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

authored by

Longxue He, Yong Liu, Sifeng Bi, Li Wang, Matteo Broggi, Michael Beer

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.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

Dalian Jiaotong University
University of Liverpool
Tongji University
Wuhan University

Type

Article

Journal

Underground Space (China)

Volume

5

Pages

315-323

No. of pages

9

ISSN

2096-2754

Publication date

12.2020

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Civil and Structural Engineering, Geotechnical Engineering and Engineering Geology, Building and Construction

Electronic version(s)

https://doi.org/10.1016/j.undsp.2019.07.001 (Access: Open)