Abstract : The structural stability of metro systems is essential for safe and reliable urban rail operation. Large-scale underground construction may influence existing metro lines, making accurate settlement prediction necessary. Traditional empirical and numerical methods often fail to capture long-term settlement behavior. This study predicts track bed settlement of Hangzhou Metro Line 1 using monitoring data collected during the Grand Canal diversion construction. A hybrid model (CEEMDAN-BWO-BiLSTM-ATT model) integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Beluga Whale Optimization, Bidirectional Long Short-Term Memory, and an attention mechanism is developed. Results from four monitoring points along the up line show good performance, with an average R 2 of 0.962, RMSE of 0.076 mm, MAE of 0.066 mm, and MAPE of 6.383%. Validation using a down-line monitoring point confirms accuracy and generalization. The results indicate that the model captures nonlinear settlement behavior and provides a reliable data-driven approach for metro deformation prediction. • A CEEMDAN–BWO–BiLSTM–ATT hybrid model is proposed for long-term metro track settlement forecasting. • Multi-scale decomposition, BWO-based tuning, and attention-enhanced BiLSTM are integrated to improve prediction. • Field monitoring at multiple points confirms reliable performance for practical settlement prediction and early warning.
He et al. (Wed,) studied this question.