Advanced Bayesian networks for reliability and risk analysis in geotechnical engineering

Download statistics - Document (COUNTER):

He, Longxue: Advanced Bayesian networks for reliability and risk analysis in geotechnical engineering. Hannover : Gottfried Wilhelm Leibniz Universität, Diss., 2020, viii, 111 S. DOI: https://doi.org/10.15488/9426

Selected time period:

year: 
month: 

Sum total of downloads: 607




Thumbnail
Abstract: 
The stability and deformation problems of soil have been a research topic of greatconcern since the past decades. The potential catastrophic events are induced by various complex factors, such as uncertain geotechnical conditions, external environment, and anthropogenic influence, etc. To prevent the occurrence of disasters in geotechnical engineering, the main purpose of this study is to enhance the Bayesian networks (BNs) model for quantifying the uncertainty and predicting the risk level in solving the geotechnical problems. The advanced BNs model is effective for analyzing the geotechnical problems in the poor data environment. The advanced BNs approach proposed in this study is applied to solve the stability of soil slopes problem associated with the specific-site data. When probabilistic models for soil properties are adopted, enhanced BNs approach was adopted to cope with continuous input parameters. On the other hand, Credal networks (CNs), developed on the basis of BNs, are specially used for incomplete input information. In addition, the probabilities of slope failure are also investigated for different evidences. A discretization approach for the enhanced BNs is applied in the case of evidence entering into the continuous nodes. Two examples implemented are to demonstrate the feasibility and predictive effectiveness of the BNs model. The results indicate the enhanced BNs show a precisely low risk for the slope studied. Unlike the BNs, the results of CNs are presented with bounds. The comparisonof three different input information reveals the more imprecision in input, the more uncertainty in output. Both of them can provide the useful disaster-induced informationfor decision-makers. According to the information updating in the models, the positionof the water table shows a significant role in the slope failure, which is controlled bythe drainage states. Also, it discusses how the different types of BNs contribute toassessing the reliability and risk of real slopes, and how new information could beintroduced in the analysis. The proposed models in this study illustrate the advancedBN model is a good diagnosis tool for estimating the risk level of the slope failure.In a follow-up study, the BNs model is developed based on its potential capabilityfor the information updating and importance measure. To reduce the influence ofuncertainty, with the proposed BN model, the soil parameters are updated accuratelyduring the excavation process, and besides, the contribution of epistemic uncertainty from geotechnical parameters to the potential disaster can be characterized based on the developed BN model. The results of this study indicate the BNs model is aneffective and flexible tool for risk analysis and decision making support in geotechnical engineering.
License of this version: CC BY 3.0 DE
Document Type: DoctoralThesis
Publishing status: publishedVersion
Issue Date: 2020
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie
Dissertationen

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 183 30.15%
2 image of flag of China China 56 9.23%
3 image of flag of United States United States 54 8.90%
4 image of flag of India India 37 6.10%
5 image of flag of No geo information available No geo information available 24 3.95%
6 image of flag of Indonesia Indonesia 18 2.97%
7 image of flag of Ireland Ireland 17 2.80%
8 image of flag of Colombia Colombia 17 2.80%
9 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 16 2.64%
10 image of flag of Russian Federation Russian Federation 14 2.31%
    other countries 171 28.17%

Further download figures and rankings:


Hinweis

Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository


Browse