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

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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Geotechnica Springer Journals

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

Pan, Q. J.; Li, X. Z.; Wang, S. Y.; Phoon, K. K.
Acta Geotechnica , Volume 19 (1) – Jan 1, 2024
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References (62)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1861-1125
eISSN
1861-1133
DOI
10.1007/s11440-023-01901-9
Publisher site
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Abstract

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.

Journal

Acta Geotechnica – Springer Journals

Published: Jan 1, 2024

Keywords: Bayesian model selection; Ground deformations; Kullback–Leibler divergence; Model complexity; Tunneling

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