A continuous Bayesian network for earth dams’ risk assessment: an application

Morales-Nápoles, Oswaldo; Delgado-Hernández, David Joaquín; Escobedo, David De León; Artega-Arcos, J.C.

doi:10.1080/15732479.2012.731416

Cited by 31 publications

(11 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Clemen et al (2000), this technique performed reasonably accurate in comparison to other methods and only rarely incoherent assessments were made. Similarly, Garth-1290 waite et al (2005), Kunda & Nisbett (1986) and (Gokhale & Press, 1982) come to the conclusion that this method is reasonably accurate and might be preferred if a population is given. Yet the importance of an expert's familiarity with the population is emphasized.…”

mentioning

confidence: 91%

See 1 more Smart Citation

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Werner

Bedford

Cooke

et al. 2017

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

Many applications in decision making under uncertainty and probabilistic risk assessment require the assessment of multiple, dependent uncertain quantities, so that in addition to marginal distributions, interdependence needs to be modelled in order to properly understand the overall risk. Nevertheless, relevant historical data on dependence information are often not available or simply too costly to obtain. In this case, the only sensible option is to elicit this uncertainty through the use of expert judgements. In expert judgement studies, a structured approach to eliciting variables of interest is desirable so that their assessment is methodologically robust. One of the key decisions during the elicitation process is the form in which the uncertainties are elicited. This choice is subject to various, potentially conflicting, desiderata related to e.g. modelling convenience, coherence between elicitation parameters and the model, combining judgements, and the assessment burden for the experts. While extensive and systematic guidance to address these considerations exists for single variable uncertainty elicitation, for higher dimensions very little such guidance is available. Therefore this paper offers a systematic review of the current literature on eliciting dependence. The literature on the elicitation of dependence parameters such as correlations is presented alongside commonly used dependence models and experience from case studies. From this, guidance about the strategy for dependence assessment is given and gaps in the existing research are identified to determine future directions for structured methods to elicit dependence. (Anca M. Hanea), O.MoralesNapoles@tudelft.nl (Oswaldo Morales-Nápoles) cause variables in the model are correlated, or indirectly when an uncertainty analysis of model parameters is carried out to explore model robustness. Both cases exhibit complex interrelations and dependencies which need to be considered if assumptions such as independence are not 10 justifiable.However, it is often not straightforward to either model or quantify dependence. In particular whenever no relevant historical data are available, the only sensible way to achieve uncertainty quantification is through eliciting ex-15

show abstract

mentioning

confidence: 91%

“…"Given that X is known to bex with complete certainty, (2008) and Weisberg (2005). Recall that here information on the de- In a similar manner, parameters can be elicited for (multiple) linear regression models.…”

mentioning

confidence: 99%

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Werner

Bedford

Cooke

et al. 2017

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…The conditional expected maintenance is now 3.6 years, though in this case, the 95th percentile of the distribution would indicate that the next maintenance may be performed after 14.3 years. In this section, the maintenance time is presented as a random variable, but when specific conditions regarding particular information for each dam under investigation are available, the maintenance time becomes a deterministic value as shown in the accompanying paper (Delgado-Hernandez et al, 2012) and in the next section.…”

Section: Maintenance Decisionsmentioning

confidence: 99%

A continuous Bayesian network for earth dams' risk assessment: methodology and quantification

Morales-Nápoles

Delgado-Hernández

Escobedo

et al. 2013

Structure and Infrastructure Engineering

Self Cite

View full text Add to dashboard Cite

Dams' safety is highly important for authorities around the world. The impacts of a dam failure can be enormous. Models for investigating dam safety are required for helping decision-makers to mitigate the possible adverse consequences of flooding. A model for earth dam safety must specify clearly possible contributing factors, failure modes and potential consequences of dam failure. Probabilistic relations between variables should also be specified. Bayesian networks (BNs) have been identified as tools that would assist dam engineers on assessing risks. BNs are graphical models that facilitate the construction of a joint probability distribution. Most of the time, the variables included in a model for earth dam risk assessment involve continuous quantities. The presence of continuous random variables makes the implementation of discrete BNs difficult. An alternative to discrete BNs is the use of non-parametric continuous BNs, which will be briefly described in this article. As an example, a model for earth dams' safety in the State of Mexico will be discussed. Results regarding the quantification of conditional rank correlations through ratios of unconditional rank correlations have not been presented before and are introduced herein. While the complete application of the model for the State of Mexico is presented in an accompanying paper, here some results regarding model use are shown for demonstration purposes. The methods presented in this article can be applied for investigating risks of failure of civil infrastructures other than earth dams.

show abstract

“…Based on the response surface methodology and first-order reliability method, Babu and Srivastava [16] performed reliability analyses for four selected rehabilitated earth dams. Delgado-Hernández et al [17] and Peyras et al [18] explored risk assessment of earth dams based on a continuous Bayesian network; Chen et al [19]optimized the point estimation method, solved the problem that a large amount of storage space is needed in the calculation process, and applied the point estimation method to embankment slope stability reliability analysis. Recently, several studies have introduced reliability theory into the assessment of ultrahigh dam slope stability.…”

Section: Introductionmentioning

confidence: 99%

Safety Standard for Special Class Damslopes Based on Reliability Analysis

Huang

Zheng

et al. 2020

Advances in Civil Engineering

View full text Add to dashboard Cite

The risk of slope failure is determined by the degree of damage caused by the slope slide. For the special-high slope of some high-risk water conservancy and hydropower projects, the standard should be appropriately raised. Thus, the safety standard for these slopes is explored on the basis of reliability analysis. The slopes with high risk of failure are divided into special class I and special class II slopes depending on the risk levels and acceptable risk standards. The concept of reliability theory-based relative ratio of the safety margin is utilized to establish the relationship between annual failure probability and safety factor, thereby obtaining the reasonable safety factors for different slopes. Results show that the values of safety factors for special class I and special class II are 1.40 and 1.35, respectively. These results can provide a reference for exploring the safety standards of dams with a height of more than 200 m.

show abstract

A continuous Bayesian network for earth dams’ risk assessment: an application

Cited by 31 publications

References 7 publications

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

A continuous Bayesian network for earth dams' risk assessment: methodology and quantification

Safety Standard for Special Class Damslopes Based on Reliability Analysis

Contact Info

Product

Resources

About