interpreter whether this effect is within the acceptable range. If an accelerometer is too loosely connected, the velocity signal will be dominated by a single wavelength, and most of the frequencies contained in the force signal are lost. In this case, the data from the loose accelerometer should be removed, or the bolted connection tightened. On the other hand, the Northwoods accelerometer is bonded to the drill rod, a stiff connection that does not loosen. The force-velocity proportionality is generally much better, and significant frequencies up to 50,000 Hz are detected. Obtaining a reliable velocity-time history of the drill rod is the most challenging part of SPT stress wave measurement. The accelerometers are far more expensive and sensitive than the strain gauges and are easily damaged by rough handling. The challenging shock environment and the drawbacks of soft mounting the accelerometers are extensively reviewed in Howie et al. (2003), and they show that a rigid mount accelerometer measured 7% higher FVenergy than a soft mount. The two accelerometers were mounted side by side on a laboratory SPT system with a safety hammer and reliably reproduced this result. A more sensitive accelerometer can reduce the error incurred using a soft mount, but this must be paired with a higher sampling frequency based on the Nyquist theorem. The theorem states that the sampling frequency should be at least twice as high as the highest frequencies found in the signal to prevent aliasing, and the more rigid the mount is, the higher the frequency content will be. As well, digital or analog filters are always applied to SPT 11 data for the same purpose of anti-aliasing. The filters are automatically applied by the PDA and Northwoods systems without user input. An inherent source of problems with the FV method is that it requires double integration of the acceleration signal. First, the acceleration trace is integrated to obtain velocity, and then the product of force and velocity is integrated again to obtain FVenergy. Minor errors are propagated and magnified through the integrations, and these can be seen clearly if the velocity trends away from zero at the end of the blow record while the force signal remains at zero (Howie et al., 2003). Stress wave measurement is also invasive. An instrumented subassembly is inserted into the drill rods, affecting stress transfer differently depending on its design (Howie et al., 2003). Stress wave measurement can contain a significant but unknown bias error because there is no way to calibrate the measurement devices (Daniel, 2008). This bias error is impossible to distinguish from the variability of natural soils. In ASTM D4633 (2016), the section “Precision & Bias” states that “Test data on precision are not presented… it is either not feasible or too costly.” In addition to the unknown bias error, the measured stress wave energies contain random scatter. Daniel (2008) showed that ETR measurements could be assumed to be normally distributed in his historical database. Therefore, the significance of the random scatter will decrease as more data are collected; however, it might be critical for small SPT projects. In the data set collected for this paper, the stress wave measurements had a coefficient of variation (CV) of 3 to 5% under highly controlled conditions with artificial soil. The historical database presented in Daniel (2008) has much larger CVs, between 8 and 15%. This random error is often a cause for confusion because, for some blows, the measured impact energy will be lower than the stress wave energy, resulting in more than 100% energy transfer (violating the conservation of energy). Efficiency above 100% is seen often in the data collected for this thesis, and the two different stress wave measurement devices sometimes give significantly different results. 2.2 Relationship Between Impact and Stress Wave Energies The hammer’s kinetic energy at impact (KE) is divided by the maximum FVenergy to quantify the relationship between the impact and stress wave energy methods. This calculation is central to the “energy transfer approach” described in Daniel (2008). In this thesis, the quotient is called e2 12 because it captures the post-impact energy loss (cf. Decourt, 1989), whereas e1 is the pre-impact loss. Eq. 4 𝑒2 =𝐹𝑉 𝑒𝑛𝑒𝑟𝑔𝑦hammer′s impact KE Here is a sample of the e2 data in the literature. Some studies found e2 to be practically equal to 100%, whereas others found significant post-impact energy losses. Daniel et al. (2005) found e2 of 97.7% with a safety hammer, stating that the post-impact energy loss was “within the level of accuracy of the measurements,” whereas Lee et al. (2014) found that a donut hammer only had an e2 of 70%. Abou-matar & Goble (1997) measured impact velocity and FVenergy for an automatic hammer, but e2 was not the focus of their study, and the data were not presented. Abou-matar & Goble (1997) used the stress wave simulation software GRLWEAP to calculate e2 for their CME automatic hammer and found that it should be close to 100%. Santana et al. (2014) measured e2 with Brazilian SPT tests between 92% and 100% (average of 96%) and between 86% and 94% (average of 90%) for another borehole. This difference between the two boreholes is likely due to the operator dependence of the Brazilian SPT test, which is much greater than for automatic hammers. Cavalcante et al. (2011) found e2 of 99.9% for 98 blows with the Brazilian SPT. Daniel (2008) presented a historical database of 1,017 safety hammer blows with an average e2 of 96.1%. He emphasized that the e2 data were roughly normally distributed. This observation means that e2 was practically constant with large enough datasets and therefore predictable, contra Kovacs et al. (1983). Measurement of stress wave energy has a distinct potential advantage over hammer impact velocity because it can capture post-impact energy loss. Several studies have measured FVenergy at the bottom of the drill rods (cf. Odebrecht et al., 2005; Lukiantchuki et al., 2017) and found less energy than at the top of the rods. The sampler tube is where the soil penetration occurs, and detection of energy losses along the path from the hammer to the sampler will provide further quality control. Measuring the FVenergy downhole is rare because of instrumentation and data interpretation problems. There is an effort within the research community to find analytical relationships between the energy that reaches the soil sampler, the resultant penetration distance, and the soil properties (e.g., 13 Odebrecht et al., 2005; Schnaid et al., 2009). This strategy would lessen the engineer’s reliance on empirical relationships and standardization. However, both strategies require knowledge of the hammer’s efficiency. 2.3 SPT Hammer Impact Velocity Measurement Impact velocity measurement for SPT research has been done in various studies (cf. Kovacs, 1983, Morgano & Liang, 1992; Daniel, 2008; Lee et al., 2010, 2014). However, each of these studies had limitations that left further questions. Furthermore, the past studies of SPT impact velocity used measurement tools that were either expensive or unreliable or are no longer available. Since then, at least one new SPT hammer has emerged that measures impact velocity cheaply and reliably. This thesis uses one such device, the eSPT from MARL Technologies. Daniel et al. (2005) did not directly measure the impact velocity but could assume free-fall velocity because they used the “string-cut” method with a safety hammer. This method may be reliable but is cumbersome for collecting large data sets and is not commercially viable. A proprietary device from PDI Inc. (the Hammer Performance Analyzer, HPA) measures hammer impact velocity with a doppler radar system. It was designed for use with pile driving and SPT hammers. Morgano & Liang (1992) used this device and an SPT Analyzer to calculate e2 for field SPTs. Abou-matar & Goble (1997) also used these two devices in a laboratory SPT energy study. Finally, Daniel (2008) presented data from three safety hammer SPT programs for 416 blows where the HPA was used along with a custom-built FV energy measurement system. A company called IDS GeoRadar makes a radar device that has been used to measure pile penetration and hammer impact velocity for pile driving (Tokhi, 2012). This device has a 200 Hz frequency and can measure movement up to 1 km away. It is primarily used for monitoring slope stability in mines. This type of device might work well for safety or donut SPT hammers with visible hammer weights but is not as suitable for measuring the impact velocity of automatic SPT hammers, which hide the hammer entirely from sight. PileTrac (Piletrac.com) makes and sells the PileTrac Pile Set Monitor, which records SPT blow count and penetration distance using a laser (mentioned alongside the eSPT in ASTM, 2018). It is 14 described as having many different and interesting features, but it seems that hammer impact velocity measurement is not currently among them. Cameras were used to measure hammer impact velocity as early as the 1980s, but the available frame rate has since increased dramatically. Some researchers in the 2000s still only used a 30 Hz frame rate (Cavalcante et al., 2011), while others used as much as 20 kHz (Lee, 2014). Santana et al. (2014) used a 210 Hz frame rate and 480 x 360 pixels. Lee et al. (2010, 2014) used a line-scan camera to measure the hammer and the anvil’s pre and post-impact velocity. A frame rate of 20,000 kHz may be necessary to measure the anvil’s post-impact velocity, but it should not be necessary to measure the hammer’s velocity. Cavalcante et al. (2011) found significant blurring with the 30 Hz frame rate and only obtained three or four frames from release to impact. To overcome this limitation, they fit a quadratic curve to the displacement-time history, which was then differentiated to produce velocity and acceleration curves. A cubic or quartic formula could fit the displacement-time history to provide a more complex description of the velocity and acceleration, but the acceleration was shown to be practically constant, which justifies using the quadratic displacement-time curve. In contrast, Lee et al. (2010, 2014) used a more sophisticated algorithm to calculate displacement with the software paired with their line-scan high-speed camera. Cavalcante et al. (2011) also showed that with the Brazilian SPT, the hammer’s velocity-time relationship becomes closer to linear as the drop progresses. This trend is expected for automatic hammers as well. Contact between the hammer and its guide tube occurs during the drop with automatic SPT hammers because the hammer throws the weight upwards, causing some rotation and eccentricity in the drop tube. This contact means the weight generally does not reach the theoretical freefall velocity, and the acceleration will not be perfectly constant. However, this rotational energy is dissipated during the drop, so using a subset of the data closer to the point of impact will generally provide a more accurate displacement-time curve for calculating the impact velocity. A regression curve with a higher R-squared value or a lower mean error shows an improvement in predictive capacity when using the data subset. Lee et al. (2014) used their 20 kHz frame rate cameras to measure both the hammer and anvil’s velocity. The anvil requires a higher frame rate because it does not move as much, and its motion 15 has higher frequency components than the hammer. For this thesis, only the hammer impact velocity is desired. A Samsung S9 smartphone that records video with a 960 Hz frame rate was used to measure the hammer impact velocity. This camera provides an appropriate balance between cost and quality for preliminary verification of the eSPT’s impact velocity measurement. The eSPT used in this thesis measures hammer impact velocity with Temposonics® linear-position sensors manufactured by MTS Sensors. The velocity measurement sensor is in a hole that goes down the hammer's center from end to end. MTS describes these as being magnetostrictive, absolute, non-contact, linear-position. 2.4 Test Holes or Chambers The soil encountered during an SPT is highly variable in the field, so sand has often been used for laboratory SPT research, typically compacted to a known density for each test. For example, this “calibration chamber” procedure is common for developing empirical correlations between SPT blow count N and different engineering properties of soil. Calibration chambers are typically about ten times the diameter of the SPT sampler (cf. Schnaid & Houlsby, 1992). The size ratio and the stiffness of the chamber walls significantly affect the blow count. In contrast, the test holes used in this thesis are about three times the diameter of the SPT sampler. The most common method of collecting SPT data is to collect available datasets from industrial SPT testing programs or to accompany these programs and use research devices while they are carried out. Otherwise, in the laboratory, the drill rod length is limited by the height of the roof. Daniel et al. (2005) tested two different rod lengths, but the laboratory setting limited the rod length to 6.5 m. Odebrecht et al. (2005) used an under-construction building to make a calibration chamber SPT with a 35.8 m rod length. Howie et al. (2003) did a laboratory test without any soil, simply resting the bottom of the drill rods on wood. Testing in closed-bottom cased boreholes (such as in this thesis) is not reported in the literature. 2.5 Effect of SPT Drill Rods on Energy Transfer Efficiency There is a theory (arising from observation of stress wave data) that e2 is affected by the length of the drill rods, which has made the study of e2 more complex. However, this theory is controversial. 16 Daniel et al. (2005) reviewed the so-called “short rod corrections” and tentatively concluded that they are no longer appropriate for calculating the SPT N60. Other researchers have reached the same conclusion, and ASTM (2018) no longer recommends these corrections. The test site used in this thesis might be well suited to research rod length effects, but that is not the present focus. Howie et al. (2003) recommended that only box-threaded drill rod couplers be used for SPT testing. J-type tapered drill rod threads were shown to loosen much faster than non-tapered (box) threads. North American SPT operators often prefer AWJ and NWJ rods because they are easier to unthread (compared to AW and NW), but they also loosen much more during the SPT, which causes more energy loss and may significantly affect the blow count (Howie et al., 2003). Howie et al. (2003) also showed that NW rods should be more inefficient than AW because the NW couplers are much more massive than AW couplers relative to the drill rods. The massive couplers have a larger cross-sectional area which causes more stress wave reflections that interfere with stress wave measurement. Abou-Matar & Goble (1997) showed that using Mayhew rods caused a significant decrease in hammer efficiency. This rod type has twice the cross-sectional area of AW rods. They recommended that a correction be included in the relevant ASTM standards for rod cross-sectional area. ASTM 1586 (2018) only mentions A and N-type rods, saying that there is no definitive research on the relative effect of these rod types but that N-type rods may decrease hammer efficiency due to the larger couplers. Byun & Lee (2013) and Howie et al. (2003) showed that loosely connected drill rods significantly affect ETR. Byun & Lee (2013) purposely loosened rod joints and showed the effect on the FVenergy using Dynamic Cone Penetrometer rods. Howie et al. (2003) purposely loosened two joints on a 4.5 m AW drill rod string during an SPT. The result was 2.3% lower FVenergy. This 2.3% effect may not be significant, but more than two joints will likely become loose during a field SPT test. This likelihood rises with the age of the drill rods, higher blow counts, tapered threads, longer drill rod strings, and lack of operator care. Drill rod couplers wear out with use, and the coupler threads become rounded when the test is performed without the rods being snug against the coupler. Based on the testing for this thesis, even if all joints are tightened with a pair of pipe wrenches, some joints will inevitably become loose by the end of the SPT test. For some other studies, such as Daniel (2005), joint tightness was 17 easier to ensure because they were not performing full SPTs over 450 mm, but only individual blows. It is assumed that the joints would have been checked for tightness after each blow because they only performed one blow for each test. Researchers have recently shown much more significant energy loss in the drill string than was previously assumed (Lukiantchuki et al., 2017; Santana et al., 2013; Odebrecht et al., 2005). Note that these studies involved the Brazilian SPT test, which uses different rods than either A or N. These rods have either 8 or 11 threads per inch (Portuguese: ABNT, 2001) compared to 2.54 threads per inch of AW rod. The cross-sectional areas are similar, although Lukiantchuki et al. (2017) observed 12 m (40 feet) rods buckling in soft soil, indicating they are lighter than either AW or NW. Tatone (2018) measured a much more significant rod length effect than others had found. ASTM D1586 (2018) proposed an optional correction factor for energy loss with drill strings over 30 m long. Energy loss in the 32 m drill rod for this thesis is not expected to affect the results significantly. Odebrecht et al. (2005) presented an equation to calculate the cumulative inefficiency of long drill strings due to joint effects. They also concluded that the drill string weight contributes to increased penetration depth as the blow count decreases. Daniel (2008) proposed a “modified energy transfer ratio” incorporating the observations from Odebrecht et al. (2005). The modified energy transfer ratio will be applied to the data collected in this thesis as a secondary interest. 