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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Bibliographic Details
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
Description
Summary: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.

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