International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP) (12th : 2015)

A multi hazard risk assessment methodology accounting for cascading hazard events Choine, Mairéad Ní; O’Connor, Alan; Gehl, Pierre; D’Ayala, Dina; García-Fernández, Mariano; Jiménez, María-José; Gavin, Kenneth; Van Gelder, Pieter; Salceda, Teresa; Power, Richard Jul 31, 2015

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12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  1 A Multi Hazard Risk Assessment Methodology Accounting for Cascading Hazard Events  Mairéad Ní Choine Research Engineer, Roughan O’Donovan Innovative Solutions, Dublin, Ireland. Alan O’Connor Director, Roughan O’Donovan Innovative Solutions, Dublin, Ireland. Pierre Gehl Researcher, University College London, London, UK Dina D’Ayala Reader, University College London, London, UK. Mariano García-Fernández Senior Researcher, Institute of Geosciences (CSIC-UCM), Madrid, Spain.  María-José Jiménez Senior Researcher, Institute of Geosciences (CSIC-UCM), Madrid, Spain.  Kenneth Gavin,  Director, Gavin Doherty Geo Solutions, Dublin, Ireland. Pieter Van Gelder Director, Probabilistic Solutions Consult and Training, The Hague, Netherlands. Teresa Salceda Technical Directorate Engineer, DRAGADOS SA, Madrid, Spain Richard Power Corporate Secretary, Roughan O’Donovan Innovative Solutions, Dublin, Ireland.  ABSTRACT: The INFRARISK project is developing reliable stress tests on European Critical Infrastructure using integrated tools for decision-support. This aims to achieve higher infrastructure network resilience to rare and low probability extreme events. As part of the project, a hazard assessment methodology is developed to account for extreme natural hazards with cascading effects. Often hazard scenarios arising from cascading effects lead to disastrous consequences because such hazards are not prepared for. In particular, this paper focuses on the cascading hazard scenario involving earthquake triggered landslides. Traditional risk analysis considers each risk source as independent from the others. As a consequence, values for risk are usually defined regardless of interactions among the multiple risks present in a region. The current approach accounts for interaction between the two hazards in such a way that the probabilities of occurrence can be aggregated as part of an overall risk assessment methodology. The methodology is then demonstrated on a virtual road network case study as a proof of concept.   12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  2 Throughout history, natural hazards have caused devastation to many communities throughout the world. The severity of the consequences of a natural hazard event is largely dependent on the level of preparedness and resilience of society for such events. It is of particular importance that the infrastructure network of a society remains functioning in the aftermath of a natural hazard event, to allow for the movement of emergency services. To this end, the INFRARISK project aims to achieve higher infrastructure network resilience to natural hazards through the development of reliable stress tests using integrated tools for decision support.  The dependency between hazards can greatly increase the severity of their consequences. For example, during the Wenchuan earthquake in China in 2008, approximately 25,000 lives were lost due to earthquake-induced landslides (Zhang, 2014). Traditionally, a multi hazard risk analysis considers each risk source as independent from the others. As a consequence, values for risk are usually defined regardless of interactions among the multiple risks present in a region. This may lead to unrealistic estimates of risk; therefore, a method for assessing multi hazard risk accounting for interaction between hazards is necessary.  The INFRARISK project proposes a multi-hazard assessment methodology that considers both cascading events and co-occurrences of interacting main hazards. This methodology is outlined in D’Ayala et al. (2014). Figure 1 illustrates the hazards considered within the INFRARISK project. This paper focuses on the highlighted branch of Figure 1, which involves a hazard assessment of a region vulnerable to both earthquake and landslide hazards. In this case, the earthquake ground shaking can trigger - that is, cascade into - landslides, thus causing further damage to an infrastructure network. As part of this methodology, it is necessary to firstly assess the earthquake hazard and then assess the landslide hazard, dependent on the earthquake hazard. The earthquake and landslide hazard assessments are linked to each other through the earthquake intensity, which in this case is the pinch point variable. Note that the here proposed methodology is modular in nature, in that the hazard assessment methodology of the triggering earthquake can be changed without affecting the hazard assessment methodology of the subsequent landslide. Only the earthquake triggered landslide hazard assessment methodology is described in detail in this paper. A detailed description of the triggering earthquake hazard assessment methodology, as proposed for the INFRARISK project can be found in D’Ayala et al. (2014).  