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

作者：Wengang Zhang,Runhong Zhang,Chongzhi Wu,Anthony Teck Chee Goh,Suzanne Lacasse,Zhongqiang Liu,Hanlong Liu

摘要：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.

發文機構：Key Laboratory of New Technology for Construction of Cities in Mountain Area National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas School of Civil Engineering School of Civil and Environmental Engineering Department of Natural Hazards

關鍵詞：Softcomputingmethod(SCM)UndergroundexcavationsWalldeformationPredictivecapacity

分類號： TP3[自動化與計算機技術—計算機科學與技術]