Damage diagnosis and remaining life prediction of pipeline infrastructure systems is still a challenging problem despite tremendous progress made during the past several decades, such as the damage accumulation in plastic gas distribution pipes. The goal of our part of the project is to implement Bayesian network for classification via images taken inside the pipe. And develop a maintenance framework for plastic pipeline system. Various imaging processing techniques and feature extraction algorithms will be developed for the accurate representation of pipe damage. Advanced parallel computing will be developed for the automatic detection of large imaging datasets. Reliability-based optimization framework will be developed for the pipe infrastructure integrity assessment and maintenance.