The paper is divided into three parts. The first part provides a general overview of a multi hazard risk analysis. The second part of the paper presents the earthquake triggered landslide hazard assessment methodology. The third part provides a simple proof of concept example, so that the method described can be demonstrated on a simple infrastructure network.  1. RISK ASSESSMENT METHODOLOGY According to D’Ayala et al. (2014), in order to carry out a risk assessment, it is necessary to a) estimate the hazard at the site of the infrastructure component and b) identify or generate fragility curves for the infrastructure component and the hazard considered. Fragility curves provide the probability of the infrastructure component exceeding a given damage state for a given hazard level.  D’Ayala et al. (2014) presented an overall risk assessment methodology accounting for multi-hazards. In this overall methodology, it is first necessary to select the relevant hazard source events. It is then necessary to specify the possible intensity levels of these source events; noting that these intensity levels may vary at the different vulnerable sites under consideration. Using fragility curves and the potential intensity levels at the site, it is possible to sample the potential damage states of the non-site infrastructure components. If the relevant fragility functions are not available for the given combination of hazard types and structural 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  3 components, the hazard at the site is required to construct these fragility functions using an analytical approach. This can be done efficiently by tailoring the fragility study to the specific response of the infrastructure element to the level of intensity considered (D'Ayala, et al., 2014). According to D’Ayala et al. (2014), for a given main hazard event, the computation of the distributed intensity measures serves the purpose of feeding the fragility functions for the estimation of damage at the vulnerable sites. However, if interactions between main and secondary hazards are assumed, then the main event intensity measures are also used to generate the initial condition (i.e. sources variables) for the triggering of the secondary event.  The resulting distributed intensity measures of this secondary hazard event are then fed into its corresponding selected/ derived fragility functions. This process may iterate to tertiary, and higher order cascading hazard events. This paper discusses a multi hazard case study in which an earthquake is the main hazard event and the earthquake intensity measures serve as inputs for the hazard assessment of the secondary hazard event of an earthquake-triggered landslide.  2. EARTHQUAKE TRIGGERED LANDSLIDE HAZARD ASSESSMENT 2.1. Background The definition of landslide considered in the INFRRISK project includes both slow and fast moving debris flows, rockfalls and permanent ground deformations resulting from earthquake ground movement (Courture, 2011). These events occur throughout the world, under all climatic conditions and terrains, cost billions in monetary losses, and are responsible for thousands of deaths and injuries each year (Highland & Bobrowsky, 2008). Landslide hazard assessment has played an important role in developing land utilization regulations aimed at minimizing the loss of lives and damage to property (Motamedi & Liang, 2013). Furthermore, it can be used in order to prioritize risk mitigation measures and retrofit strategies. Varnes (1984) defined landslide hazard as the probability of occurrence of a potentially destructive landslide within a Figure 1 Preliminary presentation of the different hazard types considered in the INFRARISK framework (D'Ayala, et al., 2014) 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  4 specified period of time and within a given geographical area. Traditionally, landslide hazard assessment is carried out independent of the source hazard. As a result, values of risk are usually assigned independent of the interaction between the source and the secondary hazard. This may lead to unrealistic estimates and therefore it is necessary to predict the occurrence of landslides accounting for the seismicity in the region.   