Unsupervised structure damage classification based on the data clustering and artificial immune pattern recognition

Document Type

Conference Proceeding

Publication Date



This paper presents an unsupervised structure damage classification algorithm based on the data clustering technique and the artificial immune pattern recognition. The presented method uses time series measurement of a structure's dynamic response to extract damage-sensitive features for the structure damage classification. The Data Clustering (DC) technique is employed to cluster training data to a specified number of clusters and generate the initial memory cell set. The Artificial Immune Pattern Recognition (AIPR) algorithms are integrated with the data clustering algorithms to provide a mechanism for the evolution of memory cells. The combined DC-AIPR method has been tested using a benchmark structure. The test results show the feasibility of using the DC-AIPR method for the unsupervised structure damage classification. © 2009 Springer.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)