Estimation of failure probability in braced excavation using Bayesian networks with integrated model updating

Tools

He, Longxue and Liu, Yong and Bi, Sifeng and Wang, Li and Broggi, Matteo and Beer, Michael (2020) Estimation of failure probability in braced excavation using Bayesian networks with integrated model updating. Underground Space, 5 (4). pp. 315-323. ISSN 2467-9674 (https://doi.org/10.1016/j.undsp.2019.07.001)

[thumbnail of He-etal-US-2020-Estimation-of-failure-probability-in-braced-excavation]
Preview
 Text. Filename: He_etal_US_2020_Estimation_of_failure_probability_in_braced_excavation.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (1MB)| Preview

Abstract

A probabilistic model is proposed that uses observation data to estimate failure probabilities during excavations. The model integrates a Bayesian network and distanced-based Bayesian model updating. In the network, the movement of a retaining wall is selected as the indicator of failure, and the observed ground surface settlement is used to update the soil parameters. The responses of wall deflection and ground surface settlement are accurately predicted using finite element analysis. An artificial neural network is employed to construct the response surface relationship using the aforementioned input factors. The proposed model effectively estimates the uncertainty of influential factors. A case study of a braced excavation is presented to demonstrate the feasibility of the proposed approach. The update results facilitate accurate estimates according to the target value, from which the corresponding probabilities of failure are obtained. The proposed model enables failure probabilities to be determined with real-time result updating.

ORCID iDs

He, Longxue, Liu, Yong, Bi, Sifeng ORCID logoORCID: https://orcid.org/0000-0002-8600-8649, Wang, Li, Broggi, Matteo and Beer, Michael;
CORE (COnnecting REpositories)