A structural damage detection and classification algorithm based on clone selection

Inspired by identification ability of biological immune system, damage detection and classification problem in structural health monitoring is studied based on the autonomous, adaptive and evolutional artificial immune theory and method. A structural damage detection and classification algorithm based on clone selection principle is proposed. The algorithm samples data of structure model as antigen which stimulates the antibody sets. In order to improve the quality of memory cells, the antibodies go through learning and evolving process including cloning, mutation and selection. At last memory cell sets of high quality are used to detect and classify measured data. The experiment results of the proposed algorithm using benchmark structure model show that the algorithm can identify and classify the structural patterns exactly. The parameter settings which can achieve high classification success rate are proposed on the results analysis of experiment in this paper.