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

verfasst von

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.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

Dalian Jiaotong University
The University of Liverpool
Tongji University
Wuhan University

Typ

Artikel

Journal

Underground Space (China)

Band

5

Seiten

315-323

Anzahl der Seiten

9

ISSN

2096-2754

Publikationsdatum

12.2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Tief- und Ingenieurbau, Geotechnik und Ingenieurgeologie, Bauwesen

Elektronische Version(en)

https://doi.org/10.1016/j.undsp.2019.07.001 (Zugang: Offen)