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Title:

Uncertainty Quantification and PCA-Based Model Reduction for Parallel Monte Carlo Analysis of Infrastructure System Reliability

Accession Number:

01629804

Record Type:

Component

Abstract:

Natural disasters can have catastrophic impacts on the functionality of transportation systems and cause severe physical and socio-economic losses. Therefore, it is essential to develop tools that provide accurate and efficient evaluation of the system reliability to facilitate optimal decision making for mitigation, preparedness, response, and recovery practices. While numerous research efforts have addressed and quantified the impact of natural disasters on transportation systems, they still suffer from high computational cost, impairing the accuracy and efficiency of the application of these approaches on large networks. To overcome this challenge, this paper presents fast approaches for: (i) uncertainty quantification in the modeling of natural disasters, in order to improve the accuracy of system response calculations, and (ii) infrastructure system model reduction, based on the principal component analysis, in order to speed-up computations of the system response. The gain in accuracy and efficiency is achieved by a more effective exploration of the sample space lead to an accelerated convergence of Monte Carlo simulations. The proposed approaches are then applied to a simulation-based study of the two-terminal connectivity in a benchmark transportation network subject to an extreme earthquake event. The numerical results highlight the effectiveness of the proposed approaches in improving the accuracy and efficiency of the reliability analysis.

Supplemental Notes:

This paper was sponsored by TRB committee ABR10 Standing Committee on Critical Transportation Infrastructure Protection.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-06540

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Nabian, Mohammad Amin
Meidani, Hadi

Pagination:

15p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References

Uncontrolled Terms:

Principal component analysis

Subject Areas:

Highways; Planning and Forecasting

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-06540

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 12:40PM