Deep learning based identification and uncertainty analysis of metro train induced ground-borne vibration

The problems of ground-borne vibration induced by running metro trains are becoming a major concern. Considering the uncertainty in train-track-tunnel-soil-building system, the vibration evaluation based on long-term monitoring is required to investigate the effect of vibration on the residents, sensitive instruments, and buildings to provide evidence for vibration mitigation design. In this study, a deep learning based approach is proposed to identify train induced vi-bration segments efficiently for subsequent vibration evaluations. Furthermore, an experiment is presented in which vibration monitoring on the tunnel wall, ground surface and floors in a building due to metro train passages were performed. The proposed identification approach is validated in the experiment, and then the characteristics and sources of the uncertainty for train induced ground-borne vibration are analysed to improve the quality of vibration evaluation.