Master of Engineering Science


Civil and Environmental Engineering


Dr. Wenxing Zhou


Bayesian models are developed to calibrate the accuracies of high-resolution in-line inspection (ILI) tools for sizing metal-loss corrosion defects and to characterize the growth of individual defects on energy pipelines. Moreover, a methodology is proposed to evaluate the time-dependent system reliability of a segment of a pressurized pipeline containing multiple active corrosion defects. The calibration of ILI tools is carried out by comparing the field-measured depths and ILI-reported depths for a set of static defects. The proposed methodology is able to quantify the measurement errors of multiple ILI tools simultaneously and evaluate the correlation between random errors associated with different ILI tools. The corrosion growth model is developed in a hierarchical Bayesian framework. The depth of the corrosion defects is assumed to be a power-law function of time characterized by two power-law coefficients and the corrosion initiation time, and the probabilistic characteristics of the parameters involved in the growth model are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on ILI data collected at different times for a given pipeline. The model accounts for the constant and non-constant biases and random scattering errors of the ILI data, as well as the potential correlation between the random scattering errors associated with different ILI tools. Both the conventional Monte Carlo simulation and MCMC simulation techniques are employed in the methodology to evaluate the failure probability of the pipeline. The methodology considers three distinctive failure modes, namely small leak, large leak and rupture, and incorporates the hierarchical Bayesian power-law growth model for the depth of individual corrosion defect.