Comparison of GA and PSO in Boundary Element Inverse Analysis for Rebar Corrosion Detection

This paper presents the comparison of the two optimization methods, particle swarm optimization (PSO) and genetic algorithm (GA) in boundary element inverse analysis that applied to detect the corrosion location of rebar in the concrete. This comparison focuses at analyzing the performance of both methods in reaching the global optimum, considering that both heuristics are based on population search techniques. The model of 2-dimension rectangular reinforced concrete was used as a case example to compare both methods in boundary element inverse analysis. The boundary element inverse analysis was developed by combining Boundary Element Method (BEM) and PSO or GA. The inverse analysis is carried out by means of minimizing a cost function. The cost function is a residual between the calculated and measured potentials on the concrete surface. The calculated potentials are obtained by solving the Laplace's equation using BEM. The GA or PSO is used to minimize the cost function. Thus, the corrosion location of reinforcing steel in concrete can be detected. The numerical simulation results showed that one of PSO or GA can be used for the inverse analysis for detecting rebar corrosion by combining with BEM. However, it shows that PSO seem numerically superior compared to GA in term of consistency and accuracy in finding global optimum solution for such a problem.