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Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling

文献类型：期刊论文

作者Jiang, Quan3; Yao, Pin-Pin2; Chen, Dong-Fang1; Feng, Xia-Ting3; Xu, Ding-Ping3; Yang, Cheng-Xiang1
刊名TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
出版日期2016
卷号51页码:372-386
关键词Tunnel collapse Layered rock strata Artificial neural network Genetic algorithm
ISSN号0886-7798
DOI10.1016/j.tust.2015.09.010
英文摘要Numerous collapses have occurred during the excavation of diversion tunnels in the thin and extremely thin layered rock strata at Wudongde Hydropower Station in China. Hence, a reliable method is required to predict the risk and the depth of collapse. However, both theory and practice indicate that one single criterion methods cannot satisfactorily predict the collapse depth accurately. In this study, using an artificial neural network (ANN), an intelligent prediction method has been investigated. Through theoretical and statistical analyses, six input parameters (i.e., cover depth, minor-major principal stress ratio, geological strength index, excavation method, support strength and attitude of rock), have been selected and used in the model. Obtained from three diversion tunnels at Wudongde Hydropower Station, forty-five learning samples and six testing samples were used in the training of the model. The structural parameters and the initial weights of the ANN have been optimized by a genetic algorithm (GA). The trained model was then used to predict the collapse depth of another six excavation sites. The predictions show good agreement with the measurements at the sites. The absolute errors between the predicted and the measured collapse depths are all less than 0.35 m, and the relative errors are all less than 15%. Application of the improved ANN method to the tunnel collapse analysis at Wudongde Hydropower Station confirms its effectiveness in predicting collapse depth during tunnelling. (C) 2015 Elsevier Ltd. All rights reserved.
WOS研究方向Construction & Building Technology ; Engineering
语种英语
WOS记录号WOS:000367493200036
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.198/handle/2S6PX9GI/3798]  
专题岩土力学所知识全产出_期刊论文
岩土力学所知识全产出
作者单位1.Northeastern Univ, Key Lab, Minist Educ Safe Min Deep Met Mines ;
2.China Three Gorges Corp,
3.Chinese Acad Sci, Inst Rock & Soil Mech ;
推荐引用方式
GB/T 7714		 Jiang, Quan,Yao, Pin-Pin,Chen, Dong-Fang,et al. Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling[J]. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,2016,51:372-386.
APA Jiang, Quan,Yao, Pin-Pin,Chen, Dong-Fang,Feng, Xia-Ting,Xu, Ding-Ping,&Yang, Cheng-Xiang.(2016).Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling.TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY,51,372-386.
MLA Jiang, Quan,et al."Use of an improved ANN model to predict collapse depth of thin and extremely thin layered rock strata during tunnelling".TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 51(2016):372-386.

入库方式： OAI收割

来源：武汉岩土力学研究所

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