Ground Settlement Prediction in Urban Tunnelling: Leveraging Metaheuristic-Optimized Random Forest Models

Abstract With the continuous acceleration of urbanization, the problem of ground settlement induced by underground tunnel construction has received more and more widespread attention. This study addresses the challenge of predicting surface subsidence in urban tunnel construction, a critical concern in geotechnical engineering. Random forest (RF) models were optimized using three distinct metaheuristic algorithms: ant lion optimizer (ALO), multiverse optimizer (MVO), and grasshopper optimization algorithm (GOA). The enhancements significantly improved model accuracy, as demonstrated by the detailed performance metrics of the GOA-optimized RF model (GOA-RF Pop = 20) on the Changsha Metro Line 3 dataset, which included 294 instances and 12 feature parameters. This model achieved an MAE of 1.3820, MAPE of 181.2249, correlation coefficient of 0.9273, and RMSE of 2.5209 in the training set; and an MAE of 2.4695, MAPE of 275.2054, R value of 0.8877, and RMSE of 4.2540 in the testing set. A sensitivity analysis within the random forest framework revealed that torque (To) and ground condition (Gc) had the most significant impact on predicting surface subsidence, whereas the influence of the modified dynamic penetration test (MDPT) was the least pronounced. Additionally, a MATLAB-based application was developed using the App Designer module, integrating these models into a user-friendly GUI that facilitates the prediction and management of surface subsidence risks, thereby enhancing the practical application and effectiveness of engineering risk mitigation strategies.