A Multi-Source Intelligent Fusion Assessment Method for Dynamic Construction Risk of Subway Deep Foundation Pit: A Case Study

Abstract

The construction of a subway deep foundation pit is complex and risky, thus multiple safety risk factors bring great challenges to evaluating the safety status accurately. Advanced monitoring technology equipment could obtain a large number of monitoring data, and how integrating complex and diversified monitoring data to assess the safety risk of foundation pits has become a new problem. Therefore, an intelligent multi-source fusion assessment model is proposed. This model is mainly used for solving risk probability distribution, deep learning, and intelligent prediction of monitoring indicators, and then evaluating safety status by fusing various parameters of multiple indicators. Thus, based on the data of deep learning and the measured multivariate data, the dynamic risk during foundation pit construction can be obtained. Moreover, a typical case study was performed through monitoring and carrying out the risk assessment which is located at the Martyrs’ Lingyuan Station of Jinnan Metro Line R2, China. In this case, the PSO-SVM and LSTM models are used to predict the deformation trend, and the monitoring data is reliable with high precision. After multi-index fusion model calculation, the results show that the foundation pit structure is in a safe state, and the evaluation situation is basically consistent with the site. Consequently, the prediction of the new multi-source intelligent fusion risk assessment method is convincing.