The earthquake-triggered landslides considered as part of this study involve the development of a planar failure surface parallel to the slope surface (See Figure 3). If the depth of the failure surface, t is small in relation to the length of the failure surfaces and infinite slope analyses may be assumed for simplicity. This type of landslide can affect a road section in two ways, depending on whether the slide happens above or below the road section. If the slide happens above the road section, the displaced ground could block the roadway. An example of this occurred following the Niigata-Cheutsu Oki, Japan Earthquake in 2007 where a shallow translational landslide occurred on a steep natural slope along the road connecting the towns of Kashiwaza and Kariwa. The landslide blocked a major road and prevented access, Figure 2 (Saygili, 2008; Kayen, et al., 2007). If the slide happens below the road section, the road surface may become damaged.    Figure 2 Landslide following the Niigata-Cheutsu Oki, Japan Earthquake 2007 (Kayen, et al., 2007) 2.2. Methodology According to Rathje et al. (2013) the seismic performance of slopes is typically evaluated based on the sliding displacement predicted to occur along a critical sliding surface. Rathje et al. (2013) define this displacement as the cumulative downslope movement of a sliding mass due to earthquake shaking, measured as Permanent Ground Deformation (PGD). The rigid sliding block approach, first proposed by Newmark (1965), is adopted in numerous studies to estimate the yield acceleration of a slope (Jibson, et al., 2000; McCrink, 2001; Saygili & Rathje, 2009). The yield acceleration of a sliding block represents the horizontal accelation that results in a factor of safety equal to 1.0 for the slope and this acceleration level initiates sliding (Saygili & Rathje, 2009). The yield acceleration can be derived using an infinite slope model, as shown in Figure 3, and is a function of multiple slope characteristics (Saygili & Rathje, 2009). Earthquake induced displacements are expected if the Peak Ground Acceleration (PGA) of the earthquake exceeds the yield acceleration.   Figure 3 Infinite slope representation used to define ky (Saygili & Rathje, 2009)  In order to carry out an earthquake induced landslide hazard analysis, information is required about the yield acceleration of the relevant slopes and the seismic hazard in the region. Using the infinite slope model shown in Figure 3, ky can be explicitly described as (Saygili & Rathje, 2009): 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  5   tan1'tan1FS gk y                                             (1) (1)  tan'tantan'tansin'FS mtc w              (2)  where ky is the yield acceleration in the horizontal direction in units of g (g = acceleration due to gravity), FS is the static factor of safety, α is the slope angle, φ’ is the effective internal friction angle of the soil, c’ is the effective cohesion of the soil, γ is the unit weight of the soil, t is the failure surface depth normal to the slope, γw is the unit weight of water and m is the percentage of failure thickness that is saturated (i.e. saturation ratio).  is the shear resistance mobilized on the failure surface which is controlled by , the effective normal stress. Values for ky should be estimated across the study area at suitable intervals producing a landslide susceptibility map. The expected level of shaking, taken from the results of an earthquake hazard assessment, must be accounted for to carry out a full hazard analysis. Numerous empirical models to calculate the sliding displacement of a rigid sliding mass have been developed in the literature. Saygili and Rathje (2008) proposed two empirical models developed from displacements computed using over 2,000 recorded motions from the Next Generation Attenuation (NGA) database (Rathje, et al., 2013). The first model presented here uses a single ground motion parameter (PGA) and the earthquake magnitude (M) to predict the sliding displacement and is therefore referred to as a scalar model (Equation 3 & 4). Equation (3) provides the mean value of displacement and Equation (4) provides the standard deviation. This second model uses two ground motion parameters (PGA, PGV) and is therefore referred to as a vector model (Equation 5 & 6). Equation (5) provides the mean value of displacement and Equation (6) provides the standard deviation. These models predict displacement in cm and assume a lognormal distribution for displacement (Rathje, et al., 2013).    