An Adaptive OVMD-SSA-GRU Hybrid Framework for Highway Soft Rock Slope Deformation Prediction

Highway soft rock slope deformation monitoring produces nonlinear, non-stationary, and multi-scale time series that are strongly affected by rainfall and field noise. This study proposes an adaptive hybrid framework that combines optimal variational mode decomposition (OVMD), the sparrow search algorithm (SSA), and gated recurrent unit (GRU) networks. High-precision BeiDou Global Navigation Satellite System (GNSS) observations collected hourly over a 120-day K55 monitoring campaign (late 2022 to early 2023) are cleaned using a cumulative-sum (CUSUM) change-point detector and cubic-spline reconstruction, while rainfall-related hydro-mechanical variables derived from seepage and slope-stability analyses are incorporated as external inputs. To prevent future-information leakage during blind testing, OVMD is recomputed causally at each one-day-ahead forecast origin using only observations available up to that origin; it then separates the deformation signal into physically interpreted trend, periodic, and high-frequency components, and SSA adaptively optimizes component-specific GRU hyperparameters for parallel prediction and reconstruction. On the K55 strongly weathered shale slope, across five independent runs the proposed model achieved a mean root-mean-square error (RMSE) of 0.04 ± 0.01 mm and a mean absolute percentage error (MAPE) of 0.18 ± 0.05%, outperforming standard GRU, long short-term memory (LSTM), and back-propagation neural network (BPNN) baselines that received an equivalent validation-based hyperparameter search. Cross-scenario evaluation on a geologically distinct K14 marl slope, independently retrained on its own record, yielded a mean RMSE of 0.14 ± 0.02 mm and a mean MAPE of 0.42 ± 0.08%. The results indicate that the proposed framework improves prediction accuracy while retaining useful cross-scenario robustness, supporting intelligent monitoring and early warning of rainfall-sensitive highway slopes.