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A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials Saad, Ahmed Hassan; Nahazanan, Haslinda; Yusuf, Badronnisa; Toha, Siti Fauziah; Alnuaim, Ahmed; El-Mouchi, Ahmed; Elseknidy, Mohamed; Mohammed, Angham Ali
Abstract
According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques.
Item Metadata
Title |
A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials
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Creator | |
Publisher |
Multidisciplinary Digital Publishing Institute
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Date Issued |
2023-06-19
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Description |
According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques.
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Subject | |
Genre | |
Type | |
Language |
eng
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Date Available |
2023-09-07
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Provider |
Vancouver : University of British Columbia Library
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Rights |
CC BY 4.0
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DOI |
10.14288/1.0435773
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URI | |
Affiliation | |
Citation |
Sustainability 15 (12): 9738 (2023)
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Publisher DOI |
10.3390/su15129738
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Peer Review Status |
Reviewed
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Scholarly Level |
Faculty; Researcher; Other
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
CC BY 4.0