18 Chapter 3: Testing Equipment and Methodology In this chapter, the testing equipment and methodology are described. After the overview and definition of terms, the testing facility and SPT equipment are described. Next is the eSPT verification method, followed by the ETR and e1 data collection equipment and method. 3.1 Overview and Definition of Terms The following list describes the high-level methodology for the test program. 1) Assess the precision and bias of the eSPT’s impact velocity measurements. 2) Perform SPTs while measuring impact velocity (with the eSPT) and stress wave energy. a. Repeat with the alternate stress wave energy device. b. Repeat with different rod lengths. 3) Compare and correlate the hammer impact velocity and stress wave energy. 4) Assess the effects of the measurement device and rod length. In this thesis, the hammer efficiency is divided into pre and post-impact efficiencies (cf. Décourt, 1989; Santana et al., 2014). The pre-impact efficiency, e1, is the quotient of the hammer’s maximum kinetic energy at impact over the standard potential energy. In the past, e1 has often been called ERv (cf. Kovacs et al., 1983; Daniel, 2008). In this thesis, the post-impact efficiency, e2, is the quotient of the hammer’s maximum stress wave energy measured below the anvil (known as FVenergy) over its kinetic energy at impact. In these past studies, e2 was called ETR. It is important to note that ETR is used differently in this thesis. Following ASTM (2016) and the Pile Dynamics literature (PDA), ETR refers to the quotient of the FVenergy over the standard potential energy. The following equations use m for mass, v for velocity, g for the earth’s gravitational acceleration (9.81 m/s2), h for the drop height, and E* is the standard SPT energy (475 J). Eq. 5 𝑒1 =𝐾𝐸𝐸∗ Eq. 6 𝐸𝑇𝑅 = 𝑒1 ∗ 𝑒2 =𝐾𝐸𝐸∗∗𝐹𝑉𝐾𝐸=𝐹𝑉𝐸∗, where 𝐸 ∗= 475 J 19 Eq. 7 𝐾𝐸 = 1 2⁄ 𝑚𝑣𝑖𝑚𝑝𝑎𝑐𝑡2 Eq. 8 𝑣𝑖𝑚𝑝𝑎𝑐𝑡 = max (𝑣 =𝜕𝑥𝜕𝑡) Eq. 3 𝐹𝑉𝑒𝑛𝑒𝑟𝑔𝑦 = max {∫ (𝑓 × 𝑣) 𝑑𝑡∞0} Eq. 9 𝑒2 =𝐹𝑉𝑒𝑛𝑒𝑟𝑔𝑦12⁄ 𝑚𝑣𝑖𝑚𝑝𝑎𝑐𝑡2 Three techniques are used separately to record the hammer’s displacement: eSPT, a high-speed camera, and a string potentiometer. The impact velocity is found by differentiation of the displacement-time history. Equation 8 is used to find the impact velocity from the eSPT’s displacement sensors, but a linear regression is used to find the impact velocity using the camera and string potentiometer. FVenergy is measured by an instrumented subassembly inserted into the drill string, which measures the stress wave energy with strain gauges and accelerometers. The equipment and procedure for stress wave energy measurement follow the current ASTM standard method for energy measurement in dynamic penetration tests (ASTM D4633, 2016). The length of the drill string from the anvil to the soil sampler is a controlled variable. Testing is done in three different closed cased boreholes, each having a different depth but otherwise identical conditions. Several techniques are used to keep conditions in the test holes as constant as possible. First, the media the sampler penetrates is held in a single basket on a cable so that it can be used in all three boreholes and visually inspected. Other techniques are described below in their respective categories. First, the testing facility will be described, followed by the impact velocity verification and energy measurement methods. 3.2 Testing Facility 3.2.1 Drill Rig MARL’s eSPT is unique amongst North American automatic hammers in that it records drop height, hammer impact velocity, and penetration distance for every blow using internal sensors. It is also one of only two or three SPT automatic hammers that floats over the anvil while lifting and dropping the weight, rather than resting the weight of the hammer assembly on the anvil. This 20 feature of the eSPT was noted in ASTM D1586-18, “SPT and Split-Barrel Sampling of Soils.” For performing the SPT in lower density soils, this floating feature gives more consistent results because otherwise, the hammer assembly might be causing an unknown amount of penetration from self-weight. The eSPT used for this research was mounted on a 10-ton drill truck backed into position over each casing. The truck had a 10 m tall winch tower and two winches. Four outriggers kept the rig stable and level during testing. 3.2.2 Drill Rods and Equipment A 50.8 mm (2.0”) outer diameter, 0.762 m (30”) length SPT sampler was used for all tests. This sampler has the standard dimensions of a split spoon SPT sampler but does not split open. Since no samples were collected, a single-piece, more durable sampler was used. A standard 34.93 mm (1.37”) inner diameter drive shoe was used for all tests. No sample liner or core catcher was used. The sampler and shoe are pictured below, with an AW coupler set in the sampler’s top. Figure 1. SPT driving shoe, sampler, AW coupler Drill rods are used to connect the anvil to the SPT soil sampler. All testing was done with AW-type drill rods and box-threaded AW couplers manufactured by MARL Technologies. The drill rods are 1.52 m (5’) long, except for one extra 0.76 m (2.5’) rod used at the ground surface with the PDA because the PDA’s transducer assembly is 0.61 m shorter than the Northwoods’s. All the SPT driving shoe Solid SPT sampler AW coupler at top 21 drill rods are 30.1 mm (1.219”) ID and 44.5 mm (1.75”) OD. The AW couplers have a standard bore of 15.9 mm (5/8”). Both instrumented subassemblies are also mounted on AW-type drill rods with box threads. Having matching rod types such as this is ideal for minimizing stress wave reflections. Only AW rods with new to moderately worn AW box threaded rod couplers were used. Each joint was tightened with a pair of pipe wrenches before starting each SPT test. Partway through the testing program, all the coupler threads were covered in electrical tape to keep the joints from loosening during the 18-inch SPTs. The rods were pulled out of the casing after each 18-inch test. Then all the joints were re-tightened with a pair of pipe wrenches, and loose joints were noted. The anvil was also manufactured by MARL Technologies. The dimensions and materials (solid steel) were the same as the CME Automatic Anvil described in Howie et al. (2003). The flange’s outer diameter is 82.55 mm (3.25”), and the primary diameter is 63.5 mm (2.5”). However, the CME anvil has a tapered AWJ thread, whereas the MARL anvil is box threaded. Figure 2. eSPT anvil with AW coupler The hammer is machined (by MARL Technologies) from a 139.7 mm (5.5”) diameter round steel bar and has a total height of 0.673 m (26.5”). A hole down the centre houses one of the displacement sensors for the eSPT. Accounting for the hole, the area of the hammer is approximately 0.15 m2 (23.5 sq. inches). The hammer weight is 63.5 kg (140 lb), and the drop Hammer-avil contact surface MARL SPT anvil AW coupler at bottom 22 height is 0.762 m (30 in), resulting in the standard energy of 475 J. The entire hammer assembly diagram is shown in Figure 3. The methodology for verifying the hammer’s displacement measurements is described in a separate section below. Figure 3. eSPT Automatic hammer, copyright MARL Technologies During regular operation of the eSPT, the maximum velocity for each blow is recorded, as automatically calculated by the hammer. However, this feature was unavailable during testing for this thesis because the hammer was not cloud-connected to MARL’s servers. The eSPT produced a velocity-time record of the hammer. The maximum velocity for each blow was found using the velocity-time recording and an algorithm developed in Excel. The algorithm searches for local extrema in the velocity record and checks to ensure they are true “blows” rather than noise. For example, any local maxima less than 2 m/s were rejected. Each 45 cm (18-inch) SPT test was Hammer guide tube Hydraulic piston mounted to drill rig 23 checked individually to ensure the number of blows was equal between the eSPT, the stress wave device, and the extrema finding algorithm. A similar algorithm was applied to find the drop height of each blow from the eSPT displacement record. 3.2.3 Cased Test Holes and Test Medium The boreholes are installed at the MARL test facility in Edmonton, Alberta. The stratigraphy was described by MARL as follows. Clay fill to 5 m underlain by a wet coal seam to 6 m, underlain by shale and sandstone. The water table is typically 5 to 6 m below the ground surface, close to the bedrock surface. Historical SPT data were found for the site from the surface to 4 m. The N values were less than 10 in the top 2 m and increased to as high as 48 at 4 m depth. The test holes are closed-bottom cased boreholes made from 6-inch Schedule 40 steel pipe (0.280” wall thickness). Each casing has a 0.5 m diameter steel skirt welded to its top edge. This skirt’s primary function is to stop the casing from being driven into the ground during SPT testing. The skirt also makes manual penetration distance measurement easier and helps to keep the test hole free of debris. The three cased test holes are installed to depths of 6, 14.2, and 29.5 m. These depths result in drill rod lengths of 8.5, 16.2, and 32 m. Having three test holes allows the drill string length to be a control variable affecting the hammer efficiency. The three casings were installed 4 m apart from each other. The three cased test holes are shown in Figure 4. Figure 4. Three cased test holes at the test site in Edmonton 24 A single basket containing steel shot mixed with rust inhibitor was used as an energy dissipator for the SPT sampler to penetrate during testing. The steel shot is shown in Figure 5 and the basket in Figure 6. The weight of the basket and media is 55 kg. The basket is made from 5-inch Schedule 40 steel pipe (0.257” wall thickness). The media used for testing was steel shot manufactured for sandblasting applications. The relative bulk density of this material is around 3.8, while that of dry sand is around 1.6. This high bulk density was helpful because it contributed to consistently high blow counts, and low penetration distances and the media naturally settled to a consistent density without needing compaction after each test. The angle of repose of “most heavy granular abrasives,” such as this material, is approximately 32 degrees (Hansel, 1999). This angle of repose is not as great as some natural soils encountered in SPT testing. However, this material is exceptionally incompressible because it is uniformly graded. All steel shot particles are between 1 and 3 mm in diameter. Figure 5. Clean steel shot before use The steel shot can be easily viewed for inspection or washing. It is easy to observe whether corrosion or wear has occurred, either in the steel basket or when the media is poured out. The 25 depth of the media in the basket could also be measured to check for changes and degradation. No steel shot degradation was observed during the present study. In addition to the use of rust inhibitor, all the data presented in this thesis were collected within a single week, ensuring that the steel shot would not rust significantly during the data collection period. Figure 6. Media basket with couplers for the lift cable The basket is lifted from the casing for storage or inspection and can be placed in any casing to test with different rod lengths. Transfer of the basket is accomplished with a pair of winches on a 10 m tall mast, which are used to raise/lower the media basket and the drill string. The height is helpful for productivity because 10 m long strings of drill rods can be laid aside without breaking them apart. After about 0.45 m (18-inch) penetration is completed in the test chamber, the split spoon soil sampler tends to be stuck in the media. The soil sampler is often sufficiently stuck in the media basket that they will be pulled up together. Various techniques can be used to release the soil sampler from the media. Sometimes, simply twisting the top of the drill rods is enough to release the sampler. Otherwise, hitting a sledgehammer against the drill string will release the basket. If the basket and rods are lifted to be struck, two winches are required to prevent the basket from falling to the bottom of the hole. If each test is limited to ten or fewer blows, the drill string can be pulled out of the media with a single winch. In the deepest borehole (30 m), the drill string weighs approximately 215 kg. 26 Two cased boreholes are waterproof when the cap is installed at the top, whereas the deepest one fills with groundwater from a leak in the casing to about 15 m below the ground surface. 3.3 eSPT Verification Method The automatic hammer used in the present study is owned and manufactured by MARL Ltd. and is trademarked as the eSPT (Patent #2998280). The eSPT is used because it records the drop height and hammer impact velocity for every blow. Drop height means the maximum distance between the hammer and anvil before the blow. Impact velocity means the maximum velocity of the hammer at (or just before) the hammer-anvil impact. The drop height and velocity data from the eSPT are used to determine the hammer’s potential energy before it is dropped and its kinetic energy at impact. The eSPT is embedded with sensors that monitor the position of the hammer throughout the test. The eSPT software uses these sensors to identify when the weight is at rest, being lifted, dropping, among other “states.” These data are used to present blow-by-blow information and summary data for the test, including drop height, peak velocity, and penetration distance. Assessment of the precision and bias of these measurements is part of the quality assurance strategy for the ETR and e1 data collected for this thesis. Precision is “the closeness of agreement between independent test results obtained under stipulated conditions” (ASTM, 2020). If the true impact velocity is constant, how much variance is there in the measurements? Precision depends only on random errors. The best indicators of imprecision are the standard deviation and coefficient of variation. Bias is “the difference between the expectation of the test results and an accepted reference value” (ASTM, 2020). Bias is due to total systematic error, not random errors. As defined by ASTM (2020), accuracy would capture both random and systematic errors. Precision and bias are therefore synonymous with accuracy. Establishing the bias of the eSPT’s measurements would only be possible if an Accepted Reference Value were established for each measurement in question. It will be seen in the Results chapter that this was not possible for impact velocity. Nonetheless, the method described here was informative and effectively provided quality 27 assurance for the ETR and e1 data collection program. Careful hand measurements can establish the Accepted Reference Value for penetration distance. 3.3.1 String Potentiometer A string potentiometer (see Appendix A: Posital Fraba, 2021) was used during an initial attempt to verify the eSPT’s displacement and velocity. The string potentiometer (string pot) was mounted to the top of the hammer guide tube, and the string was attached to a metal arm attached to the top of the hammer. This metal arm protruded through a slot in the hammer guide tube to be visible during testing. The string potentiometer continuously measured the hammer displacement during a series of blows. The metal arm used to attach the string to the hammer was also used for high-speed camera measurement of the hammer displacement and velocity. The setup is shown in Figure 7, where the guide tube is transparent, showing the hammer and metal arm within. The yellow box shows the location of the metal arm. The black arrows point at brackets to help map the photo onto the drawing. The hammer (and the metal arm) are near the top of the guide tube in the drawing and near the bottom in the photo. 