Structural health monitoring based on continuous ACO method

Structural damage detection remains as a challenging task in the field of structural health monitoring (SHM), which has occupied many scientific communities over the last two decades. As a new exploring attempt to the SHM problem, this paper proposes an ant colony optimization (ACO) based algorithm for continuous optimization problems on structural damage detection in the SHM field. First of all, the theoretical background of ACO is introduced for search of approximation best solution to discrete optimization problems and further to continuous optimization problems. Then four benchmark functions are used to evaluate the performance of continuous ACO (CnACO) algorithm. After that, the problem on the structural damage detection is mathematically converted into a constrained optimization problem, which is then hopefully solved by the CnACO algorithm. Meanwhile, effect of measurement noise on the algorithm is considered in all the damage scenarios. Upon extensive numerical simulations for single and multiple damages of a 2-storey rigid frame structure, the proposed method is extended to four damage patterns of a building model of 3-storey steel frame structure made in laboratory for further experimental verification of the proposed method. Illustrated results show that the proposed method is very effective for the structural damage detection. Regardless of weak damage or multiple damages, the identification accuracy is very high and the noise immunity is better, which shows that the proposed method is feasible and effective in the SHM field.