Presentation Type
Oral Presentation
Start Date
5-8-2024 1:00 PM
End Date
5-8-2024 3:00 PM
Subjects
Civil engineering
Advisor
David Y. Yang
Student Level
Doctoral
Abstract
Efficiently assessing the risk of asset failure due to deterioration or extreme events is crucial for transportation asset management. Traditional methods often lack effectiveness in directly evaluating system performance-based risks, facing challenges like the exponential increase in system states and the emergence of low-probability high-consequence events ("grey swan" events). To address these, this paper introduces a novel performance-based risk assessment approach for large-scale transportation networks, inspired by the Transitional Markov Chain Monte Carlo (TMCMC) method. This method transforms the risk assessment problem into a high-dimensional posterior distribution, with system risk acting as the normalization factor (evidence). It also provides risk-based importance factors for assets, offering comprehensive risk insights. Analytical examples and a case study on the Oregon highway network demonstrate the approach's effectiveness and practicality in real-world scenarios.
Creative Commons License or Rights Statement
This work is licensed under a Creative Commons Attribution 4.0 License.
Persistent Identifier
https://archives.pdx.edu/ds/psu/41943
Included in
Performance-Based Risk Assessment For Large-Scale Transportation Networks
Efficiently assessing the risk of asset failure due to deterioration or extreme events is crucial for transportation asset management. Traditional methods often lack effectiveness in directly evaluating system performance-based risks, facing challenges like the exponential increase in system states and the emergence of low-probability high-consequence events ("grey swan" events). To address these, this paper introduces a novel performance-based risk assessment approach for large-scale transportation networks, inspired by the Transitional Markov Chain Monte Carlo (TMCMC) method. This method transforms the risk assessment problem into a high-dimensional posterior distribution, with system risk acting as the normalization factor (evidence). It also provides risk-based importance factors for assets, offering comprehensive risk insights. Analytical examples and a case study on the Oregon highway network demonstrate the approach's effectiveness and practicality in real-world scenarios.