28 Figure 7. String potentiometer mounted on eSPT hammer: transparent guide tube, copyright MARL Technologies The pull-back force of the string potentiometer is 9.8 Newtons (kg×m/s2). This upwards force, applied to the 63.5 kg hammer, results in a net freefall acceleration of 9.66 m/s2 (see calculation below). The actual average acceleration during the hammer drop will be less due to other frictional forces, primarily from the contact between the hammer and the guide tube. Calculation: net freefall acceleration = 9.81 𝑚/𝑠2 −9.8 𝑘𝑔∗𝑚/𝑠263.5 𝑘𝑔= 9.81 𝑚/𝑠2 − 0.154 𝑚/𝑠2 The data sampling rates for both the eSPT and the string potentiometer were equal during this experiment. This rate is approximately 7.5 ms/sample, neither being perfectly constant. Brackets to bridge custom cut slot Brackets to bridge custom cut slot Metal arm with string attached String pot. → 29 The displacement-time data from these two sensors were not perfectly synchronized. Therefore, the string potentiometer time (t0 = zero) was shifted until its curve graphically matched the eSPT data. Due to the excellent agreement between the two displacement records, the appropriate time shift was obvious and easily determined in all cases. The velocity was calculated by taking the slope of displacement-time using Eq. 10. Eq. 10 velocity = −(positiont−positiont−1)(timet−tt−1) A smartphone was also used to record high-speed videos of the hammer throughout a blow cycle with a ruler set behind the moving metal tab. However, these videos were not analyzed because the string potentiometer data were satisfactory for verifying the eSPT’s displacement measurements. 3.3.2 High-Speed Camera The displacement data collected by the eSPT were also used to calculate and record hammer velocity and find the maximum velocity during each blow. To independently assess this function of the eSPT, a smartphone was used in slow-motion video mode. The automatic hammer used in the present study was modified to make the hammer visible to the camera. A slot was cut down the hammer guide tube to allow an aluminum tab to protrude. This same tab was used for string potentiometer measurement, but the string was removed from the tab during filming (see Figure 7). A Samsung S9+ phone was used to video record the hammer impact with a resolution of 1280 x 720 pixels and a framerate of either 960 or 480 frames per second (fps=Hz), equivalent to a sampling period of either 2 or 1 milliseconds. The Samsung S9 stock video software automatically begins to record for 0.2 seconds when it detects movement in the frame. One hammer drop video was captured with 960 Hz, and four videos with 480 Hz. Compared to the eSPT and string potentiometer sampling period of about 133 Hz, the camera provides a much higher sampling rate, which helps capture the maximum velocity more precisely. The videos recorded at 480 Hz (fps) were supposed to be 960 Hz. However, the frames were all duplicated by either software or user error. Duplicate frames were skipped over during video analysis, producing half the nominal frame rate. 30 The position of the metal tab attached to the top of the hammer was recorded in the videos. Figure 8 shows this aluminum tab in a single frame from one of the high-speed videos. These videos were taken with the camera located approximately 0.3 m from the tab’s plane of movement, resulting in the tracking point being visible over a travel distance of about 0.15 m before impact. Figure 8. Frame from a close-up video just before impact An open-source (free) software called Tracker (Open Source Physics, version 5.1.5) was used to determine the location of this steel tab in each frame of the video. Each video’s scale and axis are defined in the Tracker software, and the object was identified in each frame. The height of the tracked object itself was used as the scale bar. Tracker automatically calculates distance, velocity, and acceleration in engineering units based on the pixel coordinates of the object and the timestamp of each frame. However, the displacement record was exported to a spreadsheet for velocity and 31 acceleration calculations for this research. Tracker can also adjust the video for colour, brightness, perspective, and more. It is simple to skip duplicate frames using this software. However, the software will not calculate velocity if the object is not identified in successive frames. Therefore, the displacement must be exported to Excel in this case. The camera methodology has much in common with the string potentiometer methodology in the previous section. Like the string potentiometer data, the eSPT and camera data records were not perfectly synchronized, so the camera velocity-time data were shifted to display these curves on the same graph. This time shift was named “curve matching” because t0 was shifted until the two curves appeared to match. Equation 10 was used to calculate velocity from displacement for both the string potentiometer and camera data. The maximum camera velocity was determined in the same way as for the string potentiometer tests above, using linear regressions. It was necessary to define the last velocity-time data point before impact for each blow, which is the dependent variable and the last data point included in the linear regressions. Unlike the string potentiometer tests above, velocity was not calculated manually from the displacement data. Instead, the eSPT automatically calculated the velocity. This change in methodology was made because the displacement data were not logged from the hammer during the camera tests. When the camera tests were begun, the desired outputs were manually selected for logging, and displacement was not selected. Estimating when the hammer was moving upwards in a rebound was necessary to produce the velocity graphs (Figure 30 through Figure 34). Estimating when the hammer was moving upwards within the eSPT’s velocity-time data is another form of curve matching. The Tracker software reports negative velocities during a rebound of the hammer, while the eSPT only reported the absolute velocity. This difference between the two velocity-time curves is evident when examined on the same graph. Based on curve matching, all the negative eSPT velocities in Figure 30 through Figure 34 have been manually multiplied by -1, whereas this was not required for the camera data. Figure 30 is shown here as Figure 9 to illustrate the result of the curve matching process. See also Figure 37, which shows the eSPT velocity data before and after the -1 multiplication. 32 Figure 9. Curve matching example (Figure 28: Drop 1: eSPT and camera impact velocity) As seen in Figure 8, a ruler was fixed to the hammer guide tube, parallel to the hammer’s plane of movement, about 3 cm behind the tab from the camera’s perspective. Initially, this ruler was intended to provide a scale bar for calculating velocity. However, the distance between the tab and the ruler introduced a significant error because 3 cm is about 10% of the distance between the camera and the tab (estimated at 30 cm). Ultimately, the height of the tab itself (14.71 mm at the outer edge) was used as the scale bar, making the ruler unnecessary. The scale bar was defined on a video frame when the tab was near the midpoint between the top of the image and the impact location. Such a frame is shown in Figure 8. The tab should appear smaller when it is nearer the outer edges of the image. In the middle of the frame, this scale bar location should minimize the error in the linear regression due to image distortion. However, it was deemed impractical to apply a systematic distortion correction due to the blur in these videos. The blur was inconsistent across frames and resulted in unpredictable variance in the tab’s height across different positions in the videos. The tab’s height is approximately 120 pixels tall in these videos. The theoretical impact velocity under freefall conditions was calculated based on the drop height measured by the eSPT for each blow. In theory, this freefall velocity should be the upper boundary for impact velocity measurements. If a measurement exceeds this boundary, it may either indicate the magnitude of error in the drop height or the impact velocity measurements. 33 3.3.3 Penetration Distance and Blow Count Measurement Real-time calculation of test penetration depth is another feature of the eSPT verified during this research. The penetration depth measurement is used to automatically calculate the blow count for each 6-inch segment of the 18-inch test, as n1, n2 and n3. The eSPT also automatically uses this information to end the test after 18 inches or if the soil sampling tube reaches refusal. In order to verify the eSPT’s penetration distance measurements (and possibly the accelerometer data), careful hand measurements of penetration distance were taken with a measuring tape. The test chamber design makes these measurements easy because the top of the casing is used as an immobile measurement platform. Immobility is further assured by the steel skirt welded to the top of the casing, which prevents the closed-bottom casing from being driven down into the ground. Three lines were marked with chalk on the drill string with 6-inch spacing at the start of the test, as shown in Figure 10. As each chalk mark disappeared below ground, the blow count was recorded. Data for this function of the eSPT are provided by the same sensors that record the hammer displacement. One sensor is in the elevator assembly, and the other is in the hammer. Together they sense the cumulative distance travelled by the anvil and sampler. Verification of the precision and bias of the penetration distance function of the eSPT is also an indirect verification of the displacement sensors used to calculate hammer velocity. However, this indirect verification process will not be emphasized further. 34 Figure 10. Manual penetration depth measurement 3.4 Stress Wave Energy Measurement The stress wave energy was measured according to the ASTM D4633 (2016) standard method. Two devices were used for measuring stress waves. The first is the SPT Analyzer from PDI, almost universally used in research and industry. This device will be referred to as the PDA, after the software package that it comes with, Pile Driving Analyzer. The other device was made by Northwoods Instruments for BC Hydro. These devices have strain gauges and either one or two accelerometers mounted on a piece of AW rod. This instrumented rod is called the instrumented 35 subassembly or transducer subassembly (ASTM, 2016) and is installed into the drill string immediately below the anvil. The strain gauges are used to calculate the force within the drill rod, and the accelerometers are used to calculate the velocity of the drill rod. The devices both have piezoresistive accelerometers and a 100 kHz sampling frequency. Each comes with a portable computer that samples the sensors, filters the signals, and digitally integrates the acceleration to produce velocity. Table 3-1. SPT stress wave measurement devices used in this thesis Stress Wave Energy Device Characteristics SPT Analyzer (PDA) • 100 kHz sampling rate per channel • Digital integration of acceleration • 0.61 m (2’) long AW rod instrumented subassembly • Two piezo-resistive accelerometers with a 20,000 g linear range and 30,000 g shock capacity • Stud-mounted accelerometers • 150 ms maximum record length • The signal was baselined by averaging the full 150 ms trace Northwoods Instruments • 100 kHz sampling rate per channel • Digital integration of acceleration • 1.22 m (4’) long AW rod instrumented subassembly • One piezo-resistive accelerometer with a 20,000 g linear range and 60,000 g shock capacity (Endevco 7270A) • Double screwed accelerometer chamfered to match the curvature of the rod, also about 1/5th the mass of the stud-mounted accelerometers (Howie et al., 2003). • Accelerometer is also bonded to the rod for maximum connection stiffness • 80 ms maximum record length • The signal was baselined by averaging the full 80 ms trace A significant difference between these two devices is that the Northwoods’ accelerometer is more rigidly mounted than the PDA’s accelerometers, which results in the detection of much higher frequencies. Also, the Northwoods instrumented subassembly is twice as long as the PDA, 1.22 m 36 compared to 0.61 m, meaning the first change in cross-sectional area that the stress waves meet is 0.61 m farther away with the Northwoods device. Other differences are discussed below. In this section, force and scaled velocity units are imperial (kips) for comparison with Howie et al. (2003). 3.4.1 Pile Driving Analyzer The PDA instrumented subassembly is shown installed in the drill string below the anvil in Figure 11. A new AW coupler was used directly under the instrumented subassembly with the PDA and Northwoods. This joint will degrade the data quality if it loosens during a test. Furthermore, as with all the joints, electrical tape was used to keep the joint from vibrating loose (Figure 12). 37 Figure 11. PDA instrumented subassembly Force transducers Accelerometers AW coupler 38 Figure 12. New taped AW coupler below the PDA instrumented subassembly Some PDA ETR results will be presented in the following paragraphs (and Figure 12) to explain why some PDA data were discarded. Testing was done in the shallow test hole first (8.5 m rod), then the medium (16.2 m rod), and finally the deepest hole (32 m rod). After the first test with the 16.2 m rod length, one of the PDA accelerometers (A2) began to produce erratic velocities. In post-processing, each accelerometer was disabled separately, and ETR was re-calculated. With the 16.2 m rod length, A2 alone produced an average ETR value 8.3% greater than A1. With the 32 m rod length, the difference was 18%. The PDA ETR data are presented chronologically in Figure 13, having occurred over four days from September 15-18, 2021. The ETR is presented using each accelerometer individually in the first two plots at different time scales. The resultant ETR is presented alongside the e1 in the third plot after discarding the poor data from accelerometer 2 (A2). After reviewing the ETR values and the force-velocity traces, it was decided to discard all the A2 data starting from the end of the first test at 16.2 m rod length (blows 341 to 503 in Figure 13). 39 Theoretically, the ETR should always be less than or equal to e1. In the third plot of Figure 13, this is true for most PDA blows. Often the first blow of each test has an abnormally high ETR. These blows were included nonetheless. 40 Figure 13. PDA ETR vs blow number: complete chronological sequence The acceleration traces from both accelerometers are shown for a typical blow with the PDA device and 8.5 m rod length in Figure 14 (PDA 8.5m Test #2, Blow #53). The maximum acceleration measured with A1 was 5,200 g at the first peak, while A2 only measured 3,450 g. At Start 32 m rod length Begin to discard A2 data 41 this early stage of the data collection, the two accelerometers still produced reasonably similar traces. The two accelerometers should not be expected to give identical results due to the frequent eccentricity of hammer-anvil contact, which causes bending in the drill rods. Figure 14. PDA acceleration traces with 8.5 m rod length: entire 150 ms record not shown The Fast Fourier Transform (FFT) of the acceleration trace in Figure 14 is shown in Figure 15. The x-axis extends to the Nyquist frequency of 50 kHz (half the sampling frequency of 100 kHz). There is significant frequency content up to 15 kHz, with the largest amplitude at 2 kHz. The PDA software includes a simple FFT module that does not allow the user to export raw frequency information. The graph displayed in the PDA software had the same shape as the FFT in Figure 15. The FFT was produced in Excel and R (R Core Team, version 4.1.2). The acceleration trace was pasted into R to perform the function Amplitude=2*abs(FFT(x))/L, where L is the number of data points, and x is the acceleration trace. The Amplitude output was then pasted back into Excel for plotting. The y-axis magnitude on the FFT graphs is less important than the relative amplitude across the frequency scale (x-axis). However, the magnitudes of the FFTs presented here (units of g/Hz) can be reliably compared to each other because they were all produced in the same way. Initial impact sensed by accelerometer Second impact sensed by accelerometer 42 Figure 15. PDA FFT of acceleration with 8.5 m rod length Force and velocity traces for a typical blow from the PDA with the 8.5 m rod length are shown in Figure 16 (PDA 8.5m Test #2, Blow #53, same blow for which acceleration is shown above), and the 16.2 m rod length in Figure 17 (PDA 16.2 m Test #1, Blow #24). The units are presented in kips (1000 lbs force). The velocity is converted to kips by multiplying by the impedance (Z) of the steel rods (Eq. 11). The impedance is the product of the Young’s modulus of steel (Em) and the cross-sectional area of the rod divided by the speed of stress wave propagation in steel (c). The following figures show the force-velocity proportionality represented by FMX/VMX (maximum force over maximum velocity). Ideal proportionality with a perfect SPT rod having constant cross-section and infinite length would have FMX/VMX=1. In reality, the coupler at the bottom of the instrumented subassembly will cause upwards tensile reflections, which return to the instruments before the initial force and velocity peaks. This upward tensile stress wave causes a reduction in force and increased velocity. The shorter instrumented rod will generally result in decreased proportionality, as is seen below comparing the 0.61m and 1.22m rods for the PDA and Northwoods device. Eq. 11 𝑆𝑐𝑎𝑙𝑒𝑑 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 = 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 ∗ 𝑍 , where 𝑍 =𝐸𝑚∗𝐴𝑟𝑜𝑑𝑐 The first velocity peak probably lags behind the force peak “due to relative motion between accelerometer and rod,” as Howie et al. (2003) stated. The “time to rise” from impact to peak velocity is 0.3 ms for both the PDA blows displayed below. It is unclear why the maximum Nyquist frequency 43 velocity is 25% greater than the maximum force. If the measurement system were ideal, the maximum force and velocity should be equal because the instrumented subassembly and drill rods have the same cross-sectional area. Upward tensile stress wave reflections are generally the cause of lost proportionality. However, an upward tensile reflection should only occur if there is a reduction in cross-sectional area. This reduction does occur at the bottom of the soil sampler (and at the top of the hammer). 