Social Parameter’s Role in BEM-PSO-Based Inverse Analysis for Locating Corrosion of Reinforced Concrete

Abstract

The objective of this research is to investigate the effect of social parameter on the performance of boundary element inverse analysis (BEIA) to determine the location of reinforcing steel corrosion in concrete. BEIA was constructed by integrating the Boundary Element Method (BEM) and Particle Swarm Optimization (PSO) (PSO). BEM was used to calculate the corrosion potential on the whole surface of reinforced concrete. The cost function for detecting corrosion of reinforcing steel in concrete was evaluated using PSO. BEIA was conducted with 15 electrical potential data on the surface of reinforced concrete as a reference, such as from half-cell potential measurement. Numerical simulations of reinforced concrete with a single reinforcement reveal a difference in the speed of BEIA in locating corrosion sites. The higher the social parameter value, the faster the particles tend to migrate. However, if the parameter value is too high, the number of iterations required for the particles to converge increases, which has a negative effect on locating the corrosion point. Consequently, the variation of the social parameter affects the performance of BEIA in detecting the location of corrosion in reinforced concrete.