The structural integrity and operational safety of High-Speed Railway (HSR) bridges are increasingly threatened by low-frequency industrial vibrations. As traditional numerical methods are inadequate for real-time monitoring, this study proposes a novel FE-Augmented intelligent prediction framework to evaluate and forecast the structural response of HSR bridges subjected to ambient industrial excitations. Field measurements conducted on an HSR bridge near a stone-processing plant revealed a severe 1.5 Hz global resonance, with lateral displacements exceeding safety limits by a factor of 4.4 even without train loads. To overcome data scarcity for machine learning, a 3D coupled bridge-soil finite element model was developed to augment the vibration dataset. Subsequently, a hybrid deep learning framework integrating Long Short-Term Memory (LSTM) networks and Random Forest (RF) was established. By aligning the LSTM input time-window with the physical wave propagation delay, the predictive model gains physical interpretability. Furthermore, ablation studies confirmed that integrating spatial waveguide information with hybrid features minimizes prediction errors. Validation demonstrates that by fusing LSTM-extracted deep temporal features with handcrafted statistical indicators, the proposed LSTM-RF framework overcomes the amplitude clipping effect common in standard neural networks. It accurately captures complex spatial interference and beat phenomena under multi-machine operations, achieving a high Coefficient of Determination (R 2 = 0.965) and a low Normalized RMSE of 2.43% under extreme load scenarios. The trained hybrid model accelerates prediction inference to the millisecond scale, representing over six orders of magnitude improvement in computational efficiency compared to conventional 3D FEM. This framework provides an efficient, real-time early-warning methodology for safeguarding HSR infrastructure against complex environmental vibrations.
Bi et al. (Tue,) studied this question.