Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 315-323 |
| Seitenumfang | 9 |
| Fachzeitschrift | Underground Space (China) |
| Jahrgang | 5 |
| Ausgabenummer | 4 |
| Frühes Online-Datum | 16 Sept. 2019 |
| Publikationsstatus | Veröffentlicht - Dez. 2020 |
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.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Erdkunde und Planetologie (insg.)
- Geotechnik und Ingenieurgeologie
- Ingenieurwesen (insg.)
- Bauwesen
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in: Underground Space (China), Jahrgang 5, Nr. 4, 12.2020, S. 315-323.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Estimation of failure probability in braced excavation using Bayesian networks with integrated model updating
AU - He, Longxue
AU - Liu, Yong
AU - Bi, Sifeng
AU - Wang, Li
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Funding information: This work is supported by the Chinese Scholarship Council .
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Bayesian networks
KW - Braced excavation
KW - Failure probability
KW - Sensitivity analysis
KW - Stochastic model updating
UR - http://www.scopus.com/inward/record.url?scp=85090326744&partnerID=8YFLogxK
U2 - 10.1016/j.undsp.2019.07.001
DO - 10.1016/j.undsp.2019.07.001
M3 - Article
AN - SCOPUS:85090326744
VL - 5
SP - 315
EP - 323
JO - Underground Space (China)
JF - Underground Space (China)
SN - 2096-2754
IS - 4
ER -