2L/c is the theoretical time when the principal tensile reflection should reach the instrumentation. However, it is generally expected that this returning tensile wave would arrive slightly later than 2L/c. The arrival of the major returning tensile wave at the transducer is identified as the time when the velocity becomes significantly greater than the force. This moment can be seen in each example force and velocity traces shown below, such as at 8.0 ms in Figure 16 (3.1 ms after impact). The force and velocity traces with the PDA always returned to zero at the end of the blow record, which indicates that the baselining technique was appropriate and the records were sufficiently long. Howie et al. (2003) describe the purpose of baselining as “the noise level of the acceleration must be accurately averaged to zero, or there will be a tendency for the computed velocity, displacement, and FVenergy to drift above or below the true value through the integration process.” The average of all acceleration readings is taken as the baseline. This baseline acceleration, whether positive or negative, is added to all acceleration values before calculating velocity by integration. The PDA device allows the user to specify which portion of the record to use for baselining. However, using anything other than the entire record would be unwise. This method worked because the record was long enough to allow all movement to cease. The entire 150 ms record is plotted at the bottom of Figure 16, showing that the traces trend to zero. Both the PDA and Northwoods devices carry out the baselining automatically. The raw data from both devices were also baselined manually in a spreadsheet for producing the acceleration and FFT graphs. The maximum FVenergy was reached at close to 4L/c for the blows in Figure 16 and Figure 17. 4L/c is when the principal stress wave has travelled twice to the bottom of the sampler and back to the transducers. For the blow with the 8.5 m rod length, the maximum FVenergy corresponds closely with the second impact sensed by the accelerometers (see Figure 14). 44 Figure 16. PDA 8.5 m typical blow: quality assessment of energy data at three scales Force and velocity trend to zero FMX/VMX=0.8 Primary tensile reflection returns slightly after 2L/c 45 Figure 17. PDA 16.2 m typical blow: quality assessment of energy data at three scales FMX/VMX=0.89 Force and velocity trend to zero Primary tensile reflection returns slightly after 2L/c 46 3.4.2 Northwoods Instruments The Northwoods device has a single Endevco 7270A piezoresistive accelerometer with a linear range of 20,000 g and a shock limit of 60,000 g. The accelerometer is the same as Howie et al. (2003) used. They write, “It was attached on an aluminum mounting block with the combined accelerometer and mount mass less than 20 g. The mount was chamfered to match the curvature of the rod.” The accelerometer is attached to the rod with two #6-32 machine screws and glued to the rod for maximum connection stiffness. The mass of the Northwoods’ accelerometer is about 1/5th of each PDA accelerometer. Due to the smaller size and chamfered mount, the centre of gravity is much closer to the rod centre. The difference in the accelerometers can be seen below in the acceleration and FFT data (e.g. Figure 20 vs. Figure 15). The Northwoods ETR data are presented chronologically in Figure 18 and were collected over three days from September 16-18, 2020. No Northwoods data were discarded. Figure 18. Northwoods ETR complete chronological sequence The acceleration trace for a typical blow with the Northwoods device and 8.5 m rod length is shown in Figure 19 (Northwoods 8.5m Test #4, Blow #3). The maximum acceleration is 9,400 g at the first peak. The Fast Fourier Transform of the acceleration trace in Figure 19 is shown in Figure 20. There is significant frequency content up to the Nyquist frequency of 50 kHz, with the largest amplitude at 23 kHz. This FFT has a similar shape to the one shown in Howie et al. (2003) for the “Delrin (stiff 47 plastic) accelerometer mount,” which also had significant frequency content of up to 50 kHz. The Northwoods device detects much higher accelerations and frequencies than the PDA. Figure 19. Northwoods acceleration trace with 8.5 m rod length: entire 80 ms record not shown Figure 20. Northwoods FFT of acceleration with 8.5 m rod length Force and velocity traces from a typical blow from the Northwoods device with the 8.5 m rod are shown in Figure 21 (Northwoods 8.5 m Test #4, Blow #3) and the 16.2 m rod in Figure 22 (Northwoods 16.2 m Test #3, Blow #9). Force-velocity proportionality until the first peak was excellent with the Northwoods instrument throughout the data collection period. The time to rise Initial impact sensed by accelerometer Nyquist frequency Second impact sensed by accelerometer 48 for the Northwoods velocity traces below is 0.1 ms, compared to 0.3 ms for the PDA device, which shows how much stiffer the accelerometer-rod connection is with the Northwoods. The principal tensile wave arrives slightly earlier than the PDA device. The earlier waves could be related to the longer instrumented rod or a sign of tighter drill rod joints. The Northwoods traces also return to zero by the end of the record, indicating that the averaging baseline technique was effective. 49 Figure 21. Northwoods 8.5 m typical blow: quality assessment of energy data at three scales FMX/VMX=0.89 Minor tensile reflections 50 Figure 22. Northwoods 16.2 m typical blow: quality assessment of energy data at three scales FMX/VMX=0.94 51 3.4.3 Data Processing All data were processed in accordance with the PDA and Northwoods user manuals, with most of the data processing (filtration, integration, baselining, conversion to engineering units) done automatically by the respective programs. All the data quality checks outlined by Howie et al. (2003) were followed to ensure the reproducibility of the results. The PDA literature does not describe the anti-bias filters that it uses. For the Northwoods device, “The anti-aliasing filters are hardware three-pole low-pass Butterworth filters made using a Burr-Brown UAF42 chip… [with a] 33kHz 3dB cut-off [for both force and velocity]” (S. Jackson, personal communication, March 19, 2022). The measured strain is divided by the rod impedance (𝐸 × 𝐴 𝑐⁄ , where E is Young’s modulus of steel, A is the cross-sectional area of the rod, and c is the stress wave velocity in steel) to calculate the force in units of kips or kN. The measured acceleration trace is integrated with respect to time to produce a velocity trace in engineering units. The FV equation (Eq. 3) integrates the force times the velocity in the drill rod. As prescribed in ASTM D4633 (2016), the FV equation is integrated from before impact until the force and velocity return to zero. However, the maximum value of the integral is taken rather than the final value. The Northwoods program exports the force and velocity traces in ASCII files, while the PDA program uses CSV format. For plotting, both file types were converted to the XLSX file type. Acceleration traces were transformed into the frequency domain (FFT) using R (R Core Team, version 4.1.2). The results from the two stress wave devices were compared as a quality control check. Table 3-2 shows other statistics that were used for comparison. The PDA device's average time to peak force is also about twice as long as the Northwoods device. The difference between the two devices shows the uncertainty involved in stress wave measurement due to the difficulty of measuring stress waves and the variation in the design of these devices. 52 Table 3-2. Force and velocity data comparison: PDA and Northwoods Avg. peak force (kips) Average peak velocity (kips) (ft/s) Average time to peak force (ms) Average time to peak velocity (ms) PDA 8.5 m 33.0 38.9 (19.6 ft/s) 0.20 (SD=0.01)† 0.30 (SD=0.01)† PDA 16.2 m 32.6 37.8 (19.2 ft/s)* 0.19 (SD=0.01)† 0.29 (SD=0.01)† PDA 32 m 30.1 37.1 (18.7 ft/s) 0.20 (SD=0.01)† 0.31 (SD=0.01)† Northwoods 8.5 m 32.6 35.4 (17.9 ft/s) 0.11 (SD=0.02) 0.13 (SD=0.03) Northwoods 16.2 m 32.5 35.2 (17.8 ft/s) 0.11 (SD=0.02) 0.09 (SD=0.02) Northwoods 32 m 31.2 32.8 (16.5 ft/s) 0.14 (SD=0.01) 0.12 (SD=0.03) *only for one of the tests †not given by the PDA program, measured on 14 random blows in total The displacement records of the hammer produced by the eSPT can be used to verify that the accelerometers are functioning as expected. The acceleration data from the stress wave devices can be double integrated to calculate the displacement of the drill string and compared to the eSPT’s output of penetration distance per blow. However, this was not done. It was satisfactory that the force and velocity traces returned to zero by the end of the record. 53 Chapter 4: Results of Verification of MARL’s Electronic SPT This chapter presents the test results for two objectives. The first objective is to assess the precision and bias of the eSPT's automated features for further research and SPT testing in general. The second objective is to compare impact energy and stress wave energy measurements during the SPT. The comparison was made with rod lengths of 8.5, 16.2, and 32.0 m. The results are presented in metric and imperial units because the SPT methodology and equipment were developed in imperial units. The following section presents the precision and bias assessment of the eSPT's electronic features using a string potentiometer, a high-speed camera, and manual counting/measurements. Table 4-1 summarizes the eSPT verification testing program. The relatively small dataset indicates that more work can be done to assess the precision and bias of the eSPT thoroughly. However, the data presented here were deemed satisfactory to support the ETR and e1 data collection program. Table 4-1. Summary of the eSPT verification testing program Method eSPT feature verified Number of blows presented Number of high-quality blows recorded String potentiometer Displacement and velocity 2 4 High-Speed Camera Velocity 5 5 Manual blow count Penetration distance and blow count 1000+ 1000+ 4.1 String Potentiometer eSPT Verification Figure 23 and Figure 24 show displacement-time data from the eSPT and the string potentiometer for two successive blows. The displacement-time curves have a concave-downwards shape as the hammer drops (accelerates). The curve becomes flat when the hammer comes to rest on the anvil. These two displacement-time curves are indistinguishable (in excellent agreement) for the entire record, as shown in these graphs, where it is difficult to see the string potentiometer displacement behind the markers for the eSPT displacement. This level of agreement was typical for four sequential blows. This agreement is seen as a strong verification of the eSPT’s ability to record 54 displacement accurately. However, compared to measuring hammer displacement, it is more difficult and interesting to measure hammer velocity as an SPT quality control and research tool. The sampling rates of both the eSPT and the string potentiometer were equal during this experiment. This rate is 7.5 ms/sample on average, neither perfectly constant. The string pot produced displacement values every 1 ms; however, they were mostly duplicates. After filtering out the duplicates with an Excel Macro (Hughes, 2021), the average resultant sample rate was 7.5 ms/sample. The eSPT also had an average sample rate of 7.5 ms/sample, with a skewed distribution range from 5 to 19 ms/sample. Figure 23. String potentiometer and eSPT displacement and velocity: Drop 1 55 Figure 24. String potentiometer and eSPT displacement and velocity: Drop 2 Even though the string potentiometer and eSPT displacement curves are in excellent agreement, there is some significant variance between the two velocity curves, especially as the hammer approaches maximum velocity. Therefore, a quantitative comparison of the velocities at the point of impact is necessary. The velocity of the hammer at the time of impact is the velocity of interest for SPT testing and research. Figure 27 and Figure 28 display the velocity versus time from the eSPT and the string pot, emphasizing the velocity trend up to the time of impact. Figure 27 and Figure 28 show the same two drops with a closer look at the moment of impact. Due to the relatively high variance (lack of smoothness) in the string potentiometer velocity-time curve, a linear regression was required to calculate the maximum velocity consistently. Under freefall conditions, the velocity-time history of the hammer should be linear. The linear regression was applied to the velocity-time curve up to the moment of impact. In Figure 27 and Figure 28, the string potentiometer velocity curve changes colour after impact. This colour change shows which data points were used to generate the linear regression. The average acceleration of the hammer calculated by each device is seen as the first constant of the regression equations. As described in the methodology section, the string potentiometer reduces the freefall acceleration of the hammer to 9.66 m/s2. The average acceleration is slightly less than 9.66 m/s2 due primarily to the frictional contact between the hammer and guide tube. 56 Figure 25. String potentiometer and eSPT impact velocity: Drop 1 Figure 26. String potentiometer and eSPT impact velocity: Drop 2 57 Figure 27. Close-up string potentiometer and eSPT impact velocity: Drop 1 Figure 28. Close-up string potentiometer and eSPT impact velocity: Drop 2 The calculated impact velocity difference between the eSPT and string potentiometer was less than 0.1 m/s for the two drops. This equates to a 2.4% or less difference in velocities for the two measurement techniques (Table 4-2). Given that the average accelerations of the hammer were significantly less than freefall (9.81 m/s2) and that the e1 values are close to 100%, it is clear that the drop height for these two blows was greater than the SPT standard (762 mm). 785 mm and 802 mm drop heights result from back-calculation using the string potentiometer average accelerations and final velocities. 58 Table 4-2. Maximum velocity: eSPT and string potentiometer eSPT e1 (maximum velocity) String Pot. e1 (maximum velocity) Percent Difference Drop 1 99.1% (3.83 m/s) 102.5% (3.92 m/s) +2.4% Drop 2 98.3% (3.80 m/s) 99.3% (3.85 m/s) +1.4% The string potentiometer velocity-time curve is less smooth than the eSPT velocity-time curve, as seen in Figure 27 through Figure 28. This greater variance in the string potentiometer data was expected because the string was attached about 200 mm from the hammer's centre, whereas the eSPT’s displacement sensor is centred inside the hammer. This linkage arm exaggerates any non-linear motion in the falling hammer. The string potentiometer also exerts a small eccentric force on the hammer (9.8 N), which causes some of the hammer’s non-linear motion. The linkage arm may also cause a higher-frequency, low-amplitude noise signal in the string potentiometer velocity trace prior to impact, which is not present in the eSPT velocity trace. Up to the time of impact, there was excellent agreement in the eSPT velocity-time curves and the linear regression of the string potentiometer velocity-time curves. One source of error in the data interpretation is knowing the precise “time of impact” that goes into the trendline equation. If the time of impact (maximum velocity) from the eSPT velocity-time curve were used in the string potentiometer linear regression equation, there would be less than a 1% difference for both blows. One solution to reducing this error could be a sensor that provides a time history of hammer-anvil contact, such as Daniel et al. (2005). The root mean squared error between the string potentiometer and eSPT displacement over the time periods displayed in Figure 23 and Figure 24 are 5.3 mm (Drop 1, 1.2 to 2.0 s) and 3.1 mm (Drop 2, 7.9 to 8.7 s). The displacement residual is plotted in Figure 29. As with Figure 23 and Figure 24, it is difficult to see the string potentiometer displacement behind the markers for the eSPT displacement. The manufacturer of the eSPT’s displacement sensor explained that the peak in this residual curve is caused by the sensor switching to “fast mode.” This switch appears to have been near completion before impact when the residual returns to less than 0.2 mm. Even the peak value of the residual curve is still insignificant, at about one-thousandth of the full scale of measurement. The residual curve for Drop 2 is similar, although it has a slightly smaller peak value. 