Bayesian Model Updating for the Minimum Cross-Sectional Thickness of a Belt Conveyor Support Structure Member

Abstract Belt conveyor support structures are often exposed to corrosive environments due to dust accumulation, making them vulnerable to corrosion damage and posing significant safety risks. Particularly, the minimum cross-sectional thickness of a lower-chord member is critical for structural safety assessment, because it directly determines the member’s ultimate tensile capacity. This study presents a framework for evaluating the minimum cross-sectional thickness of such members. In this approach, the target member is divided into multiple sections, and the average thickness of each section is estimated using the transitional Markov Chain Monte Carlo (TMCMC) algorithm, based on the member’s cross-sectional modes. A linear regression model is then developed from real corroded members to relate the average cross-sectional thickness to the minimum cross-sectional thickness. Finally, the global minimum cross-sectional thickness is inferred by combining the estimated thicknesses with the regression model. Based on a numerical example, this study investigates several factors that may influence the performance of the TMCMC algorithm. It is found that extending the Markov Chain length significantly improves the identification accuracy and consistency, even when the chain number is limited. This strategy is subsequently validated on five real specimens, including a naturally corroded member. The experimental validations also indicate that the average cross-sectional thicknesses are more identifiable than the section thicknesses, exhibiting lower estimation uncertainty, error, and higher consistency. For the naturally corroded member, the proposed framework effectively estimates the damage level and location of the global minimum cross-sectional thickness, highlighting its practical applicability.