An Optimization Procedure for Improving the Prediction Performance of Failure Assessment Model

Improving the Prediction Performance (PP) of crack pipeline Failure Assessment Model (FAM) is of great significance for the safety of pipeline structure and engineering. However, conventional optimizations for PP always focus on either safety or accuracy, failing to balance the overall requirements of structural applications. Therefore, this paper proposes an optimization procedure for comprehensively improving FAM’s PP. The establishment of the procedure can be divided into three parts: 1. setting a rational and robust optimization target, where the Improved Guo-Ni Model (IGNM) is raised to provide an absolute score s for fully quantifying FAM’s PP in terms of the multi-dimensional performances, including stability and Distributional Location Characterizations (DLCs) of FAM’s prediction results; 2. determining the candidate solutions which are selected as the Critical Safety Factor (CSF) values related to FAM’s prediction confidence level (R1) in this paper; 3. constructing the optimization framework based on the Particle Swarm Optimization algorithm to search for the optimal CSF (OCSF) that can maximize s. Finally, empirical verification results show that the procedure enhances the overall s values of BS 7910:2019 and CorLAS models by 3.32% and 6.09%, respectively, through balancing DLCs, which increases the applicability of FAM across different projects and provides a new approach for the optimization control of FAM’s overall performance.