59 Figure 29. String potentiometer and eSPT squared displacement residual: Drop 1 The sampling rate of both the eSPT and the string potentiometer were equal, so the difference in the impact velocities cannot be due to the sampling rate. The difference in maximum velocities as measured by these two devices is well within the expected precision for SPT testing. Energy transfer measurements in SPT research are typically expected to have a 5% margin of error. However, the data produced with this string potentiometer test set-up required substantial post-processing and a verification tool with a sampling rate faster than the eSPT was desired. For these reasons, a high-speed camera verification method was also used. 4.2 High-Speed Camera eSPT Verification Five videos from five different SPT blows were analyzed and are presented in this section. Figure 30 through Figure 34 show the velocity-time curves for five drops, calculated from the eSPT and the high-speed camera. Like the string potentiometer graphs in the previous section, a change in colour in Figure 30 through Figure 34 indicates which points were used to produce the linear regressions. The time corresponding to the final data point included in the linear regression was used as “x” in the linear regression equation to calculate the maximum velocity. The linear regression equations are displayed for each drop plotted in these figures. Figure 35 and Figure 36 show close-up graphs of the moment of impact for Drops 2 and 4 because they are the clearest examples of the eSPT “missing” the peak velocity due to having a slower 60 sampling rate than the camera. Note that the data series change colour from pre- to post-impact to show which data points are used to calculate the linear regressions. This colour change is not related to the eSPT’s own maximum velocity output. The eSPT takes the maximum value as the impact velocity during normal operation. The eSPT produces the smoothest velocity curve of all the measurement devices (eSPT, string pot, camera). The eSPT velocity follows a linear trend up to the impact, having an R-squared greater than 0.99. It is assumed that this smartphone camera could produce a significantly more linear velocity curve if more data were collected before impact and if a surveyor’s target were introduced. Figure 30. Drop 1: eSPT and camera impact velocity, 960 fps 61 Figure 31. Drop 2: eSPT and camera impact velocity, 480 fps Figure 32. Drop 3: eSPT and camera impact velocity, 480 fps 62 Figure 33. Drop 4: eSPT and camera impact velocity, 480 fps Figure 34. Drop 5: eSPT and camera impact velocity, 480 fps 63 Figure 35. Drop 2: eSPT and camera impact velocity close-up, 480 fps. Figure 36. Drop 4: eSPT and camera impact velocity closeup, 480 fps. The maximum velocities found by the camera and the eSPT are listed in Table 4-3 below. 64 Table 4-3. Maximum velocity: eSPT and high-speed camera eSPT % of frictionless drop (max velocity) Camera % of frictionless drop (max velocity) % Diff. Drop 1 960 FPS 95.6% (3.70 m/s) 98.1% (3.80 m/s) +2.6% Drop 2 480 FPS 93.6% (3.61 m/s) 97.1% (3.75 m/s) +3.8% Drop 3 480 FPS 97.1% (3.81 m/s) 96.8% (3.80 m/s) -0.4% Drop 4 480 FPS 96.3% (3.78 m/s) 98.2% (3.84 m/s) +2.0% Drop 5 480 FPS 96.8% (3.83 m/s) 98.2% (3.89 m/s) +1.4% The eSPT’s actual measured drop height was used to calculate the theoretical frictionless impact velocity to check these results further. This value is slightly different from e1, which uses the standard SPT drop height rather than the actual drop height. This check was successful because the theoretical frictionless impact velocities were always greater than the measured velocities. Theoretical efficiency should be less than 100% due primarily to friction from the contact of the hammer and the guide tube. The eSPT’s drop height measurement was indirectly verified using the string potentiometer in the previous section. The displacement measured by the string potentiometer and eSPT is indistinguishable, as seen in Figure 23 and Figure 24. Agreement on displacement throughout the hammer cycle for all four blows also implied agreement on drop height. More work could be done to verify the eSPT’s drop height measurements. For Drop 2, if the linear regression were used to calculate maximum velocity from the eSPT data, the velocity would be about 2.5% greater. For Drop 4, the velocity would be about 1.8% greater. This result is displayed graphically in Figure 35, Figure 36, and Figure 37. For these calculations, the impact time used in the camera’s linear regression was also used in the eSPT’s linear regression equation. Perhaps a triangulation algorithm could be used by the eSPT to estimate a more accurate time of impact when there is no camera data available. Two linear regressions could be used to estimate the maximum impact velocity, as shown in Figure 37. The left image shows the raw eSPT data, having only absolute velocity values. The data in the right image have been adjusted based on the time of impact seen in the camera data, as described in Chapter 3. The non-absolute data should provide a less biased estimate of the deceleration rate. 65 Perhaps the eSPT software could be changed to record positive and negative velocity and produce a less biased triangulation, as seen on the right. However, the triangulation shown on the left is also a significant improvement over the simple maximum velocity value currently used by the eSPT. Figure 37. Triangulation of eSPT impact velocity A much easier way of improving the eSPT’s impact velocity measurement would be to calculate the average bias error and apply the same correction to all blows. Given the eSPT’s sample rate of 7.5 ms/sample, the theoretical maximum bias error that could occur is 1.91%, based on a free-fall impact energy of 475 J (Eq. 12). This 1.91% is a coefficient of variation (CV), Ω(V0) in the notation of Daniel (2008). Therefore, the average bias error is likely 0.8%, given a random distribution of values between 0 (perfect capture of impact velocity) and 1.9% (worst under-estimate). This one-sided (under-estimate) error is best seen in Drops 2 and 4 (Figure 35, Figure 36, and Figure 37). The camera data suggests that this one-sided error can exceed 2%. However, there is insufficient confidence in the precision of the camera-based impact velocities presented here to calibrate the eSPT using this method, and 2% is likely a conservative estimate of the upper limit of bias error in the eSPT’s impact velocity. 66 Eq. 12 Max. bias error = 1 −3.87𝑚/𝑠−9.81𝑚/𝑠2∗7.5𝑚𝑠3.87𝑚/𝑠= 1 −3.79𝑚/𝑠3.87𝑚/𝑠= 1.9% Given: 12∗ 63.5𝑘𝑔 ∗ (3.87𝑚/𝑠)2 = 475 Joules = 𝐸 ∗ The preceding discussion has assumed that 100% of the bias error in the eSPT’s impact velocity measurement comes from the sampling rate. It seems likely that the sampling rate is the most significant source of bias error, but there may be other sources. A more rigorous independent measurement is needed to establish an Accepted Reference Value and determine the total bias error of the eSPT’s impact velocity measurement. The camera used in this thesis may be capable of establishing an Accepted Reference Value of eSPT hammer impact velocity, but methodological improvements are needed. In hindsight, it would have been better to move the camera back farther to capture more of the drop. Some of the videos have too few frames (data points) before impact, resulting in a relatively large variance (low R-squared value) in velocity before impact. A distortion correction might have been practical if a better target were used, such as a white on black circle or triangle (e.g., Lee et al., 2014, or photogrammetry ground control targets). Such an improved target could be fixed to the metal tab. In summary, future video analysis should use the camera phone’s maximum frame rate of 960/s, an improved black and white target, and a slightly farther camera position so that about 0.4 m of the drop height before impact is visible. 4.3 Penetration Distance and Blow Count Verification The eSPT calculates the blow count based on the measurement of the cumulative penetration distance during the SPT. Therefore, the verification of blow count is taken as representative of both features. Twenty-five 450 mm (18-inch) SPTs were performed in three cased boreholes with depths of 8.5 to 32 m (Figure 4). The n1, n2 and n3 counts were recorded for each test using the chalk mark method and a verbal count. Blow counts were accurately recorded to the nearest blow by holding a ruler over the top of the casing during the test’s operation. The element of human error involved in this process was whether the chalk line was touching the ruler or not. For this reason, it should be expected that the blow counts from the eSPT and hand measurements will sometimes differ by 67 one blow. The results of these twenty-five tests were never in disagreement by more than one blow per test or segment. A one-blow disagreement for a 450 mm (18-inch) test only occurred during tests with blow counts greater than 50. The eSPT was shown to record the correct blow count reliably under a wide range of N counts. The N count is the sum of the blow counts from 150-450 mm (6-18 inches) of penetration. Six SPTs were performed with N of 11 or less (due to water infiltration into the deepest casing). Two of these tests had more than 6 inches of penetration on the first blow. At the other extreme, 7 SPTs were performed with N greater than 70. At N = 70, the average penetration distance per blow is less than 5 mm. Under all these conditions, the eSPT blow count was never more than one blow apart from the count found using the manual chalk measurement method. This method provided a high degree of confidence that the eSPT reliably records the correct cumulative penetration distance and blow count and stops the test correctly when the 18-inch penetration is completed. 4.4 Summary of eSPT Verification A string potentiometer was mounted to the eSPT to measure the hammer displacement and velocity independently of the eSPT’s onboard instrumentation. Only two blows were analyzed using the string pot; however, this was intended as a preliminary verification. The agreement in the displacement traces between the string potentiometer and the eSPT was better than necessary for this application. The impact velocities measured with the string potentiometer and eSPT were within 2.4% for Drop 1 and 1.4% for Drop 2. A second technique to independently measure the hammer impact velocity relied on analyzing images captured by a high-speed camera. The results from five videos of hammer impacts were presented, with the most significant impact velocity discrepancy between the eSPT and camera being 3.8%. The camera's impact velocity measurements were not of sufficient quality to establish an Accepted Reference Value and calibrate the eSPT’s measurements. Therefore, a statement on the total bias of the eSPT’s impact velocity measurements is not possible without an improved camera method. Some suggestions were made for capturing better videos with the same high-speed camera in the future. 68 The higher sample rate of the camera served to demonstrate the one-sided bias error in the eSPT’s impact velocity measurements due to the sample rate. Based on the theoretical free-fall impact velocity of the hammer and the eSPT’s sample rate, the upper limit of bias error due to the eSPT’s sampling rate should be 1.9% (Equation 12). Two suggestions were made to improve the eSPT software to reduce or eliminate this bias error due to the sample rate. The first was a triangulation algorithm for estimating the time of impact (see Figure 37). Alternatively, if the bias is assumed to be entirely due to the sample rate and randomly distributed between no and 1.9% error, a correction of +0.8% could be applied to all impact velocity measurements. In the following chapter, the imprecision and bias of the eSPT’s impact velocity measurements will be assumed as insignificant. 69 Chapter 5: Analysis and Interpretation of the Impact and Stress Wave Energy Data In this chapter, the e1 and ETR data are first presented separately for each rod length and then analyzed and interpreted. The primary questions are: 1) Are the data normally distributed? 2) Are there reliable relationships between these three values? e2, the quotient of e1 and ETR, is the most important relationship to analyze. 3) Is there a statistically significant difference between e1 and ETR? 4) Is there a discernible trend related to rod length or hammer set? 5.1 Overview of Impact Energy and Stress Wave Energy Data This section presents the collected data set of impact kinetic energy and stress wave energy. ETR (energy transfer ratio) is the quotient of stress wave energy (found using the FV method) by the standard SPT energy, as defined in Eq. 2 and Eq. 6. e1 is the quotient of impact kinetic energy by the standard SPT energy, as defined in Eq. 5. Table 5-1 shows the number of blows for which ETR and impact velocity was measured. 70 Table 5-1. Size of ETR and impact velocity data sets for each stress wave device and rod length Stress wave device Rod length (m) ETR blows Impact velocity blows All data All 1,327 1,263 Northwoods All 828 798 PDA All 499 465 Both 8.5 688 669 Both 16.2 579 571 Both 32 60 23 Northwoods 8.5 456 456 PDA 8.5 232 213 Northwoods 16.2 326 319 PDA 16.2 253 252 Northwoods 32 46 23 PDA 32 14 0 The distributions of these ETR and e1 datasets are displayed in histograms in Figure 38. The grey lines are normal distributions having the same mean (μ), standard deviation (σ), and area as the ETR distributions. The μ and σ values shown on the histograms are for the ETR. The ETR data can be roughly described as normally distributed, especially with the Northwoods data. Statistical definitions of normality are discussed later in this chapter. 71 Figure 38. Overview histograms of ETR and e1 data. The results show that the difference between impact kinetic energy and stress wave energy (referred to as e1 and ETR when expressed as a percent of E*) is predictable and, in some cases, insignificant. The average ETR is 89.9% ± 3.5% for all 1,327 blows, corresponding to a 68.3% confidence interval (± one standard deviation). The average e1 is 93.4% ± 1.7%, within the one standard deviation confidence interval of the average ETR. Each energy device and rod length produced a slightly different result. Using only the Northwoods device for ETR measurement produced an average ETR of less than one standard deviation below the average e1. However, PDI's SPT Analyzer (PDA) measured a slightly lower average ETR with a smaller standard deviation. The average difference between ETR and e1 with the PDA was 3.8%. 5.1.1 Box and Whisker Plots of Impact Energy and Stress Wave Energy The impact energy and stress wave energy comparison results are displayed as box and whisker plots in Figure 39. Only stress wave energy was measured for 37 blows with the 32 m rod length, including all 14 PDA blows and 23 of the 46 Northwoods blows. For this reason, the 32 m data in Figure 39 are all presented on a single graph, whereas the 8.5 m and 16.2 m graphs show the PDA 72 and Northwoods devices separately. The “n=” in these box plots indicates the number of blows for which ETR was measured. The boxes represent the interquartile range (IQR = 75th to 25th percentile range). The line across the box is the median, and the x-mark is the mean. The "whisker" lines either extend to the extreme data point or the farthest data point within 1.5*IQR beyond the IQR. All points (including outliers) are also shown on these two figures to clarify which the whiskers represent. If outliers are plotted, the whiskers only extend to the farthest data point within 1.5*IQR. Some of the farthest PDA outliers are not visible in these two figures, but all outliers were used to calculate all statistics. 73 Figure 39. Box plots of ETR (blue) and e1 (orange) 32 m rod 16.2 m rod 74 5.2 Breakdown of Collected Data by Rod Length As with Figure 38, the distributions of the ETR and e1 datasets are displayed in histograms in Figure 40 through Figure 43. The grey lines are normal distributions having the same mean (μ), standard deviation (σ), and area as the ETR distributions. Table 5-2 through Table 5-7 summarize the ETR and e1 results with a stress wave device and rod length combination. All standard deviations are sample standard deviations rather than population. e1 is calculated with Eq. 5. 5.2.1 8.5 m Rod Length The ETR datasets with the 8.5 m rod length follow a roughly normal distribution. A normal distribution with the same mean and standard deviation is plotted over the histograms to show how normal the distributions are. A roughly normal distribution is desirable because this indicates that the random errors in the ETR measurements will cancel out as the number of blows increases for a given day or testing program with the same SPT system. Breaking down the ETR and e1 results into the different rod lengths is the best way to indicate the bias error present in the stress wave measurements. Technically there is no way to determine the bias of the stress wave measurement, but the average difference in ETR of 2.3% under otherwise identical test conditions gives some idea of the possible magnitude of bias error. The 16.2 m dataset has the largest difference in average ETR (5.2%). 