A hybrid immune model for unsupervised structural damage pattern recognition

This paper presents an unsupervised structural damage pattern recognition approach based on the fuzzy clustering and the artificial immune pattern recognition (AIPR). The fuzzy clustering technique is used to initialize the pattern representative (memory cell) for each data pattern and cluster training data into a specified number of patterns. To improve the quality of memory cells, the artificial immune pattern recognition method based on immune learning mechanisms is employed to evolve memory cells. The presented hybrid immune model (combined with fuzzy clustering and the artificial immune pattern recognition) has been tested using a benchmark structure proposed by the IASC–ASCE (International Association for Structural Control–American Society of Civil Engineers) Structural Health Monitoring Task Group. The test results show the feasibility of using the hybrid AIPR (HAIPR) method for the unsupervised structural damage pattern recognition.

Research highlights
► AR-model-based feature vectors are able to represent structural damage patterns.
► AIPR improves the quality of memory cells through memory cell evolution.
► MCRT impacts pattern recognition success rate and the number of memory cells.