Research on safety evaluation of collapse risk in highway tunnel construction based on intelligent fusion
Bo Wu,
Yajie Wan,
Shixiang Xu,
Yishi Lin,
Yonghua Huang,
Xiaoming Lin,
Ke Zhang
Affiliations
Bo Wu
School of Civil and Architectural Engineering, East China University of Technology, Nanchang, 330013, Jiangxi, China; College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China
Yajie Wan
School of Civil and Architectural Engineering, East China University of Technology, Nanchang, 330013, Jiangxi, China; Corresponding author.
Shixiang Xu
School of Civil and Architectural Engineering, East China University of Technology, Nanchang, 330013, Jiangxi, China; Corresponding author.
Yishi Lin
Fujian Rongsheng Municipal Engineering Co., Ltd., Fuzhou, 350011, Fujian, China
Yonghua Huang
Lianjiang City Construction Investment Group, Fuzhou, 350011, Fujian, China
Xiaoming Lin
Lianjiang City Construction Investment Group, Fuzhou, 350011, Fujian, China
Ke Zhang
College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China
To solve the problems of untimely and low accuracy of tunnel project collapse risk prediction, this study proposes a method of multi-source information fusion. The method uses the PSO-SVM model to predict the surrounding rock displacement. With the prediction index as the benchmark, the Cloud Model (CM) is used to calculate the basic probability assignment value. At the same time, the improved D-S theory is used to fuse the monitoring data, the advanced geological forecast, and the tripartite information indicators of site inspection patrol. This method is applied to the risk assessment of Jinzhupa Tunnel, and the decision-makers adjust the risk factors in time according to the prediction level. In the end, the tunnel did not collapse on a large scale.
