Citation

Wu B, Wan Y, Xu S, Lin Y, Huang Y, Lin X, Zhang K. Heliyon 2024; 10(4): e26152.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.heliyon.2024.e26152

PMID

38404906

PMCID

PMC10884445

Abstract

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.

Language: en

Keywords

Cloud model; D-S evidence; PSO-SVM; Tunnel collapse