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

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.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 15p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01629804
  • Record Type: Publication
  • Report/Paper Numbers: 17-06540
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 8 2016 12:40PM