Predictive modeling of shallow tunnel behavior: Leveraging machine learning for maximum convergence displacement estimation

Accurate prediction of maximum convergence in unsupported, shallow tunnel construction is crucial for optimizing the lining and ensuring tunnel safety. Machine learning (ML) algorithms, especially through boosting techniques, enable effective solution of complex engineering problems and demonstrate their capabilities in problem solving and optimization. In this study, the FLAC 3D package was used to create a robust and validated database of 954 datasets. Five tree-based ML algorithms, including extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), gradient boosting machine (GBM), histogram-based gradient boosting (HGB) and categorical boosting (CatBoost), were used to predict the maximum convergence displacement for unsupported shallow tunnels. For the test dataset, XGBoost outperformed the other models with an excellent coefficient of determination of 0.9633, a minimum mean absolute error of 0.0021 and a low root mean squared error of 0.00725. HGB followed closely behind, and GBM and CatBoost showed strong performances, while Adaboost was less effective. The superior performance of XGBoost highlights its effectiveness in predicting maximum convergence in shallow tunnels. An in-depth sensitivity analysis within the XGBoost model showed the significant influence of soil elastic modulus on the maximum convergence displacement in unsupported tunnels. The remarkable results achieved by the XGBoost algorithm on our complex tunnel convergence predictions illustrate the profound ability of ML to tackle complicated geotechnical challenges. This interdisciplinary collaboration demonstrates the potential of advanced algorithms to improve safety and efficiency in construction, underlining the crucial role of technology in tackling complex problems and establishing a new paradigm for innovation in the field.