Damage detection in water distribution pipe network using Bayesian framework and system reliability analysis

When a natural or man-made hazard occurs, it is essential to detect damaged components in lifeline networks to enable rapid recovery of the utility service in the impacted areas. However, inspections of individual network components such as buried pipes are often impractical due to exceedingly large costs and time. This paper presents a new system reliability method using a Bayesian method developed for identifying network components with higher conditional probabilities of damage given post-disaster network flow monitoring data. This method achieves an optimal matrix-based representation of the problem for efficient damage detection. The developed method is demonstrated by a water pipeline network consisting of 15 pipelines. The conditional probabilities of damage in 15 pipelines given post-disaster network flow observations are obtained by a Bayesian method for damage detection purpose. The results of the post-disaster damage detection by the proposed system reliability method are compared to those by Monte Carlo simulations and by the matrix-based system reliability method without selective expansion scheme in order to demonstrate the accuracy and efficiency.