An ACO-based algorithm for structural health monitoring

Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behavior of real ant colonies. As a new exploring attempt to the structural health monitoring (SHM), the ACO algorithm is applied to the continuous optimization problems on the structural damage detection in the SHM field in this paper. First of all, the theoretical background on the ACO is introduced for the search of approximation solutions to discrete optimization problems and further to continuous optimization problems. Then four benchmark functions are used to evaluate the performance of the continuous ACO (CnACO) algorithm. After that, the problem on the structural damage detection is converted into a constrained optimization problem, which is then hopefully solved by the CnACO algorithm. Based on the numerical simulations for single and multiple damages of a 2-story rigid frame structure, some illustrated results show that the ACO-based algorithm is very effective for the structural damage detection. The algorithm can not only locate the structural damages but also quantify the severity of damages. Regardless of weak damage or multiple damages, the identification accuracy is very high and noise immunity is better, which shows that the ACO-based algorithm is feasible and effective in the SHM field.