Multimodality Information Fusion for Aging Pipe Strength and Toughness Estimation Using Bayesian Networks

Abstract

Accurate estimation of the mechanical property of aging pipes is critical to maintain the safety and to scheduling maintenance. Destructive testing for mechanical properties measurement is very expensive and sometime impossible. Inference methods are needed for estimating the bulk properties by multimodality surface material measurements from nondestructive testing, such as chemical composition, volume fraction and hardness. Bayesian network modeling is utilized to integrate the information from various types of surface measurements for a more accurate bulk mechanical property estimation. To improve the approximation of the actual underlying model and avoid the risk of overfitting, Bayesian model averaging (BMA) of Bayesian networks is implemented to account for Bayesian network model uncertainty. The models considered are weighted based on the posterior model probability. Markov Chain Monte Carlo sampling provides an effective way for numerically computing the marginal likelihoods, which are essential for obtaining the posterior model probabilities. The predictive performance of single best model and BMA are compared by logarithmic scoring rule. The predictive capability of the proposed method is evaluated. It is shown that the Bayesian network and model averaging approach can provide more reliable results in predicting the bulk mechanical properties of the pipelines.