Parameterizing bayesian network representations of social-behavioral models by expert elicitation

2010 ◽  
Author(s):  
Stephen Walsh ◽  
Angela Dalton ◽  
Paul Whitney ◽  
Amanda White
Keyword(s):  
Bayesian Network ◽  
Expert Elicitation ◽  
Behavioral Models

scholarly journals Collapse Risk Analysis of Deep Foundation Pits in Metro Stations Using a Fuzzy Bayesian Network and a Fuzzy AHP

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Guohua Zhang ◽  
Chengtang Wang ◽  
Yuyong Jiao ◽  
Hao Wang ◽  
Weimin Qin ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Fuzzy Ahp ◽  
Fault Tree Analysis ◽  
Expert Elicitation ◽  
Deep Foundation ◽  
Foundation Pit ◽  
Metro Station ◽  
Potential Risk Factors ◽  
Effective Decision Making

Collapse risk analysis is of great significance for ensuring construction safety in foundation pits. This study proposes a comprehensive methodology for dynamic risk analysis of foundation pit collapse during construction based on a fuzzy Bayesian network (FBN) and a fuzzy analytical hierarchy process (FAHP). Firstly, the potential risk factors contributing to foundation pit collapse are identified based on the results of statistical analysis of foundation pit collapse cases, expert inquiry, and fault tree analysis. Then, a FAHP and improved expert elicitation considering a confidence index are adopted to elicit the probability parameters of the BN. On this basis, quantitative risk reasoning and sensitivity analysis of foundation pit collapse are achieved by means of fuzzy Bayesian inference. Finally, an actual deep foundation pit in a metro station was used to illustrate a specific application of this approach, and the results were in accordance with the field observations and numerical simulation results. The proposed approach can provide effective decision-making support for planners and engineers, which is vital to the prevention and control of the occurrence of the foundation pit collapse accidents.

scholarly journals Estimating Bridge Reliability by Using Bayesian Networks

2021 ◽  
Author(s):  
Andrew B. Groeneveld ◽  
Stephanie G. Wood ◽  
Edgardo Ruiz
Keyword(s):  
Mechanical Properties ◽  
Reinforced Concrete ◽  
Bayesian Network ◽  
Cross Section ◽  
Visual Observation ◽  
Expert Elicitation ◽  
Structural System ◽  
Concrete Girders ◽  
Bridge Reliability ◽  
Main Components

As part of an inspection, bridge inspectors assign condition ratings to the main components of a bridge’s structural system and identify any defects that they observe. Condition ratings are necessarily somewhat subjective, as they are influenced by the experience of the inspectors. In the current work, procedures were developed for making inferences on the reliability of reinforced concrete girders with defects at both the cross section and the girder level. The Bayesian network (BN) tools constructed in this work use simple structural m echanics to model the capacity of girders. By using expert elicitation, defects observed during inspection are correlated with underlying deterioration mechanisms. By linking these deterioration mechanisms with reductions in mechanical properties, inferences on the reliability of a bridge can be made based on visual observation of defects. With more development, this BN tool can be used to compare conditions of bridges relative to one another and aid in the prioritization of repairs. However, an extensive survey of bridges affected by deterioration mechanisms is needed to confidently establish valid relationships between deterioration severity and mechanical properties.

scholarly journals Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network

2021 ◽  
Vol 11 (8) ◽  
pp. 3619
Author(s):  
Weiliang Qiao ◽  
Xiaoxue Ma ◽  
Yang Liu ◽  
He Lan
Keyword(s):  
Bayesian Network ◽  
Absorptive Capacity ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Expert Elicitation ◽  
Safety Level ◽  
Prior Probabilities ◽  
Safety Issues ◽  
Northern Sea Route ◽  
Probability Table

The safety level of the northern sea route (NSR) is a common concern for the related stakeholders. To address the risks triggered by disruptions initiating from the harsh environment and human factors, a comprehensive framework is proposed based on the perspective of resilience. Notably, the resilience of NSR is decomposed into three capacities, namely, the absorptive capacity, adaptive capacity, and restorative capacity. Moreover, the disruptions to the resilience are identified. Then, a Bayesian network (BN) model is established to quantify resilience, and the prior probabilities of parent nodes and conditional probability table for the network are obtained by fuzzy theory and expert elicitation. Finally, the developed Bayesian networkBN model is simulated to analyze the resilience level of the NSR by back propagation, sensitivity analysis, and information entropy analysis. The general interpretation of these analyses indicates that the emergency response, ice-breaking capacity, and rescue and anti-pollution facilities are the critical factors that contribute to the resilience of the NSR. Good knowledge of the absorptive capacity is the most effective way to reduce the uncertainty of NSR resilience. The present study provides a resilience perspective to understand the safety issues associated with the NSR, which can be seen as the main innovation of this work.

scholarly journals Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

2021 ◽  
Vol 19 (1) ◽  
pp. 297
Author(s):  
Yang Liu ◽  
Xiaoxue Ma ◽  
Weiliang Qiao ◽  
Huiwen Luo ◽  
Peilong He
Keyword(s):  
Human Factors ◽  
Bayesian Network ◽  
Human Factor ◽  
Fuzzy Theory ◽  
Fault Tree ◽  
Expert Elicitation ◽  
Risk Modeling ◽  
Nested Model ◽  
Prior Probabilities ◽  
Operational Activities

The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

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