Performance evaluation of machine learning methods for ground settlement prediction

Prediction of tunneling-induced ground settlements is an important task during tunnel excavation in urban areas. Ground settlements should be limited within a tolerable threshold to avoid damages to existing buildings and infrastructures during and after the construction. Machine learning (ML) methods have been gaining an increasing popularity in many fields, including tunnel excavations, as a powerful learning and predicting technique. The paper analyzes the possibilities of different machine learning methods to predict the ground surface settlement induced by tunneling. Three different ML approaches, including support vector regression (SVR), multilayer perceptron (MLP), and long short-term memory (LSTM) networks, are utilized. Two techniques are used for the hyperparameter optimization: particle swarm optimization (PSO) and grid search (GS) methods. To assess the performance of the ML methods, three performance metrics are used: the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The paper demonstrates the applicability of the three ML methods in tunneling-induced ground settlement prediction for real-world settlement datasets. The obtained experimental results indicate that the proposed ML models can accurately and efficiently predict the ground settlement.