Inverse analysis using particle swarm optimization for detecting corrosion profile of rebar in concrete structure

In this paper, inverse analysis with the use of Particle Swarm Optimization (PSO) is developed for detecting the corrosion of reinforcing steel in concrete from a relatively small number of potential data measured on the concrete surface. PSO is a promising optimization method due to its simplicity of programming and comparable accuracy. In this proposed inverse analysis using PSO, corrosion profiles represent the location and size of reinforcing steel corrosion. In this method, candidate solution is modeled as a swarm of particles. The objective function, which is proportional to the cost function, is evaluated for the swarm of particles. This function is the difference between the calculated and measured potentials on the concrete surface. The calculated potentials on the surface of the concrete are obtained by solving the Laplace's equation by using the Boundary Element Method (BEM). The corroded and non-corroded parts of the reinforcing steel are represented by each polarization curve. Inverse analysis is carried out by minimizing the cost function using PSO. Examples of the numerical simulation were used to demonstrate the effectiveness of the proposed method. It shows that proposed inverse analysis had promising capability in detecting the corrosion profile of reinforcing steel in concrete.