Machine Learning Based on Hybrid Optimization Algorithm for the Prediction and Risk Assessment of Rock Tunnel Lining Displacement

Abstract

Accurate prediction of tunnel vault displacement and reliable assessment of deformation risk are essential for tunnel safety management under complex geological conditions. This study develops an integrated data-driven framework combining machine-learning prediction, metaheuristic hyperparameter optimization, statistical model comparison, and uncertainty-informed relative deformation-severity classification. Four baseline models, namely BP, SVM, LSTM, and Peephole-LSTM, were combined with GA, WOA, and PSO to construct 12 optimized models, resulting in 16 candidate models. Monitoring observations were chronologically divided into training, validation, and test sets at a ratio of 70%:15%:15%, and normalization parameters were calculated exclusively from the training set to prevent data leakage. Model robustness was further evaluated using rolling validation, the Friedman test, and post hoc Wilcoxon signed-rank tests with Holm correction. PSO-Peephole-LSTM achieved the best test-set performance, with an MAE of 0.058 mm, an RMSE of 0.074 mm, and an R2 of 0.884. PSO-Peephole-LSTM also achieved the lowest mean RMSE and average rank across the rolling validation windows, with statistically significant improvements over the competing models after Holm correction. Finally, 2000 Latin hypercube samples were generated to propagate prediction uncertainty and establish a five-level, project-specific relative deformation-severity classification framework. The integrated workflow provides a systematic connection between tunnel monitoring, displacement forecasting, uncertainty characterization, and uncertainty-informed monitoring and decision support.