Simulation-Based Multi-Horizon Forecasting of Train-Induced Carbody Acceleration for an Integrated Station–Bridge Building: A Yichang North Railway Station Case Study

Large integrated station–bridge buildings combine track-bearing members, station floors, transfer structures, columns, and urban-rail facilities within a single coupled structural system. For such buildings, refined train–track–station dynamic simulations can reproduce train-induced vibration, but repeated time-history analysis remains costly when many operating conditions must be screened. This study develops a simulation-based response-database framework for multi-horizon forecasting of front-end carbody vertical acceleration (FCVA), defined here as the vertical acceleration at the front-end floor evaluation point of the leading carbody, in the integrated station–bridge building of Yichang North Railway Station. The project-specific database contains 700 operating cases constructed from 100 Latin-hypercube-sampled combinations of a dimensionless track-spectrum amplitude multiplier (TSA), structural damping ratio (DR), and track-spectrum initial moving position (TSIP), each evaluated at seven train speeds. With a sampling interval of 0.002 s, supervised samples were constructed using a 200-point historical window, and prediction horizons from 20 to 300 steps (0.04–0.60 s) were evaluated under a case-level split. Classical regression, tree ensembles, a multilayer perceptron, recurrent networks, a temporal convolutional network, and a Transformer were compared after automated hyperparameter selection. For the 20-step task, Extra Trees achieved the best performance, with a root mean squared error (RMSE) of 0.00336 m/s2 and R2 = 0.9958. In the independently refitted reference-fixed horizon experiment, Extra Trees retained R2 = 0.9526 at the 300-step horizon, while the temporal convolutional network (TCN) RMSE increased from 0.00394 to 0.01502 m/s2. The results show that the response database preserves exploitable short- to medium-range dynamic continuity, although phase drift and peak-timing uncertainty increase as the forecast horizon becomes longer. Parameter analysis indicates that train speed dominates both response energy and forecast error, whereas TSA mainly affects amplitude-related response metrics. On a common central processing unit (CPU) platform, the saved Extra Trees model processed 10,000 held-out windows in 0.1404±0.0008 s. The proposed method provides a computationally efficient response-screening and post-processing layer for design-stage assessment and operating-scenario comparison within the modeled parameter domain, complementing rather than replacing refined dynamic simulation and field validation.