Wu Bo, Wan Yajie, Xu Shixiang, Lin Yishi, Huang Yonghua, Lin Xiaoming, Zhang Ke
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.
Heliyon. 2024 Feb 15;10(4):e26152. doi: 10.1016/j.heliyon.2024.e26152. eCollection 2024 Feb 29.
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.
为解决隧道工程塌方风险预测不及时、准确率低的问题，本研究提出一种多源信息融合方法。该方法采用粒子群优化支持向量机（PSO-SVM）模型预测围岩位移。以预测指标为基准，运用云模型（CM）计算基本概率赋值。同时，采用改进的证据理论融合监测数据、超前地质预报以及现场巡查三方信息指标。将该方法应用于金竹坝隧道的风险评估，决策者根据预测等级及时调整风险因素。最终，该隧道未发生大规模塌方。
