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 vibration 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.