Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 763-774 |
| Seitenumfang | 12 |
| Fachzeitschrift | Periodica Polytechnica Civil Engineering |
| Jahrgang | 63 |
| Ausgabenummer | 3 |
| Frühes Online-Datum | 20 Juni 2019 |
| Publikationsstatus | Veröffentlicht - 25 Sept. 2019 |
Abstract
To prevent catastrophic consequences of slope failure, it can be effective to have in advance a good understanding of the effect of both, internal and external triggering-factors on the slope stability. Herein we present an application of advanced Bayesian networks for solving geotechnical problems. A model of soil slopes is constructed to predict the probability of slope failure and analyze the influence of the induced-factors on the results. The paper explains the theoretical background of enhanced Bayesian networks, able to cope with continuous input parameters, and Credal networks, specially used for incomplete input information. Two geotechnical examples are implemented to demonstrate the feasibility and predictive effectiveness of advanced Bayesian networks. The ability of BNs to deal with the prediction of slope failure is discussed as well. The paper also evaluates the influence of several geotechnical parameters. Besides, it discusses how the different types of BNs contribute for assessing the stability of real slopes, and how new information could be introduced and updated in the analysis.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Erdkunde und Planetologie (insg.)
- Geotechnik und Ingenieurgeologie
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in: Periodica Polytechnica Civil Engineering, Jahrgang 63, Nr. 3, 25.09.2019, S. 763-774.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Failure analysis of soil slopes with advanced Bayesian networks
AU - He, Longxue
AU - Gomes, António Topa
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Funding information: This work presented in this article is supported by the Chinese Scholarship Council.
PY - 2019/9/25
Y1 - 2019/9/25
N2 - To prevent catastrophic consequences of slope failure, it can be effective to have in advance a good understanding of the effect of both, internal and external triggering-factors on the slope stability. Herein we present an application of advanced Bayesian networks for solving geotechnical problems. A model of soil slopes is constructed to predict the probability of slope failure and analyze the influence of the induced-factors on the results. The paper explains the theoretical background of enhanced Bayesian networks, able to cope with continuous input parameters, and Credal networks, specially used for incomplete input information. Two geotechnical examples are implemented to demonstrate the feasibility and predictive effectiveness of advanced Bayesian networks. The ability of BNs to deal with the prediction of slope failure is discussed as well. The paper also evaluates the influence of several geotechnical parameters. Besides, it discusses how the different types of BNs contribute for assessing the stability of real slopes, and how new information could be introduced and updated in the analysis.
AB - To prevent catastrophic consequences of slope failure, it can be effective to have in advance a good understanding of the effect of both, internal and external triggering-factors on the slope stability. Herein we present an application of advanced Bayesian networks for solving geotechnical problems. A model of soil slopes is constructed to predict the probability of slope failure and analyze the influence of the induced-factors on the results. The paper explains the theoretical background of enhanced Bayesian networks, able to cope with continuous input parameters, and Credal networks, specially used for incomplete input information. Two geotechnical examples are implemented to demonstrate the feasibility and predictive effectiveness of advanced Bayesian networks. The ability of BNs to deal with the prediction of slope failure is discussed as well. The paper also evaluates the influence of several geotechnical parameters. Besides, it discusses how the different types of BNs contribute for assessing the stability of real slopes, and how new information could be introduced and updated in the analysis.
KW - Advanced bayesian networks
KW - Drainage
KW - Failure probability
KW - Slope stability
KW - Water table
UR - http://www.scopus.com/inward/record.url?scp=85073574790&partnerID=8YFLogxK
U2 - 10.3311/PPci.14092
DO - 10.3311/PPci.14092
M3 - Article
AN - SCOPUS:85073574790
VL - 63
SP - 763
EP - 774
JO - Periodica Polytechnica Civil Engineering
JF - Periodica Polytechnica Civil Engineering
SN - 0553-6626
IS - 3
ER -