75 Figure 40. Histograms of ETR and e1 with 8.5 m rod length Table 5-2. PDA and eSPT summary data with 8.5 m rod length ETR Hammer Impact Velocity e1 Average 91.0% 3741 mm/s 93.6% Standard deviation 3.4% 30 mm/s 1.5% CV (SD/average) 3.7% 0.8% 1.6% n (number of blows) 232 213 213 Table 5-3. Northwoods and eSPT summary data with 8.5 m rod length ETR Hammer Impact Velocity e1 Average 88.7% 3748 mm/s 94.0% Standard deviation 3.2% 25 mm/s 1.2% CV (SD/average) 3.4% 0.7% 1.3% n (number of blows) 456 456 456 In the last plot of Figure 13 (shown again below as Figure 41), it appears that the application of electrical tape to the drill rod joints caused a relative decrease in ETR compared to e1. Before the tape was applied, the e1 and ETR were practically equal. The tape was not expected to decrease the 76 ETR but to keep the joints from coming loose. Loose joints have been shown to decrease the ETR (Howie et al., 2003; Byun & Lee, 2013). The counter-intuitive correspondence seen in this figure may be a coincidence. If not, the tape might have impeded energy transfer by holding joints open once they came loose. Figure 41. PDA ETR complete chronological sequence (shown again) 5.2.2 16.2 m Rod Length This data set is much more normally distributed than the 8.5 m. The Northwoods ETR and e1 are almost indistinguishable. Both accelerometers worked reasonably well for the first 104 blows with the PDA device. For the subsequent 149 blows, the data from A2 were discarded. After excluding the data from A2, the 16.2 m PDA ETR has the lowest standard deviation of any device-rod length combination. The 16.2 m PDA dataset also has the most positive excess kurtosis (most leptokurtic, see section 5.3). The PDA and Northwoods ETR data with the 16.2 m rod shows that the bias error present in stress wave measurement can be significant. The difference in average impact velocity was 0.4%, whereas the difference in average ETR was 5.2%. The stress wave device is the only known variable between these two tests. The rod couplers were taped for all blows with the 16.2 m rod, and the same procedure for tightening the joints was followed for all tests. Another seemingly minor variable is that the drill rod was 0.2 m longer with the PDA device, and one extra joint was present. Begin to discard A2 data 77 Figure 42. Histograms of ETR and e1 with 16.2 m rod length Table 5-4. PDA and eSPT summary data with 16.2 m rod length ETR Hammer Impact Velocity e1 Average 87.6% 3719 mm/s 92.5% Standard deviation 1.8% 32 mm/s 1.6% CV (SD/average) 2.0% 0.9% 1.7% n (number of blows) 253 252 252 Table 5-5. Northwoods and eSPT summary data with 16.2 m rod length ETR Hammer Impact Velocity e1 Average 92.8% 3735 mm/s 93.3% Standard deviation 3.1% 32 mm/s 1.6% CV (SD/average) 3.4% 0.9% 1.7% n (number of blows) 326 319 319 78 5.2.3 32 m Rod Length The ETR values with the 32 m rod are less normally distributed than with the shorter rods because the data set is the smallest. The eSPT data from the PDA 32 m test were lost. Therefore, the same e1 data from the Northwoods tests are shown on the combined ETR graph. Figure 43. Histograms of ETR and e1 with 32 m rod length Table 5-6. PDA summary data with 32 m rod length ETR Average 89.4% Standard deviation 1.9% CV (SD/average) 2.1% n (number of blows) 14 79 Table 5-7. Northwoods and eSPT summary data with 32 m rod length ETR Hammer Impact Velocity e1 Average 90.0% 3725 mm/s 92.8% Standard deviation 2.9% 40 mm/s 2.0% CV (SD/average) 3.2% 1.1% 2.1% n (number of blows) 46 23 23 5.3 Statistical Analysis of the Collected Data Statistical analysis of the impact velocity, drop height, and stress wave data are presented in multiple ways for comparative discussion. Given that the difference between the average ETR and average e1 was less than the standard deviation of ETR, it may be asked whether these two measurements are not statistically equivalent. Equivalence may be seen in the Northwoods 16.2 m dataset. However, the impact and stress wave energy generally seem statistically different. Furthermore, equivalence is irrelevant if the difference between the two is known. First, the data variance will be discussed. The variance in ETR was about twice as large as the impact kinetic energy (e1). Table 5-8 shows the variance of the ETR and e1 measurements. The eSPT's impact velocity measurement coefficient of variation (CV) was less than 1% on any given day, but the e1 is calculated using the square of the velocity and is therefore larger. ETR is unitless and is typically presented as a percentage. The ETR CV is larger than the ETR standard deviation (σ) because the average ETR was less than 1. 80 Table 5-8. Variance in ETR and e1 Dataset ETR count σ(ETR) (%) CV(ETR) (%) CV(e1) (%) All data 1,327 3.5 3.9 1.7 All Northwoods data 828 3.7 4.1 1.6 All PDA data 499 3.1 3.5 1.8 Northwoods 8.5 m 456 3.2 3.4 1.3 PDA 8.5 m 232 3.4 3.7 1.6 Northwoods 16.2 m 326 3.1 3.4 1.7 PDA 16.2 m 253 1.8 2.0 1.7 Northwoods 32 m 46 2.9 3.2 2.1* PDA 32 m 14 1.9 2.1 - *Impact velocity was only measured for 23 of these 46 blows. The magnitude of the variance in ETR seen in Table 5-8 is similar to that found in the literature. For example, Lee et al. (2014) reported σ between 2.5 and 3.9% using a Korean donut hammer; and Lukiantchuki et al. (2017) reported σ between 3.1 and 4.4% using a Brazilian automatic trip hammer. Both of these example papers used an SPT Analyzer (PDA). The database of safety hammer e2 measurements in Daniel (2008) had significantly larger standard deviations. Daniel’s entire safety hammer SPT tests database has an 11.1% average standard deviation compared to 4.0% for the data presented in this thesis (see Table 5-13). The individual studies in Daniel’s database had slightly smaller σ but not significantly. Some of the 11.2% standard deviation will be due to the different drill operators, stress wave measurement systems, and drill rods. ASTM D1586 (2018) reports on a study of 32 automatic SPT hammers giving an ETR standard deviation of 6 to 8% (after Biringen and Davie, 2008). See Table 5-9. This comparison indicates that the data presented in this thesis were collected in a relatively well-controlled environment. 81 Table 5-9. Comparison of σ(ETR) results to literature Reference Hammer type σ(ETR) Daniel (2008, database) Safety (FV and F2 method) 9.2-13.3% Biringen and Davie (2008) Automatic 6-8% Lee et al. (2014) Korean Donut 2.5-3.9% Lukiantchuki et al. (2017) Brazilian 3.1-4.4% Miller (2022) Automatic 3.1-4.0% Table 5-10 to Table 5-12 include more statistics to describe the distribution of the ETR datasets and their relationship to the e1 datasets. Table 5-10 presents the mean, standard deviation, and confidence intervals, with the final column showing the two-sided confidence interval of the ETR distribution that includes the mean e1. The smaller the confidence interval is, the more statistically equivalent the ETR and e1 distributions are. Figure 44 shows an example of a 68.3% confidence interval (CI) on the standard normal distribution where the mean ETR is zero, the standard deviation of ETR is one, and the mean e1 is one, resulting in a z-score of 1. Each confidence interval smaller than 68.3% (corresponding to one standard deviation) is shaded blue in Table 5-10. Eq. 13 𝑍 =𝑥−μσ Eq. 14 Confidence Interval = 𝑃(−𝑍 < 𝑥 < 𝑍) Figure 44. Standard normal distribution: confidence interval for a z-score of 1  68.3% CI → 82 Table 5-10. ETR and e1 statistics: mean, standard deviation, and confidence interval Dataset Count μ(ETR) σ(ETR) μ(e1) Confidence interval All data 1,327 89.9% 3.5% 93.4% 67.9% All Northwoods data 828 90.4% 3.7% 93.7% 63.0% All PDA data 499 89.2% 3.1% 93.0% 77.6% Northwoods 8.5 m 456 88.7% 3.2% 94.0% 90.5% PDA 8.5 m 213 91.0% 3.4% 93.6% 56.2% Combined 8.5 m 669 89.5% 3.4% 93.9% 80.1% Northwoods 16.2 m 319 92.8% 3.1% 93.3% 13.8% PDA 16.2 m 252 87.6% 1.8% 92.5% 99.5% Combined 16.2 m 571 90.5% 3.7% 93.3% 55.7% Northwoods 32 m 46 90.0% 2.9% 92.8% 67.4% PDA 32 m 14 89.4% 1.9% 92.8%* 92.2%* Combined 32 m 60 89.9% 2.7% 92.8%* 73.0%* *These mean e1 values were calculated using an assumed constant impact velocity. The validity of the confidence intervals rests on the assumption that the data are normally distributed. A confidence interval becomes less meaningful as the data deviates farther from a normal distribution. Some data sets have significant skewness or excess kurtosis, which means they are not normally distributed. Table 5-11 shows these statistics. A positive value of excess kurtosis will look like a thinner peak, meaning the distribution has fatter tails than the normal distribution. A negative value of excess kurtosis will look like a broader peak, meaning the distribution has thinner tails than the normal distribution. 83 Table 5-11. ETR relationship to normal distribution Dataset Count Mean difference Skewness Excess Kurtosis All data 1327 3.5% 0.34 0.28 All Northwoods data 828 3.3% -0.08 1.73 All PDA data 499 3.8% 0.71 0.15 Northwoods 8.5 m 456 5.3% -0.13 -0.51 PDA 8.5 m 213 2.6% -0.06 -0.38 Combined 8.5 m 669 4.4% -0.02 -0.33 Northwoods 16.2 m 319 0.5% 0.54 1.52 PDA 16.2 m 252 5.0% 0.79 4.3 Combined 16.2 m 571 2.8% 0.64 0.39 Northwoods 32 m 46 2.6% -0.17 -0.27 PDA 32 m 14 3.4%* 0.22 -0.82 Combined 32 m 60 2.9%* -0.07 -0.16 *These mean differences were calculated using an assumed constant impact velocity. In Table 5-12, the data are analyzed without assuming a normal distribution. This table focuses on medians and percentiles, the final column again showing a two-sided confidence interval comparable to that of the previous table but based on percent rank rather than mean and standard deviation. Percent rank was calculated in Excel using the median e1 as x and each ETR dataset as the array. The “inclusive” formula was used, which sets the least data point in the array as 0.0% and the greatest as 100.0%. The results are not much different from the confidence intervals in the previous table. The percent rank confidence interval for the entire data set is about 1% lower (implying more equity between impact energy and stress wave energy) than the confidence interval that assumes a normal distribution. Again, the confidence intervals lower than 68.3% are shaded in blue even though standard deviation was not used for the calculations. Eq. 15 Percentile Confidence Interval = (𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑟𝑎𝑛𝑘 − 50%) ∗ 2 84 Table 5-12. ETR and e1 statistics: median and 75th percentile Dataset Count Median Median e1 Median diff. Percentile CI All data 1327 89.7% 93.6% 3.9% 69.8% All Northwoods data 828 90.5% 93.6% 3.1% 65.8% All PDA data 499 88.6% 93.1% 4.5% 72.2% Northwoods 8.5 m 456 88.9% 94.1% 5.2% 94.0% PDA 8.5 m 213 91.0% 93.56% 2.6% 12.9% Combined 8.5 m 669 89.5% 94.1% 4.6% 81.4% Northwoods 16.2 m 319 92.5% 93.6% 1.1% 28.8% PDA 16.2 m 252 87.6% 92.6% 5.0% 98.4% Combined 16.2 m 571 89.9% 93.6% 3.7% 59.2% Northwoods 32 m 46 89.8% 93.6%* 3.8%* 81.2% PDA 32 m 14 89.2% 93.6%* 4.3%* >100%* Combined 32 m 60 89.6% 93.6%* 4.0%* 85.6%* *Impact velocities were only collected for 23 blows with the Northwoods device. Therefore, these median drop efficiencies were calculated using an assumed constant impact velocity. The mean difference between ETR and e1 was 3.5% for all the data. Considering only the PDA data, all the ETR and e1 datasets had mean or median differences of less than 5%. The Northwoods device gave the same result, except for the 8.5 m rod, which had a 5.3% mean difference, the mean e1 being 1.67 standard deviations above the mean ETR. From one perspective, these statistics show that measuring impact or stress wave energy is practically the same thing (that there is no significant post-impact energy loss). The definition of practical is necessarily subjective, depending on the goals of each SPT testing program. On the other hand, if the average ETR and e1 of an SPT hammer are known and are roughly normally distributed, their difference can be used to estimate the average ETR without direct measurements. The normal distribution is important because it reduces the influence of random errors. 5.4 Comparison to Other Results: e2 It is necessary to calculate e2 to compare the results to past studies (Eq. 9). Comparison of the stress wave or impact energies is less valuable than e2 because these values are dependent on the 85 SPT hammer in use. Table 5-13 and Table 5-14 show the average e2 for each rod length and device combination. For the 32 m rod, only 23 impact velocity-ETR pairs were used to calculate e2. Impact velocity was not measured on the other 37 out of 60 blows at 32 m. Table 5-13. e2 values for each rod length and device Rod Length (m) Number of Blows μ(e2) σ(e2) CV(e2) All data 1,263 96.4% 4.0% 4.2% 8.5 669 95.3% 3.8% 4.0% 16.2 571 97.3% 4.0% 4.1% 32 23 97.2% 4.1% 4.2% Device Number of Blows μ(e2) σ(e2) CV(e2) Northwoods 798 96.5% 4.3% 4.5% PDA 465 95.8% 3.3% 3.5% Table 5-14. e2 values for each combination of rod length and device Device Rod Length (m) Number of Blows μ(e2) σ(e2) CV(e2) Northwoods 8.5 456 94.4% 3.6% 3.8% PDA 8.5 213 97.2% 3.7% 3.8% Northwoods 16.2 319 99.5% 3.6% 3.6% PDA 16.2 252 94.6% 2.5% 2.6% Northwoods 32 23 97.2% 4.1% 4.2% PDA 32 0 - - - Figure 45 and Figure 46 display histograms of e2. As with the ETR-e1 histograms above, the solid lines represent the equivalent normal distributions. Note that each device has the opposite relationship between e2 and rod length. All the data together are roughly normally distributed, while each device is somewhat skewed in the opposite direction. 86 Figure 45. e2 histograms for each device separately, and all data Figure 46. e2 histograms for each device with separate rod lengths The average e2 for all blows is 96.4%. Daniel (2008) collected a database of 1,017 e2 values with a mean of 96.1% and a standard deviation of 11.4%. Daniel’s database includes safety hammer SPT tests between 1981 and 2003 that used various rod types and stress wave measurement techniques (the oldest tests used the force-squared (F2) method). The standard deviations of e2 of the individual studies within Daniel’s database were much larger than those presented above. A subset of Daniel’s safety hammer dataset had a mean e2 of 99.9% and a standard deviation of 10.2% with 841 blows. This subset includes SPT and LPT tests but excludes the studies that used 87 the F2 method of stress wave measurement (studies A and B in Daniel, 2008) and another biased by an incorrect calculation method (study C). Daniel et al. (2005) found e2 values greater than 97% with rod lengths of 3.7 and 6.4 m, using a safety hammer and a Northwoods stress wave device (200 kHz, 20,000 g, stiff mount). They did not measure impact velocity but used the string cut method, which provides free-fall hammer velocity. Santana et al. (2014) found partially contradictory e2 results. They found an average e2 of 96% in one borehole, and 91% in another, using a Brazilian automatic trip hammer. They found no relationship between rod efficiency and rod length. However, they also used a much older PDA device with piezoelectric accelerometers having a 5,000 g capacity. The newest PDA accelerometers (such as those used in this study) have a 20,000 g capacity. Furthermore, the Brazilian SPT used by Santana et al. (2014) has significant differences from the ASTM standard SPT. An important difference may be the Brazilian drill rods, which have much smaller coupler threads than AW (ABNT, 2001, Portuguese). The e2 results agree closely with Cavalcante et al. (2011), who found an average e2 of 99.9%. Like Santana et al. (2014), Cavalcante et al. (2011) found no relationship between e2 and rod length, and they also used the Brazilian SPT and 5,000 g capacity accelerometers. Table 5-15 summarizes the studies mentioned above. Only studies that used the FV method of stress wave measurement are included. 88 Table 5-15. Comparison of e2 results to literature Reference Hammer type Velocity device # of blows μ(e2) σ(e2) Daniel et al. (2005) Safety None (free-fall) 2 >97.7% * Daniel (2008) Safety HPA (radar) 416 SPT & 425 LPT 99.9% 10.2% Cavalcante et al. (2011) Brazilian Camera (30 fps) 98 99.9% * Santana et al. (2014) Brazilian Camera (1000 fps) 9 96.4% 2.9% Santana et al. (2014) Brazilian Camera (1000 fps) 10 90.6% 3.1% Miller (2022) Automatic Camera (480 fps) 1,263 96.4% 4.0% *Standard deviations were not provided for these studies. When comparing these results, another factor worth considering is the shape of the hammer and anvil. The hammer and anvil's shape have been shown to significantly affect ETR, although this may have been due to the F2 method of stress wave measurement. The hammer, anvil, and rods used to collect the data for this thesis were all manufactured by MARL Technologies but are modelled on standard automatic SPT hammer equipment. MARL's anvil has the same dimensions and material as the CME hammer used by Howie et al. (2003), except that the CME had NWJ threads while MARL's had AW threads. MARL's AW rods have standard AW dimensions. The average e2 values presented above should be extrapolated to other hammer types (e.g. donut or safety) with caution. In North America, the results should be the most widely applicable due to the prevalence of automatic SPT hammers. 