Advanced computational strategy for damage identification of offshore jacket platforms

Abstract

In this study, an efficient surrogate-assisted grey wolf optimizer (GWO) is presented by combining Kriging-based active learning to identify damages in jacketed platforms based on modal analysis. The use of active learning in parallel with GWO significantly reduced the number of calls to the objective function and increased the accuracy of the algorithm's search in the problem space. The proposed approach was first evaluated on four benchmark problems, and its performance was validated against original GWO, particle swarm optimization (PSO), and genetic algorithm (GA) techniques. Then, by generating artificial damage scenarios on a real jacket platform in ABAQUS software, it was evaluated for the identification of damaged members. The results indicated high accuracy in estimation and an appropriate convergence rate in solving the high-dimensional and complicated problem of damage detection of jacketed platforms. In such a way that the error rate of damage severity estimation in scenarios 1 and 2 was, on average, 3% and 5%, respectively. Meanwhile, the damage position was correctly estimated, and the call rate of the function was reduced by 50%. The efficiency of the proposed approach shows that it can be used for further works on the reliability-based design of jacket structures.