Artificial immune pattern recognition for structure damage classification
Document Type
Article
Publication Date
11-2009
Department
Department of Mechanical Engineering-Engineering Mechanics; Department of Electrical and Computer Engineering
Abstract
Damage detection in structures is one of the research topics that have received growing interest in research communities. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage classification problem. This paper presents an Artificial Immune Pattern Recognition (AIPR) approach for the damage classification in structures. An AIPR-based structure damage classifier has been developed, which incorporates several novel characteristics of the natural immune system. The structure damage pattern recognition is achieved through mimicking immune recognition mechanisms that possess features such as adaptation, evolution, and immune learning. The damage patterns are represented by feature vectors that are extracted from the structure's dynamic response measurements. The training process is designed based on the clonal selection principle in the immune system. The selective and adaptive features of the clonal selection algorithm allow the classifier to evolve its pattern recognition antibodies towards the goal of matching the training data. In addition, the immune learning algorithm can learn and remember different data patterns by generating a set of memory cells that contains representative feature vectors for each class (pattern). The performance of the presented structure damage classifier has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control-American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group and a three-story frame provided by Los Alamos National Laboratory. The validation results show that the AIPR-based pattern recognition is suitable for structure damage classification. The presented research establishes a fundamental basis for the application of the AIPR concepts in the structure damage classification.
Publication Title
Computers and Structures
Recommended Citation
Chen, B.,
&
Zang, C.
(2009).
Artificial immune pattern recognition for structure damage classification.
Computers and Structures,
87(21-22), 1394-1407.
http://doi.org/10.1016/j.compstruc.2009.08.012
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/6162