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Predicting tunnel convergence using Multivariate Adaptive Regression Spline and Artificial Neural Network

文献类型：期刊论文

作者Adoko, Amoussou-Coffi1; Jiao, Yu-Yong1; Wu, Li2; Wang, Hao1; Wang, Zi-Hao1
刊名TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
出版日期2013
卷号38页码:368-376
关键词Tunnel convergence New Austrian Tunneling Method (NATM) Multivariate Adaptive Regression Spline Artificial Neural Network Predictive model
ISSN号0886-7798
DOI10.1016/j.tust.2013.07.023
英文摘要Determining the tunnel convergence is an indispensable task in tunneling, especially when adopting the New Austrian Tunneling Method. The interpretation of the monitoring allows adjusting the construction methods in order to achieve more effective tunneling conditions and to avoid problems like rock collapse, trapping and jamming of boring machine, delay of the project or even geological disasters. In this research, a model capable of predicting the diameter convergence of a high-speed railway tunnel in weak rock was established based on two approaches: Multivariate Adaptive Regression Spline (MARS) and Artificial Neural Network (ANN). A tunnel construction project located in Hunan province (China) was used as case study. The input parameters included the class index of the surrounding rock mass, angle of internal friction, cohesion, Young's modulus, rock density, tunnel overburden, distance between the monitoring station and the tunnel heading face and the elapsed monitoring time. The performance of the models was evaluated by comparing the predicted convergence to the measured data using several performance indices. Overall, the results showed high accuracy of the model predictability of tunnel convergence with MARS showing a light lesser accuracy. However, MARS was more flexible and computationally efficient. It is concluded that MARS can constitute a reliable alternative to ANN in modeling nonlinear geo-engineering problem such as the tunnel convergence. (C) 2013 Elsevier Ltd. All rights reserved.
WOS研究方向Construction & Building Technology ; Engineering
语种英语
WOS记录号WOS:000328234300035
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.198/handle/2S6PX9GI/3503]  
专题岩土力学所知识全产出_期刊论文
国家重点实验室知识产出_期刊论文
作者单位1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn ;
2.China Univ Geosci, Fac Engn
推荐引用方式
GB/T 7714		 Adoko, Amoussou-Coffi,Jiao, Yu-Yong,Wu, Li,et al. Predicting tunnel convergence using Multivariate Adaptive Regression Spline and Artificial Neural Network[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2013,38:368-376.
APA Adoko, Amoussou-Coffi,Jiao, Yu-Yong,Wu, Li,Wang, Hao,&Wang, Zi-Hao.(2013).Predicting tunnel convergence using Multivariate Adaptive Regression Spline and Artificial Neural Network.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,38,368-376.
MLA Adoko, Amoussou-Coffi,et al."Predicting tunnel convergence using Multivariate Adaptive Regression Spline and Artificial Neural Network".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 38(2013):368-376.

入库方式： OAI收割

来源：武汉岩土力学研究所

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