State-of-the-art review of soft computing applications in underground excavations

State-of-the-art review of soft computing applications in underground excavations

Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft comp...

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Main Authors: Zhang, Wengang, Zhang, Runhong, Wu, Chongzhi, Goh, Anthony Teck Chee, Lacasse, Suzanne, Liu, Zhongqiang, Liu, Hanlong
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2021
Subjects:
Engineering::Civil engineering
Soft Computing Method
Underground Excavations
Online Access:https://hdl.handle.net/10356/146887
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1468872021-03-12T06:41:36Z State-of-the-art review of soft computing applications in underground excavations Zhang, Wengang Zhang, Runhong Wu, Chongzhi Goh, Anthony Teck Chee Lacasse, Suzanne Liu, Zhongqiang Liu, Hanlong School of Civil and Environmental Engineering Engineering::Civil engineering Soft Computing Method Underground Excavations Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations. A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available. Published version 2021-03-12T06:41:35Z 2021-03-12T06:41:35Z 2019 Journal Article Zhang, W., Zhang, R., Wu, C., Goh, A. T. C., Lacasse, S., Liu, Z. & Liu, H. (2019). State-of-the-art review of soft computing applications in underground excavations. Geoscience Frontiers, 11(4), 1095-1106. https://dx.doi.org/10.1016/j.gsf.2019.12.003 1674-9871 https://hdl.handle.net/10356/146887 10.1016/j.gsf.2019.12.003 2-s2.0-85077982998 4 11 1095 1106 en Geoscience Frontiers © 2019 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Civil engineering
Soft Computing Method
Underground Excavations
spellingShingle Engineering::Civil engineering
Soft Computing Method
Underground Excavations
Zhang, Wengang
Zhang, Runhong
Wu, Chongzhi
Goh, Anthony Teck Chee
Lacasse, Suzanne
Liu, Zhongqiang
Liu, Hanlong
State-of-the-art review of soft computing applications in underground excavations
description Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations. A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Zhang, Wengang
Zhang, Runhong
Wu, Chongzhi
Goh, Anthony Teck Chee
Lacasse, Suzanne
Liu, Zhongqiang
Liu, Hanlong
format Article
author Zhang, Wengang
Zhang, Runhong
Wu, Chongzhi
Goh, Anthony Teck Chee
Lacasse, Suzanne
Liu, Zhongqiang
Liu, Hanlong
author_sort Zhang, Wengang
title State-of-the-art review of soft computing applications in underground excavations
title_short State-of-the-art review of soft computing applications in underground excavations
title_full State-of-the-art review of soft computing applications in underground excavations
title_fullStr State-of-the-art review of soft computing applications in underground excavations
title_full_unstemmed State-of-the-art review of soft computing applications in underground excavations
title_sort state-of-the-art review of soft computing applications in underground excavations
publishDate 2021
url https://hdl.handle.net/10356/146887
_version_ 1695706218544234496

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