Development of an optimization model for a monitoring point in tunnel stress deduction using a machine learning algorithm  

作　　者：Xuyan Tan Weizhong Chen Luyu Wang Wei Ye 

机构地区：[1]State Key Laboratory of Geomechanics and Geotechnical Engineering,Chinese Academy of Sciences,Wuhan,China [2]Institute of Rock and Soil Mechanics,University of Chinese Academy of Sciences,Beijing,China [3]Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,Hung Hom,Kowloon,Hong Kong,China

出　　处：《Deep Underground Science and Engineering》2025年第1期35-45,共11页深地科学(英文)

基　　金：Key project in Hubei Province,Grant/Award Number:2023BCB048;National Key R&D Program of China,Grant/Award Number:2021YFC3100805;National Natural Science Foundation of China,Grant/Award Numbers:42293355,51991392;Project for Research Assistant of Chinese Academy of Sciences。

摘　　要：Monitoring of the mechanical behavior of underwater shield tunnels is vital for ensuring their long-term structural stability.Typically determined by empirical or semi-empirical methods,the limited number of monitoring points and coarse monitoring schemes pose huge challenges in terms of capturing the complete mechanical state of the entire structure.Therefore,with the aim of optimizing the monitoring scheme,this study introduces a spatial deduction model for the stress distribution of the overall structure using a machine learning algorithm.Initially,clustering experiments were performed on a numerical data set to determine the typical positions of structural mechanical responses.Subsequently,supervised learning methods were applied to derive the data information across the entire surface by using the data from these typical positions,which allows flexibility in the number and combinations of these points.According to the evaluation results of the model under various conditions,the optimized number of monitoring points and their locations are determined.Experimental findings suggest that an excessive number of monitoring points results in information redundancy,thus diminishing the deduction capability.The primary positions for monitoring points are determined as the spandrel and hance of the tunnel structure,with the arch crown and inch arch serving as additional positions to enhance the monitoring network.Compared with common methods,the proposed model shows significantly improved characterization abilities,establishing its reliability for optimizing the monitoring scheme.

关 键 词：machine learning MONITORING OPTIMIZATION simulation TUNNEL 

分 类 号：TG1[金属学及工艺—金属学]