Data-driven estimations of ground deformations induced by tunneling: a Bayesian perspective

Estimating tunneling-induced ground deformations is a key issue in tunnel engineering. Many analytical approaches, including empirical models and physical models, have been developed to predict tunneling-induced ground vertical and lateral displacements. However, the most suitable model complexity level and their associated predictive ability have not been fully plumbed. This paper aims to perform a statistically rigorous model comparison of several representative predicting models in the framework of Bayesian model selection, and a probabilistic assessment of the information gain of different types of monitoring data by assessing the Kullback-Leibler divergence. The results of the calculated model evidences show that the Loganathan-Poulos model is the most suitable one when predicting tunneling-induced ground deformations in the illustrative example even though it has the least model parameters. The analyses of the estimated Kullback-Leibler divergences indicate that the measured ground vertical deformations are more informative than the measured ground horizontal deformations. The finding of this study is a first step to clarifying the role of model complexity in tunneling-induced ground deformation analysis and is helpful to provide guidance for ground deformation monitoring in future tunneling engineering.