5.5 Secondary Discussion of Results 5.5.1 Effect of Initial Sampler Penetration The drill string self-weight was a critical variable affecting the SPT blow count for the testing methodology in this thesis. This effect is probably a distinctive feature of the energy-dissipating media (steel shot) used in this thesis. Discussion of the observed drill string weight effect is included here because of its implications for reproducing this research and future SPT research. It has long been assumed that the drill string's weight has a negligible effect on SPT blow count (Schmertmann and Palacios, 1979). However, the experience gained via the testing conducted for 89 this thesis indicates that the drill string weight affects the test results. The SPT sampler had an interior length of 0.66 m (26 inches), including the length added by the driving shoe. This length leaves room for 0.2 m (8 inches) after completing a 0.45 m (18-inch) penetration. However, the hammer and drill rod’s weight causes some penetration into the test media at the beginning of the test. The pre-penetration was frequently greater than 0.2 m (8 inches) with the 16.2 m drill rod. The following procedure was developed to minimize and control the pre-penetration distance. The drill rod was pulled up and carefully set back down on the test media, and the hammer was set on the anvil as slowly as possible before the test was started. This procedure always resulted in a significantly lower and more consistent blow count. The lowest N counts “achieved” with the 16.2 m rod were 61, 70, 70, and 72. Before this method was employed, penetration refusal was met on 3 out of 7 tests with the 16.2 m rod. Penetration refusal meant more than ten blows per inch of penetration distance. These tests finished with N counts of 88, 85, and 96. Table 5-16 shows the average N value with each rod length, N being the blow count from 0.15 to 0.45 m (6 to 18 inches) penetration distance. The “modified e2 for hammer set” is also presented because of the significant difference in blow count in each test hole. This modified e2 was defined by Daniel (2008) following the observations of Odebrecht et al. (2005). ERhs stands for “hammer set energy ratio” in the following equation. ERhs is the quotient of the permanent set (penetration distance) over hammer drop height. For the SPT, the average ERhs for a complete test is equal to 0.4/N. Accounting for the hammer set only significantly affects the 32 m data. The meaning of this correction is unclear since the number of e2 data points at 32 m is small (23 blows). Eq. 16 𝑒2𝑚 =𝐸𝑇𝑅𝑒1+𝐸𝑅ℎ𝑠 where 𝐸𝑅ℎ𝑠 =0.4𝑁 Table 5-16. Average blow count (N) per test for each rod length Rod length Average N count (150-450mm) Average e2 Modified e2 for hammer set 8.5 m 51 95.3% 94.6% 16.2 m 74 97.3% 96.4% 32 m 9 97.2% 92.4% Brief speculation is offered as to why the blow count depended on how the drill rods were set up. A pair of pipe wrenches were used to tighten the drill rods. The sampler sunk down into the media while the transducer and anvil were installed on the drill rod with pipe wrenches. There was 90 significant jostling, vibration, and inadvertent application of body weight to the wrenches. Lastly, significant pre-penetration occurs if the hammer is hastily set down on the anvil. If the pre-penetration is 0.2 m (8 inches) or more, the penetration per blow will quickly drop to less than 0.1 inches once the sampler is filled with media. Thus, for every extra inch of pre-penetration past 0.2 m (8 inches), the blow count will be increased by at least 10. With the 8.5 m rod, the blow count variance may also depend on the pre-penetration, though the effect would be less since the drill string weighs half as much. At the rod length of 8.5 m, the lowest N count was 38, and the highest was 60. However, the lifting technique was not done at this rod length because the pre-penetration effect had not yet been noticed. The eSPT is not a source for the observed blow count variance because it provides consistent drop heights and impact velocities. During the 8.5 m rod tests, the average impact velocity was 0.5% greater than at the other depths, equivalent to a 1.2% greater impact energy. This greater impact energy might have lowered the blow count. However, the blow count decreased by about 50%, while the impact energy increased by only 1.2%, from 16.2 to 8.5 m. The relationship between ETR and blow count is generally assumed to be linear (see Equation 1 reproduced below). Eq. 1 𝑁60 =𝑁×ETR%60% The pre-penetration and blow count variability should not affect the ETR and e1 data presented above. The blow count may be of interest for future research. Two solutions could be employed to reduce the effect of the drill string weight in this cased test hole system. A longer sampler would reduce the effect of pre-penetration on the blow count. A less uniformly graded media is needed to eliminate the effect of pre-penetration on blow count. See the discussion in the next chapter on future research. 5.5.2 Short Rod Effect The research program was not designed to thoroughly examine the effects of rod length on SPT energy transfer. If it had been, there should have been data with rods shorter than 8.5 m. In one sense, the results seem to confirm the conclusions of Daniel et al. (2005). Daniel observed no significant short rod effect, which made sense given that the theory of short rod losses based on the old F2 equation assumptions had been disproven. When considering only the combined data 91 sets presented above, the difference in average e2 across all rod lengths was much less than the CV. Therefore, it could be said that no rod length effect was observed. However, the PDA and Northwoods devices gave different results. With the PDA device, the average e2 was 2.6% greater with the shortest rod (comparing the 8.5 and 16.2 m rods). In contrast, with the Northwoods device, the average e2 was 5.1% lower with the shortest rod. Therefore, the results presented above are inconclusive regarding the “short rod effect.” Energy may be lost in the SPT system when using rod lengths less than 10 m. It is also possible that the detected losses are due to the instrument’s presence or the inability of accelerometers to detect the high-frequency accelerations produced by short rods. However, the idea is not well-supported because the Northwoods’ (apparently) superior accelerometer detected more losses for the 8.5 m long rods than the PDA. It was observed that the “time to max FVenergy” recorded by the Northwoods device had a non-linear relationship with the length of the rod (see Table 5-17).1 With the 8.5 m drill rod, the ratio of “time to max FVenergy” to 2L/c was 2.4, while this ratio was 1.1 at 16.2 m and 0.8 at 32 m.2 The short rod effect has also been shown to be non-linear and could depend on this ratio. The higher this ratio goes, the more times the hammer energy must reflect from the bottom of the sampler and back up to the top of the hammer and anvil. In the 8.5 m rod, this energy travelled up and down the rods between 2 and 25 times before the max FVenergy was reached. These reflections can cause energy losses and could reduce the ability of the stress wave device to measure the energy as the frequency increases with the increasing amount of reflections, or both. Perhaps this observation will be helpful to someone. Table 5-17. Time to max FVenergy vs. rod length with Northwoods device Rod length Average time to max FV (ms) σ(time to max FV) (ms) 2L/c (ms) (Average time to max FV) ÷ (2L/c) (unitless) 8.5 m 8.1 7.2 3.3 2.4 16.2 m 7.2 0.6 6.3 1.1 32 m 9.7 2.4 12.6 0.8 1 Time to max FV energy is the time at which the FV energy integral has the maximum value (Eq. 3). 2 2L/c: where L is the rod length and c is the velocity of stress wave propagation in steel, 5,123 m/s. 92 5.5.3 Drill Rod Joint Tightness Some effort was made to investigate the effect of loose drill rod joints on hammer efficiency. The results were inconclusive. The intention was to replicate the research done by Howie et al. (2003) on a larger scale (with longer drill rods). They found that loosening one joint resulted in a 2.3% lower ETR. In their study, the rods were less than 4.5 m long and were entirely above ground so that all the joints could be carefully observed during the test. For this thesis, several tests were performed with a 16.2 m drill rod where electrical tape and pipe wrenches were used to ensure that all the joints remained tight, except for one purposely loosened joint (by 180 degrees). Sometimes these loose joint tests produced a lower ETR, but other times, they did not. Despite the effort to ensure that only one joint was loose, other loose joints were found as the drill rod was pulled up after the test. Moreover, the seemingly paradoxical effect of the electrical tape observed in Figure 13 and Figure 41 makes this effort decidedly fruitless. 5.5.4 F-squared Energy Discrepancy The most significant discrepancy between the two stress wave devices was the F2 energies. The F2 method is known to be inaccurate and misleading, but the discrepancy seemed noteworthy. The F2 energy is calculated using only the force transducers (ignoring the velocity), and the integral is only taken to 2L/c rather than to the maximum value as with the FV method (Eq. 17). This difference in F2 energies might indicate a much larger reflection at the bottom of the PDA transducer. Both use an AW coupler, but the shorter PDA transducer subassembly may significantly affect F2. The Northwoods reported about 10-15% greater F2 energies than the PDA. The FV energies reported by the two devices are in relatively good agreement (relative to the variance), with the greatest difference being 5.2%. This result was unexpected because the two force measurement systems are more similar in design and operation than the velocity measurement systems. Eq. 17 𝐹2𝑒𝑛𝑒𝑟𝑔𝑦 = ∫ (𝑓 × 𝑓) 𝑑𝑡2L/c0 93 Table 5-18. Comparison of F-squared and FV energies Device Rod length (m) μ(F2 energy) μ(FVenergy*) FV - F2 Northwoods 8.5 86.7% (σ=3.2) 88.7% 2.0% PDA 8.5 77.4% (σ=3.9) 91.0% 13.6% Northwoods 16.2 95.4% (σ=4.6) 92.8% -2.6% PDA 16.2 83.4% (σ=2.5) 87.6% 4.2% Northwoods 32 96.8% (σ=4.2) 90.0% -6.8% PDA 32 81.1% (σ=1.0) 89.4% 8.3% *FVenergy = ETR 5.6 Summary of Data Interpretation The use of different stress wave devices and rod lengths in this thesis shows how much bias error may be present in the stress wave measurement data. There is no known method for determining the bias of SPT stress wave measurement because a method for establishing an Accepted Reference Value is not available. Daniel (2008) assumes “for the purpose of discussion” that the bias error in the ETR measurement is in the order of 5-10%. In the 16.2 m dataset presented above, the two ETR measurement devices produced an average difference in ETR of 5.2%. There is no way to know whether the true ETR was in the middle or otherwise, whereas the eSPT’s impact velocity measurements were (preliminarily) verified by a common smartphone camera. The Northwoods device should be more accurate than the PDA because it can detect much higher frequency accelerations. However, proving that the Northwoods is more accurate than the PDA is impossible because the bias in ETR measurement is unknown. The CV of drop height measured by the eSPT was 0.85% for 1,242 blows, and the CV of impact velocity was 0.93%, whereas the CV of ETR was 3.9%. These figures indicate that the precision of impact velocity measurement is also much better than ETR. However, precision errors are less problematic than bias because they tend to cancel out as more blows are measured. The conclusions arising from the results presented here depend on the specific goals of the SPT data collection program. For example, if researchers develop new empirical relationships for soil properties, they will likely benefit from using stress wave and impact velocity measurements at all 94 rod lengths. On the other hand, it is difficult to justify the stress wave method's expense, complexity, and uncertainty for routine site investigation if a tool for impact velocity measurement is available. Instead, the e2 value of the hammer should be determined under the careful observation of experts in SPT stress wave measurement. Then impact velocity measurements can be used to estimate ETR in the absence of its direct measurement. Daniel (2008) called this process “the energy transfer ratio approach.” 95 Chapter 6: Suggestions for Future Research This chapter summarizes the most immediate avenues of research suggested by this thesis. Some of these ideas were discussed more fully in the preceding chapters. 6.1 Impact Velocity Tools An important limitation to the impact velocity measurement approach is the lack of tools for measuring SPT hammer impact velocity. Tools other than the eSPT may exist for this purpose. PDI’s HPA has been used many times for SPT research but does not currently appear on PDI’s website (March 2022). The HPA was a commercial radar tool for measuring hammer impact velocity but cannot be used with automatic hammers because the hammer is not visible. There is a need for tools such as the HPA for use in regions where automatic hammers are not yet prevalent, most of the world outside North America. Two closely related ideas were proposed for improvements to the eSPT. A triangulation algorithm could be used to reduce the one-sided bias error of the eSPT’s maximum velocity measurement. A sensor might also be used to record a contact history of the hammer and anvil. If this sensor was synchronized with the hammer velocity sensors, it could be used with the triangulation algorithm to estimate impact velocity with greater precision. This contact sensor might also have other benefits. For example, it would indicate the time and quantity of secondary hammer blows (see Daniel et al., 2005). The simplest method of correcting the known bias error due to the eSPT’s sample rate would be to correct the average impact velocity by one-half the maximum error (+0.9%). 6.2 Use the MARL Test Site for Future Research The test site at MARL has many advantages for future SPT research. The site is already built and intended to be permanent. It does not require drilling because the drilling has already been done. It does not require anyone to climb up a tower or tall building, such as Howie et al. (2003), Daniel et al. (2005), or Odebrecht et al. (2005). However, all SPT testing requires a drill rig with a mast and winches to raise and lower the drill rods. Three winches and a 9 m tall mast are needed to raise 96 and lower the drill rods to use the deepest casing (29.5 m deep). Only two winches are needed for the shorter casings with the same 9 m mast. To fully utilize the test site at MARL in the future, it would be interesting to investigate how to make the 29.5 m casing watertight. This casing was installed un-welded with a thread sealer. However, the thread sealer did not stand up to the groundwater pressure. It is typically filled with water to about 20 m below the ground surface. 6.3 Test With Shorter Drill Rods A limitation of this thesis is that data were only collected for drill rods between 8.5 m and 32 m long. It would be interesting to install a fourth test hole at MARL’s site with a depth of less than 5 m, to expand the applicability of the research. 6.4 Investigate the Effect of Tape on Drill Rod Joints A sudden drop in e2 was shown in Figure 13, Figure 18, and again in Figure 41. This drop occurred immediately after applying electrical tape to all the drill joints. In the PDA data, the ETR seemed to remain lower than the impact energy (e1) for the rest of the tests. In the Northwoods data, the lower ETR might only have persisted for the rest of the tests in the shallow hole (8.5 m rod). This result was counterintuitive, and the opposite result was hoped for. Further testing of drill rods with and without electrical tape could be interesting. However, it is unknown how widespread taped joints are in research and industry. 6.5 Use of Different Media The media could be systematically changed to research the loss of energy through the drill string and the effect of the total mass of the drill string on the energy that reaches the soil. This type of experiment has been done by Brazilian researchers (c.f. Odebrecht et al., 2005), but the Brazilian system seems to have some significant differences from the North American SPT. Using a procedure similar to the Proctor Test, the effect of the drill rod weight might be quantifiable by putting natural soil with a controlled density in the bottom of the cased borehole. Before each test, a standard hammer mass and shape would compact the soil to the same density. 97 The steel shot was a suitable testing medium for this thesis because it provided a high blow count and could be shaken out of the sampler without disassembling the whole drill rod at the longer rod lengths. The steel shot also seemed to cause highly variable blow counts, but the blow count did not influence the primary objectives of this thesis. The relatively uniform particle size and high void ratio of steel shot amplified the effect of the drill string weight on the blow count because the sampler so readily sunk into the media, causing significant pre-penetration (often greater than 0.2 m (8 inches)). If the right combination of soil type, water content, and hand compaction were used instead of the steel shot, the pre-penetration could be minimized. Ceramic proppant could be worth trying instead of steel shot but might not give different results. A longer sampler would also reduce the effect that pre-penetration has on the blow count. If the sampler does not fill up before the test is over, the blow count will be much more consistent, but the pre-penetration distance will still affect the blow count. 