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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...

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Main Authors: Saad, Ahmed Hassan, Nahazanan, Haslinda, Yusuf, Badronnisa, Toha, Siti Fauziah, Alnuaim, Ahmed, El-Mouchi, Ahmed, Elseknidy, Mohamed, Mohammed, Angham Ali
Format: Article
Published: Multidisciplinary Digital Publishing Institute 2023
Online Access:http://psasir.upm.edu.my/id/eprint/106805/
https://www.mdpi.com/2071-1050/15/12/9738
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spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
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.
format Article
author Saad, Ahmed Hassan
Nahazanan, Haslinda
Yusuf, Badronnisa
Toha, Siti Fauziah
Alnuaim, Ahmed
El-Mouchi, Ahmed
Elseknidy, Mohamed
Mohammed, Angham Ali
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
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
title 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_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_sort systematic review of machine learning techniques and applications in soil improvement using green materials
publisher Multidisciplinary Digital Publishing Institute
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/106805/
https://www.mdpi.com/2071-1050/15/12/9738
_version_ 1809142958459453440
score 13.211869

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