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State-of-the-art review of soft computing applications in underground excavations
Institution:1. Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, Chongqing 400045, China;2. National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing University, Chongqing 400045, China;3. School of Civil Engineering, Chongqing University, Chongqing 400045, China;4. School of Civil and Environmental Engineering, Nanyang Technological University, 639798, Singapore;5. Department of Natural Hazards, Norwegian Geotechnical Institute, 0806 Oslo, Norway
Abstract: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.
Keywords:Soft computing method (SCM)  Underground excavations  Wall deformation  Predictive capacity
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