6MPGAlnPGAPGAPGAPGAln7645342321aakakakakaaDyyyy   (3)   2MPGA,ln PGA54.0PGA79.073.0  yyDkk  (4)  PGVlnPGAlnPGAPGAPGAPGAln7645342321aakakakakaaDyyyy    (5)     PGA52.041.0PGVPGA,lnyDk                    (6) Saygili & Rathje (2009) note that the most efficient models aim to minimise the standard deviation. The vector model proposed in Saygili and Rathje (2008) provides lower standard deviations. Therefore, the vector model is adopted in the current anlysis. In order to carry out a landslide risk assessment; an estimate of road damage is required for a given PGD. As mentioned previously, fragility curves are a useful tool that provide the probability of a given damage state occurring for a given intensity measure. PGA or PGD can be used as the intensity measure to estimate road damage, however, to remain consistent with the source event, PGA is recommended.  3. APPLICATION The earthquake-triggered landslide hazard model presented in the previous section is applied to a simple proof of concept example road network. The example network shown in Figure 4 consists of 3 bridges and three road segments. In the current framework, landslide damage to road 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  6 segments built along a slope is considered. Landslide damage to bridges or other components of the network is not considered due to the fact that fragility curves for these components are not available in the literature. Therefore, for the purpose of the current earthquake-induced landslide risk assessment, only the road section along the slope shown between B2 and B3 is considered. For this example, the road segment along the slope is considered as one ‘unit’ with homogenous slope, soil and ground motion parameters. In reality, various sections of the same embankment or cutting might have substantially different probabilities of failure depending on their slope angle, variability of soil parameters etc. Therefore, a real road network should be divided into homogenous units to which slope, soil and ground motion parameters can be applied.     Figure 4 Layout of proof of concept example (D'Ayala, et al., 2014)  Table 1 Soil Properties Parameter Value Effective Cohesion (c’) 0 kN/m2 Internal Friction Angle (φ’) 30o Unit weight (w) 19 kN/m3 Failure surface thickness (t) 1 m Saturation ratio (m) 0.2  Using Equations 3-6, the lnD value and associated standard deviation can be calculated. Using an example from the literature (Rathje & Saygili, 2009), the PGA value was taken as 0.33g, the earthquake magnitude, M, was taken as 7 and the PGV value was taken as 30cm/s. Figure 5 illustrates the effect of ky on the sliding displacement (D), for both the scalar model and the vector model. From the figure it can be seen that sliding displacement increases with a decrease in the yield acceleration.  As mentioned previously, the vector model is chosen in the current study due to the fact that it produces lower values for standard deviation. Figure 6 illustrates the effect of the slope angle on the sliding displacement for the soil properties used.  Figure 5 Effect of ky on Sliding displacement  Figure 6 Effect of slope angle on sliding displacement  (m = 0.2)  For the current analysis, the slope angle was taken as 20o. The effect of rainfall on earthquake induced landslides may be shown by the saturation ratio. This value is taken as 0.2 in the 0.05 0.1 0.15 0.2 0.25ky (g)D (cm)  10-1102101100Scalar Model (M PGA)Vector Model (PGA PGV)20 22 24 26 28 3010100150D (cm)Slope angle (degrees)12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  7 current case study. However, it is clear from Figure 7 that an increase in the saturation ratio leads to an increase in the sliding displacement. Considering the current case study with a saturation ratio of 0.2 and a slope angle of 20o, the vector model provides an lnD value of 2.86. This is considered as the mean value of the lognormal distribution. Taking the exponential function of this value leads to a median value of 17.45 cm. The standard deviation of this function, given by Equation (6), is calculated as 0.5173 cm. The Cumulative Distribution Function (CDF) of the sliding displacement is illustrated in Figure 8.  