Auto machine learning-based modelling and prediction of excavation-induced tunnel displacement

Abstract

The influence of a deep excavation on existing shield tunnels nearby is a vital issue in tunnelling engineering. Whereas, there lacks robust methods to predict excavation-induced tunnel displacements. In this study, an auto machine learning (AutoML)-based approach is proposed to precisely solve the issue. Seven input parameters are considered in the database covering two physical aspects, namely soil property, and spatial characteristics of the deep excavation. The 10-fold cross-validation method is employed to overcome the scarcity of data, and promote model's robustness. Six genetic algorithm (GA)-ML models are established as well for comparison. The results indicated that the proposed AutoML model is a comprehensive model that integrates efficiency and robustness. Importance analysis reveals that the ratio of the average shear strength to the vertical effective stress Eur/σv′, the excavation depth H, and the excavation width B are the most influential variables for the displacements. Finally, the AutoML model is further validated by practical engineering. The prediction results are in a good agreement with monitoring data, signifying that our model can be applied in real projects.