## Abstract

Using a matrix-based system reliability (MSR) method, one can estimate the probabilities of complex system events by simple matrix calculations. Unlike existing system reliability methods whose complexity depends highly on that of the system event, the MSR method describes any general system event in a simple matrix form and therefore provides a more convenient way of handling the system event and estimating its probability. Even in the case where one has incomplete information on the component probabilities and/or the statistical dependence thereof, the matrix-based framework enables us to estimate the narrowest bounds on the system failure probability by linear programming. This paper presents the MSR method and applies it to a transportation network consisting of bridge structures. The seismic failure probabilities of bridges are estimated by use of the predictive fragility curves developed by a Bayesian methodology based on experimental data and existing deterministic models of the seismic capacity and demand. Using the MSR method, the probability of disconnection between each city/county and a critical facility is estimated. The probability mass function of the number of failed bridges is computed as well. In order to quantify the relative importance of bridges, the MSR method is used to compute the conditional probabilities of bridge failures given that there is at least one city disconnected from the critical facility. The bounds on the probability of disconnection are also obtained for cases with incomplete information.

Original language | English (US) |
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Pages (from-to) | 1584-1593 |

Number of pages | 10 |

Journal | Reliability Engineering and System Safety |

Volume | 93 |

Issue number | 11 |

DOIs | |

State | Published - Nov 2008 |

Externally published | Yes |

## Keywords

- Bayesian method
- Bridge network
- Complex system
- Connectivity analysis
- Fragility curve
- Importance measure
- Incomplete information
- Reliability bounds
- System reliability

## ASJC Scopus subject areas

- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering