A systematic review of machine learning techniques and applications in soil improvement using green materials
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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2023
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106805/ |
| _version_ | 1848864827389247488 |
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| author | Saad, Ahmed Hassan Nahazanan, Haslinda Yusuf, Badronnisa Toha, Siti Fauziah Alnuaim, Ahmed El-Mouchi, Ahmed Elseknidy, Mohamed Mohammed, Angham Ali |
| author_facet | Saad, Ahmed Hassan Nahazanan, Haslinda Yusuf, Badronnisa Toha, Siti Fauziah Alnuaim, Ahmed El-Mouchi, Ahmed Elseknidy, Mohamed Mohammed, Angham Ali |
| author_sort | Saad, Ahmed Hassan |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T13:55:00Z |
| format | Article |
| id | upm-106805 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:55:00Z |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1068052024-08-16T08:09:03Z http://psasir.upm.edu.my/id/eprint/106805/ 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 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. Multidisciplinary Digital Publishing Institute 2023-06-19 Article PeerReviewed Saad, Ahmed Hassan and Nahazanan, Haslinda and Yusuf, Badronnisa and Toha, Siti Fauziah and Alnuaim, Ahmed and El-Mouchi, Ahmed and Elseknidy, Mohamed and Mohammed, Angham Ali (2023) A systematic review of machine learning techniques and applications in soil improvement using green materials. Sustainability, 15 (12). art. no. 9738. pp. 1-37. ISSN 2071-1050 https://www.mdpi.com/2071-1050/15/12/9738 10.3390/su15129738 |
| spellingShingle | Saad, Ahmed Hassan Nahazanan, Haslinda Yusuf, Badronnisa Toha, Siti Fauziah Alnuaim, Ahmed El-Mouchi, Ahmed Elseknidy, Mohamed Mohammed, Angham Ali A systematic review of machine learning techniques and applications in soil improvement using green materials |
| title | A systematic review of machine learning techniques and applications in soil improvement using green materials |
| title_full | A systematic review of machine learning techniques and applications in soil improvement using green materials |
| title_fullStr | A systematic review of machine learning techniques and applications in soil improvement using green materials |
| title_full_unstemmed | A systematic review of machine learning techniques and applications in soil improvement using green materials |
| title_short | A systematic review of machine learning techniques and applications in soil improvement using green materials |
| title_sort | systematic review of machine learning techniques and applications in soil improvement using green materials |
| url | http://psasir.upm.edu.my/id/eprint/106805/ http://psasir.upm.edu.my/id/eprint/106805/ http://psasir.upm.edu.my/id/eprint/106805/ |