A Water Leakage Risk Assessment Model for Shield Tunnel Based on Kalman Filter Data Fusion Method

Abstract

Multi-source information monitoring and data fusion analysis system has high practical value for the risk management of tunnel structure, especially in complex ground. The analysis of structure health will be getting more comprehensive and real-time with multisource monitoring technologies. Risk warning will be more accurate and timelier. Based on one shield tunnel project, this research analysed the water leakage risk of shield subsea tunnel in unfavourable geological conditions by using the method of geological information and structural monitoring data fusion. Firstly, Kalman filter method was used to denoise the monitoring data, and the effect of different monitoring data was compared; Then, the risk factor of water leakage was analysed, and the relationship between risk factors, geological information and monitoring data was established; Finally, the risk factors based on multi-source monitoring information were fused and analysed by using the improved D-S evidence theory method, and the final analysis results of water leakage risk were obtained. A multisource dynamic risk analysis model of tunnel water leakage was established. The results showed that the Kalman filter method has better noise reduction effect on the high-frequency monitoring data than that of low-frequency monitoring data, and the multi-source dynamic risk analysis model has a good prediction effect on the water leakage risk of tunnel lining in unfavourable ground condition.