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A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied...

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Main Authors: Moayedi, Hossein, Mosallanezhad, Mansour, A. Rashid, Ahmad Safuan, Wan Jusoh, Wan Amizah, Muazu, Mohammed Abdullahi
Format: Article
Language:English
Published: Springer 2020
Subjects:
QA75 Electronic computers. Computer science
T Technology (General)
Online Access:http://eprints.uthm.edu.my/5235/1/AJ%202020%20%28105%29.pdf
http://eprints.uthm.edu.my/5235/
https://doi.org/10.1007/s00521-019-04109-9
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spelling my.uthm.eprints.52352022-01-06T07:41:09Z http://eprints.uthm.edu.my/5235/ A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications Moayedi, Hossein Mosallanezhad, Mansour A. Rashid, Ahmad Safuan Wan Jusoh, Wan Amizah Muazu, Mohammed Abdullahi QA75 Electronic computers. Computer science T Technology (General) Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods. Springer 2020 Article PeerReviewed text en http://eprints.uthm.edu.my/5235/1/AJ%202020%20%28105%29.pdf Moayedi, Hossein and Mosallanezhad, Mansour and A. Rashid, Ahmad Safuan and Wan Jusoh, Wan Amizah and Muazu, Mohammed Abdullahi (2020) A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Computing and Applications, 32. pp. 495-518. ISSN 0941-0643 https://doi.org/10.1007/s00521-019-04109-9
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QA75 Electronic computers. Computer science
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
T Technology (General)
Moayedi, Hossein
Mosallanezhad, Mansour
A. Rashid, Ahmad Safuan
Wan Jusoh, Wan Amizah
Muazu, Mohammed Abdullahi
A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
description Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.
format Article
author Moayedi, Hossein
Mosallanezhad, Mansour
A. Rashid, Ahmad Safuan
Wan Jusoh, Wan Amizah
Muazu, Mohammed Abdullahi
author_facet Moayedi, Hossein
Mosallanezhad, Mansour
A. Rashid, Ahmad Safuan
Wan Jusoh, Wan Amizah
Muazu, Mohammed Abdullahi
author_sort Moayedi, Hossein
title A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
title_short A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
title_full A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
title_fullStr A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
title_full_unstemmed A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
title_sort systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications
publisher Springer
publishDate 2020
url http://eprints.uthm.edu.my/5235/1/AJ%202020%20%28105%29.pdf
http://eprints.uthm.edu.my/5235/
https://doi.org/10.1007/s00521-019-04109-9
_version_ 1738581354378952704
score 13.211869

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