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...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/146887 |
| _version_ | 1826126796420022272 |
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| author | Zhang, Wengang Zhang, Runhong Wu, Chongzhi Goh, Anthony Teck Chee Lacasse, Suzanne Liu, Zhongqiang Liu, Hanlong |
| 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 |
| author_sort | Zhang, Wengang |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T06:58:12Z |
| format | Journal Article |
| id | ntu-10356/146887 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:58:12Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Engineering::Civil engineering Soft Computing Method Underground Excavations |
| url | https://hdl.handle.net/10356/146887 |
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