Intelligent Prediction of Subway Tunnel Settlement: A Novel Approach Using a Hybrid HO-GPR Model

Precise prediction of structural settlement in subway tunnels is crucial for ensuring safety during both construction and operational phases; however, the non-linear characteristics of monitoring data pose a significant challenge to achieving this goal. To address this issue, this study proposes a hybrid predictive model, termed HO-GPR. This model integrates the Hippopotamus Optimization (HO) algorithm—a novel bio-inspired meta-heuristic—with Gaussian Process Regression (GPR), a non-parametric probabilistic machine learning method. Specifically, HO is utilized to globally optimize the hyperparameters of GPR to enhance its adaptability to complex deformation patterns. The model was validated using 52 months of field settlement monitoring data collected from the Urumqi Metro Line 1 tunnel. Through a series of comparative and generalization experiments, the accuracy and adaptability of the model were systematically evaluated. The results demonstrate that the HO-GPR model is superior to five benchmark models—namely Gated Recurrent Unit (GRU), Support Vector Regression (SVR), HO-optimized Back Propagation Neural Network (HO-BP), standard GPR, and ARIMA—in terms of accuracy and stability. It achieved a Coefficient of Determination (R2) of 0.979, while the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) were as low as 0.318 mm, 0.240 mm, and 1.83%, respectively, proving its capability for effective prediction with non-linear data. The findings of this research can provide valuable technical support for the structural safety management of subway tunnels.