标题:Nonlinear deformation prediction of tunnel surrounding rock with computational intelligence approaches
作者:He, Peng ;Xu, Fei ;Sun, Shang-qu
作者机构:[He, Peng ;Sun, Shang-qu ] Shandong Provincial Key Laboratory of Civil Engineering Disaster Prevention and Mitigation, Shandong University of Science 更多
通讯作者:Sun, Shangqu
来源:Geomatics, Natural Hazards and Risk
出版年:2020
卷:11
期:1
页码:414-427
DOI:10.1080/19475705.2020.1729254
关键词:computational intelligence; deformation prediction; gaussian process; support vector machine; Tunnel engineering; wavelet neural network
摘要:Deformation of surrounding rock is widely monitored to discover surrounding rock behaviors for purpose of event forecasting. This article aims to present a comparative study on surrounding rock nonlinear deformation prediction using computational intelligence techniques. The Gaussian process (GP), the support vector machine (SVM), and the wavelet neural network (WNN), are analyzed comparatively for predicting the surrounding rock deformation series. Two representative tunnels, the Wangdeng tunnel on Chenglan railway in China and the Ureshino tunnel line I on Nagasaki expressway in Japan, are illustrated. The results prove that the computational intelligence approaches are capable of predicting surrounding rock nonlinear deformation. The GP, on the whole, performs best. The SVM shows better ability than the WNN and GM (1, 1) not only in predicting the settlement and convergence deformation values but also in tracking the trends of surrounding rock deformation curves. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
收录类别:EI;SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079783871&doi=10.1080%2f19475705.2020.1729254&partnerID=40&md5=d91ee42825b4aa88856e75864001b188
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