Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity

R. E. Stern, J. Song, D. B. Work

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

The two-terminal reliability problem in system reliability analysis is known to be computationally intractable for large infrastructure graphs. Monte Carlo techniques can estimate the probability of a disconnection between two points in a network by selecting a representative sample of network component failure realizations and determining the source-terminal connectivity of each realization. To reduce the runtime required for the Monte Carlo approximation, this article proposes an approximate framework in which the connectivity check of each sample is estimated using a machine-learning-based classifier. The framework is implemented using both a support vector machine (SVM) and a logistic regression based surrogate model. Numerical experiments are performed on the California gas distribution network using the epicenter and magnitude of the 1989 Loma Prieta earthquake as well as randomly-generated earthquakes. It is shown that the SVM and logistic regression surrogate models are able to predict network connectivity with accuracies of 99% for both methods, and are 1–2 orders of magnitude faster than using a Monte Carlo method with an exact connectivity check.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalReliability Engineering and System Safety
Volume164
DOIs
StatePublished - Aug 1 2017
Externally publishedYes

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under grant number CMMI1031318. The second author would like to acknowledge support by the project Development of Life-cycle Engineering Technique and Construction Method for Global Competitiveness Upgrade of Cable Bridges of the Ministry of Land, Infrastructure and Transport (MOLIT) of the Korean Government (Grant No. 16SCIP-B119960-01) and the Integrated Research Institute of Construction and Environmental Engineering at Seoul National University.

Keywords

  • Lifeline networks
  • Logistic regression
  • Monte Carlo simulations
  • Seismic reliability analysis
  • Support vector machine
  • Surrogate models
  • System reliability analysis

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