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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.
Acta Geotechnica – Springer Journals
Published: Jan 1, 2024
Keywords: Bayesian model selection; Ground deformations; Kullback–Leibler divergence; Model complexity; Tunneling
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