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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
Language:	English English
Published:	Multidisciplinary Digital Publishing Institute (MDPI) 2023
Subjects:	TH1000 Systems of building construction
Online Access:	http://irep.iium.edu.my/105210/7/105210_A%20systematic%20review%20of%20machine%20learning%20techniques.pdf http://irep.iium.edu.my/105210/13/105210_A%20systematic%20review%20of%20machine%20learning%20techniques_Scopus.pdf http://irep.iium.edu.my/105210/ https://www.mdpi.com/2071-1050/15/12/9738/pdf?version=1687142722 https://doi.org/10.3390/su15129738
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spelling	my.iium.irep.1052102024-05-17T08:15:13Z http://irep.iium.edu.my/105210/ 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 TH1000 Systems of building construction 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 (MDPI) 2023-06-19 Article PeerReviewed application/pdf en http://irep.iium.edu.my/105210/7/105210_A%20systematic%20review%20of%20machine%20learning%20techniques.pdf application/pdf en http://irep.iium.edu.my/105210/13/105210_A%20systematic%20review%20of%20machine%20learning%20techniques_Scopus.pdf 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). pp. 1-37. E-ISSN 2071-1050 https://www.mdpi.com/2071-1050/15/12/9738/pdf?version=1687142722 https://doi.org/10.3390/su15129738
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English English
topic	TH1000 Systems of building construction
spellingShingle	TH1000 Systems of building construction 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
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
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 (MDPI)
publishDate	2023
url	http://irep.iium.edu.my/105210/7/105210_A%20systematic%20review%20of%20machine%20learning%20techniques.pdf http://irep.iium.edu.my/105210/13/105210_A%20systematic%20review%20of%20machine%20learning%20techniques_Scopus.pdf http://irep.iium.edu.my/105210/ https://www.mdpi.com/2071-1050/15/12/9738/pdf?version=1687142722 https://doi.org/10.3390/su15129738
_version_	1800081759888474112
score	13.211869

A systematic review of machine learning techniques and applications in soil improvement using green materials

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