Figure 7 Effect of saturation on sliding displacement (slope = 20o)   Figure 8 CDF of sliding displacement  Existing fragility functions from HAZUS (NIBS, 2004) are used to select a level of landslide displacement indicative of damage rates for the current case study. These are the only fragility curves available in the literature for estimating the vulnerability of roads due to landslides (Safeland, 2011). However, they have shown a realistic assessment of the expected damage level in most cases (Azevedo, et al., 2010). The median displacement levels considered to constitute slight, moderate and extensive damage on major roads (highways) are 30cm, 60cm, and 150cm, respectively. Using the CDF in Figure 8, the probability associated with each displacement can be quantified at the given PGA level:   P (D > 30 | PGA = 0.33g) = 0.1477  P (D > 60 | PGA = 0.33g) = 0.0085  P (D > 150 | PGA = 0.33g) = 1.6 x 10-5 4. CONCLUSION This paper outlines the multi hazard assessment framework developed as part of the INFRARISK project. Traditionally, a multi hazard risk analysis considers each risk source as independent from the others. As a result, values for risk are usually defined regardless of interactions among the multiple risks present in a region. This may lead to unrealistic estimates of risk; therefore, a method for assessing multi hazard risk accounting for interaction between hazards is necessary.  In order to carry out an appropriate multi hazard risk assessment, the interaction between the source event and the triggered events, shown in Figure 1, must be considered. This is a difficult task due to the lack of available models in the literature; however, an example of an earthquake triggered landslide hazard model is outlined in this paper and applied to a simple proof of concept example. An important feature of this model is that the earthquake and landslide hazard assessment models are essentially independent, yet linked through a pinch point variable, in this case the earthquake intensity. This means that the individual hazard assessment models can be amended independently of the others. 0 0.2 0.4 0.6 0.8110100150D (cm)Saturation ratio0 20 40 60 8000.20.40.60.81D (cm)Probability12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP12 Vancouver, Canada, July 12-15, 2015  8 5. ACKNOWLEDGEMENTS This project has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement No 603960. 6. REFERENCES Azevedo, D. et al., 2010. Siesmic vulnerability of lifelines in the greater Lisbon are. Bulletin of Earthquake Engineering, Volume 8, pp. 157-180. Courture, R., 2011. Landslide Terminology - National Technical Guidelines and Best Practices on Landslides, s.l.: Geological Survey of Canada. D'Ayala, D. et al., 2014. Hazard Distribution Matrix, INFRARISK D 3.1 Report, s.l.: s.n. Highland, L. M. & Bobrowsky, P., 2008. The Landslide Handbook - A Guide to Understanding Landslides, s.l.: United States Geological Survey. Jibson, R. W., Harp, E. L. & Micheal, J. A., 2000. A method for producing digital probabilistic seismic landslide hazard maps. Engineering geology, Volume 28, pp. 271-289. Kayen, R. et al., 2007. Preliminary Observations on the Niigata-Cheutsu Oki, Japan Earthquake on July 16 2007, s.l.: EERI-GEER Web Report 2007-1 v.8. McCrink, T. P., 2001. Mapping earthquake-induced landslide hazards in Santa Cruz County. In: H. Ferriz & R. Anderson, eds. Engineering geology practice in northern California: California Geological Survey Bulletin 210. s.l.:s.n., pp. 77-94. Motamedi, M. & Liang, R. Y., 2013. Probabilistic landslide hazard assessment using Copula modeling technique. Landslides, pp. 1-9. NIBS, N. I. o. B. S., 2004. HAZUS-MH: User's manual and technical manuals, Washington D.C: Federal Emergency Management Agency. Rathje, E. M. et al., 2013. Probabilisitic assessment of the seismic performance of earth slopes. Bull Earthquake Eng, 12(3), pp. 1071-1090. Safeland, 2011. D2.5 Physical vulnerability of elements at risk to landslides: Methodology for evaluation, fragility curves and damage states for buildings and lifelines, s.l.: European Union Seventh Framework Programme. Saygili, G., 2008. A Probabilistic Approach for Evaluating Earthquake-Induced Landslides, PhD Thesis, s.l.: Universtiy of Texas at Austin. Saygili, G. & Rathje, E. M., 2008. Empirical predictive models for earthquake-iduced sliding displacements of slopes. J Geotech Geoenviron Eng, 134(6), pp. 790-803. Saygili, G. & Rathje, E. M., 2009. Probabilistically based seismic landslide hazards maps: An application in Southern California. Engineering Geology, 109(3-4), pp. 183-194. Varnes, D. J., 1984. Landslide hazard zonation: A review of principles and practice., Paris: Daramtiere. Zhang, S., 2014. Assessment of human risks posed by cascading landslides in the Wenchuan earthquake area, s.l.: s.n.   

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