98 Chapter 7: Conclusion The data presented in this thesis show that impact velocity measurements should be relied upon to estimate hammer ETR and to calculate N60 using automatic SPT hammers. Impact velocity measurement with the eSPT currently has a known average one-sided bias error of 0.9%, compared to an unknown and unmeasurable bias error in SPT stress wave measurements (cf. Daniel, 2008; ASTM 2016). This bias error in the eSPT’s measurements is not inherent to impact velocity measurement and can be corrected once identified. A switch from stress wave measurement to impact velocity measurement should reduce the cost of obtaining high-quality SPT data. Stress wave measurement may still be desirable for periodically checking that the post-impact energy loss (e2) is consistent. However, these periodic stress wave measurements should be done under the supervision of an experienced engineer capable of identifying erroneous or questionable stress wave data. The new data set presented in this thesis is particularly significant for SPT research in North America because it was collected with an automatic hammer (Daniel (2008) used safety hammers) and with a promising new impact velocity measurement tool (the eSPT). The presented data are expected to be reproducible because of the well-documented and controlled test methodology. The post-impact energy loss ratio (e2) was reasonably constant for the 1,263 SPT automatic hammer blows presented above. The entire dataset has an average e2 of 96.4%, with a roughly normal distribution and a standard deviation of 4.0%. Using this same SPT hammer, instead of ongoing stress wave (ETR) measurement, a correction of 96.4% should be applied to the average measured impact kinetic energy to estimate the ETR. This correction method is the basis of the energy transfer ratio approach from Daniel (2008). This method limits the “plague” of unknown bias error in ETR measurement while also providing a live, ongoing estimation of hammer efficiency. The eSPT was shown to be a robust tool for hammer-anvil impact velocity measurement. Based on the precision required for SPT testing, the impact velocities obtained appear to be precise and reliable. The eSPT delivered drop heights within the ASTM (2018) standard (762 mm ± 30) 100% of the time. The coefficient of variation (CV) of the impact energy was 1.7% for all 1,263 blows over four days of testing. Two ideas were proposed for improvements to the eSPT’s impact 99 velocity measurements. The theoretical basis for a triangulation algorithm was described that would reduce or eliminate the known bias error in the impact velocity measurement due to the sampling rate. Alternatively, the impact velocity measured by the eSPT could be corrected for the average bias error due to the sampling rate. This average bias error is probably half the maximum expected under-estimate due to the sampling rate of 1.9%. The high-speed smartphone camera used to independently calculate hammer impact velocity may be capable of establishing an Accepted Reference Value necessary for determining the total bias error of the eSPT’s measurements. However, better videos are needed that use a surveyor’s target and a slightly more distant perspective to capture more frames before the impact. Currently, impact velocity measurement is rarely done for industrial SPT testing due to a lack of available tools. The eSPT might change this situation, either by direct sales or the inspiration of similar products for impact velocity measurement (considering it is patented). The eSPT was also shown to correctly record the blow count under a wide range of test conditions, with N counts as low as 7 and as high as 96. This feature may also become a significant part of an overall strategy for reducing uncertainty in geotechnical investigation data. A database of 1,264 automatic hammer SPT blows was created during this research, each having measurements of ETR, impact velocity, drop height, and penetration distance per 150 mm. The database is freely available as a spreadsheet for whatever purpose. Perhaps more valuable information can be drawn from it related to rod length, hammer set/penetration distance, or e2. Lastly, the system of cased test holes for SPT research used and described in this thesis holds promise for further use in SPT research. The closed, cased test holes with a removable media basket have distinct advantages over previous methods of obtaining SPT research data. Some ideas have been proposed for future research questions that could be answered at this facility or one similar. Testing could be done with shorter drill rods and different media to investigate the effect of rod type or length on the SPT blow count N. Further testing should be done with non-automatic SPT hammers found in other parts of the world to determine the magnitude and variance of e2 and whether the efficiency of these other SPT systems is affected by rod length. 100 7.1 Limitations A statement on the total bias error of the eSPT’s impact velocity measurement could not be made because the verification methods employed were not sufficiently precise and unbiased themselves. However, such a statement could be made if a more rigorous method were used (in contrast to stress wave measurement). Improvements to the high-speed camera methodology were suggested, but otherwise, a high-speed line-scan camera like the one used by Lee et al. (2014) should also be sufficient for developing an Accepted Reference Value for automatic hammer impact velocity. A slot and tab design like the one used for this thesis would be necessary to video the impact velocity with an automatic hammer. The test holes used in this thesis have some features in common with SPT calibration chambers but are also distinctly different and probably cannot be used for developing empirical correlations related to natural soils. Calibration chambers typically have diameters around ten times greater than the SPT sampler and have an enclosed pressurized lid. The three test holes used in this thesis are intended to be self-similar for assessing the effect of rod length/type on SPT hammer efficiency. The test holes and steel shot media used for data collection in this thesis were appropriate for comparing ETR data across rod lengths but were not appropriate for comparing SPT blow count N values across rod lengths. The blow counts were discussed in section 5.5.1. Comparing N across rod lengths was not necessary to the goals of this thesis but could be interesting for future research. As discussed in Chapter 6, the same test holes might be used with different media to overcome this limitation. The data analyzed in this thesis were only collected with an automatic-type SPT hammer, and therefore the conclusions should be extrapolated to other SPT systems with caution. Similarly, the data analyzed in this thesis were only collected for rod lengths of 8.5, 16.2, and 32 m, and the conclusions may be less applicable to shorter or longer rod lengths. 101 References Abou-Matar, H. & Goble, G.G. (1997). SPT Dynamic Analysis and Measurements. Journal of Geotechnical and Geoenvironmental Engineering. 123(10), 921-928. ASTM International. (2011). D6066-11, Standard practice for determining the normalized penetration resistance of sands for evaluation of liquefaction potential. http://www.astm.org.ezproxy.library.ubc.ca/cgi-bin/resolver.cgi?D6066 ASTM International. (2016). D4633-16, Standard test method for energy measurement for dynamic penetrometers. http://www.astm.org.ezproxy.library.ubc.ca/cgi-bin/resolver.cgi?D4633 ASTM International. (2018). D1586/D1586M-18, Standard test method for Standard Penetration Test (SPT) and split-barrel sampling of soils. https://doi-org.ezproxy.library.ubc.ca/10.1520/D1586_D1586M-18 ASTM International. (2020). E177-20, Standard Practice for Use of the Terms Precision and Bias in ASTM Test Methods. https://compass.astm.org/document/?contentCode=ASTM%7CE0177-20%7Cen-US Biringen, E., & Davie, J. (2008). SPT Automatic Hammer Efficiency Revisited. International Conference on Case Histories in Geotechnical Engineering, USA, 6. Byun, Y.H. & Lee, J.S. (2013). Instrumented Dynamic Cone Penetrometer Corrected with Transferred Energy into a Cone Tip: A Laboratory Study. Geotechnical Testing Journal, 36(4), 1–10, doi:10.1520/GTJ20120115. ISSN 0149-6115. International Organization for Standardization. (2005). Geotechnical investigation and testing — Field testing — Part 3: Standard penetration test (ISO Standard No. 22476-3:2005). https://www.iso.org/standard/36247.html 102 Das, B., & Sivakugan, N. (2016). Fundamentals of geotechnical engineering. Thomson-Engineering. Brazilian Technical Standards Association (ABNT). (2001). NBR 6484, Solo - Sondagens de simples reconhecimento com SPT - Método de ensaio [Soil – SPT Test Method]. Décourt, L. (1989). General report / Discussion Session 2: SPT, CPT, pressuremeter testing and recent developments in in-situ testing – Part 2: the Standard Penetration Test, State-of-the-Art Report. Proceedings of the International Conference on Soil Mechanics and Foundations, Rio de Janeiro, 12(4), 2405-2416. Cavalcante, E. H., Danziger, B. R., & Danziger, F.A.B. (2011). Measurement of drop height and impact velocity in the Brazilian SPT system. Soils and Rocks, 34(3), 207-218. Daniel, C.R., Howie, J.A., Jackson, R.S. & Walker, B.W. (2001). Development of a spreadsheet for modelling SPT stress wave data. Proceedings of the Canadian Geotechnical Conference, 54, 1116-1123. Daniel, C. R., Howie, J., Jackson, R. S., & Walker, B. (2005). Review of Standard Penetration Test short rod corrections. Journal of Geotechnical and Geoenvironmental Engineering, 131(4), 489-497. https://doi-org.ezproxy.library.ubc.ca/10.1061/(ASCE)1090-0241(2005)131:4(489) Daniel, C.R. (2008). Energy transfer and grain size effects during the Standard Penetration test (SPT) and Large Penetration Test (LPT) (T). University of British Columbia. Retrieved from https://open.library.ubc.ca/collections/ubctheses/24/items/1.0063079 Deger, T. T. (2014). Overburden stress normalization and rod length corrections for the Standard Penetration Test (SPT) (T). The University of California Berkley. Hansel, D. (1999). Abrasive cleaning systems. Metal Finishing 97(5) Supplement 1. 103 Howie, J., Daniel, C., Jackson, R. S., Walker, B., & Sy, A. (2003). Comparison of energy measurement methods in the standard penetration test: Final report, appendices I, II, III, and IV. University of British Columbia, Geotechnical Research Group. https://open.library.ubc.ca/cIRcle/collections/facultyresearchandpublications/52383/items/1.0048557 Hughes, S. (2020). eSPT string potentiometer verification. Unpublished internal document, MARL Technologies Inc. Johnsen, L. F., & Jagello, J. J. (2005). Discussion of “Energy efficiency for standard penetration tests” by Odebrecht, E., Schnaid, F., Rocha, M. M., & de Paula Bernardes, G. Journal of Geotechnical and Geoenvironmental Engineering, 133;131;(4), 486-487. https://doi.org/10.1061/(ASCE)1090-0241(2007)133:4(486.2) Kovacs, W. D., Evans, J. C., & Griffith, A. H. (1977). Towards a more standardized SPT. Proceedings of the Ninth International Conference on Soil Mechanics and Foundation Engineering, 2, 269-276. Kovacs, W.D., Salomone, L.A., & Yokel, F.K. (1983). Comparison of Energy Measurements in the Standard Penetration Test Using the Cathead and Rope Method. Report NUREG /CR-3545, U.S. Nuclear Regulatory Commission, Washington, D.C. Lee, C., Lee, J., An, S., & Lee, W. (2010). Effect of secondary impacts on SPT rod energy and sampler penetration. Journal of Geotechnical and Geoenvironmental Engineering, 136(3), 522-526. https://doi-org.ezproxy.library.ubc.ca/10.1061/(ASCE)GT.1943-5606.0000236 Lee, C., Shinwhan, A., & Woojin, L. (2014). Real-time monitoring of SPT donut hammer motion and SPT energy transfer ratio using digital line-scan camera and pile driving 104 analyzer. Acta Geotechnica, 9(6), 959-968. https://doi-org.ezproxy.library.ubc.ca/10.1007/s11440-012-0197-0 Lukiantchuki, J. A., Bernardes, G. P., & Esquivel, E. R. (2017). Energy ratio (ER) for the standard penetration test based on measured field tests. Soils and Rocks, 40(2), 77-91. 10.28927/SR.402077 Morgano, C. M., & Liang, R. Y. (1992). Energy transfer in SPT–rod length effect. Proceedings of the 4th International Conference on the Application of Stress-Wave Theory to Piles. Balkema. 121–127. https://www.pile.com/wp-content/uploads/2017/03/EnergyTransferInSPTRodLengthEffect.pdf Odebrecht, E., Schnaid, F., Rocha, M.M., & Bernardes, G.D.P. (2005). Energy Efficiency for Standard Penetration Tests. Journal of Geotechnical and Geoenvironmental Engineering, 131(10), 1252-1263. Royston, P. (1992). Approximating the Shapiro-Wilk W-test for non-normality. Statistics and Computing, 2(3), 117-119. Santana, C. M., Danziger, F. A. B., & Danziger, B. R. (2013). The measurement of energy reaching the sampler in SPT. Soils and Rocks, 37(3), 243-255. Santana, C. M., Danziger, F. A. B., & Danziger, B. R. (2014). Energy measurement in the Brazilian SPT system, Geotechnical and Geophysical Site Characterization, 4, 921-926. https://doi-org.ezproxy.library.ubc.ca/10.1201/b13251 Schnaid, F., & Houlsby, G. T. (1992). Measurement of the properties of sand in a calibration chamber by the cone pressuremeter test. Géotechnique, 42(4), 587-601. https://doi.org/10.1680/geot.1992.42.4.587 105 Schnaid, F., Odebrecht, E., Rocha, M.M., & Bernardes, G.D.P. (2009). Prediction of soil properties from the concepts of energy transfer in dynamic penetration tests. Journal of Geotechnical and Geoenvironmental Engineering, 135(8), 1092-1100. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000059 Seed, H. B., Tokimatsu, K., Harder, L. F. & Chung, R. M. (1985). Influence of SPT procedures in soil liquefaction resistance evaluations. Journal of Geotechnical Engineering, 111(12), 1425-1445. https://doi-org.ezproxy.library.ubc.ca/10.1061/(ASCE)0733-9410(1985)111:12(1425) Schmertmann, J. H., & Palacios, A. J. (1979). Energy dynamics of SPT. Journal of the Geotechnical Engineering Division, ASCE, 103(GT8), 909-926. https://doi.org/10.1016/0148-9062(80)90085-6 Sy, A., and Campanella, R.G. (1991). An alternative method of measuring SPT energy. Proceedings of International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics, USA, 2, 499-505. Tatone, F., Dawson, E., Hu, J., & Nguyen, D. (2018). Application of SPT rod energy loss to liquefaction evaluation of deep alluvium beneath an earthfill dam. Proceedings of The Geotechnical Earthquake Engineering and Soil Dynamics Conference, USA, 5, 80-93. https://doi.org/10.1061/9780784481455.008 Valiquette, M., Robinson, B., & Borden R. H. (2010). Energy Efficiency and Rod Length Effect in Standard Penetration Test Hammers, Journal of the Transportation Research Record, 2186(1), 47-56. https://doi-org.ezproxy.library.ubc.ca/10.3141/2186-06 106 Tokhi, H. (2012). A practical method for improved implementation of pile driving formula (T). RMIT University, Melbourne. Retrieved from https://researchrepository.rmit.edu.au/esploro/outputs/9921861407801341 Youd, T. L., Idriss, I. M., Andrus, R. D., Arango, I., Castro, G., Christian, J. T., Dobry, R., Liam Finn, W. D., Harder, L. F., Hynes, M. E., Ishihara, K., Koester, J. P., Liao, S. S. C., Marcuson, W. F., Martin, G. R., Mitchell, J. K., Moriwaki, Y., Power, M. S., Robertson, P. K., … Stokoe, K.H.. (2001). Liquefaction resistance of soils: Summary report from the 1996 NCEER and 1998 NCEER NSF workshops on evaluation of liquefaction resistance of soils. Journal of Geotechnical and Geoenvironmental Engineering, 127(10), 817-833. https://doi-org.ezproxy.library.ubc.ca/10.1061/(ASCE)1090-0241(2001)127:10(817) Zaiontz, C. (n.d.). Shapiro-Wilk Expanded Test. Retrieved from: https://www.real-statistics.com/tests-normality-and-symmetry/statistical-tests-normality-symmetry/shapiro-wilk-expanded-test/ 107 Appendices Appendix A: Supplementary Data 108 Figure 47. String potentiometer datasheet, Posital Fraba Figure 48. PDI accelerometer calibration sheet one 109 Figure 49. PDI accelerometer calibration sheet two 110 Figure 50. PDI force calibration sheet 111 Figure 51. Northwoods calibration sheet Appendix B: Tabulated Database The SPT data collected for this thesis are presented in a supplementary spreadsheet. A single automatic SPT hammer was used for all tests, the eSPT from MARL Technologies. e1 and ETR were calculated using the nominal SPT energy (475 J). e2 is also known as post-impact efficiency and is the quotient of stress wave energy (FVenergy) over impact energy. The modified energy 112 transfer ratio (e2m) was calculated using the measured drop height when available, or otherwise the nominal drop height (762 mm). Every data point has the following characteristics: • Hammer mass = 63.5 kg (140 lb) • Rod type = AW (box threaded couplers) • Rod area = 8.0 cm2 (1.24 in2)
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Graduate
Tannant, Dwayne
An investigation of standard penetration test hammer efficiency through measurement of impact velocity and stress wave energy
Text
Dataset
Other