Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks

Bo Chen; Chuanzhi Zang

doi:10.1117/12.815856

7 April 2009 Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks

Bo Chen, Chuanzhi Zang

Proceedings Volume 7293, Smart Sensor Phenomena, Technology, Networks, and Systems 2009; 72930K (2009) https://doi.org/10.1117/12.815856
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2009, San Diego, California, United States

Abstract

This paper presents an artificial immune pattern recognition (AIPR) approach for the damage detection and classification in structures. An AIPR-based Structure Damage Classifier (AIPR-SDC) has been developed by mimicking immune recognition and learning mechanisms. The structure 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 generate recognition feature vectors that are able to match the training data. In addition, the immune learning algorithm can learn and remember various 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 Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms.

Citation Download Citation

Bo Chen and Chuanzhi Zang "Artificial immune pattern recognition for damage detection in structural health monitoring sensor networks", Proc. SPIE 7293, Smart Sensor Phenomena, Technology, Networks, and Systems 2009, 72930K (7 April 2009); https://doi.org/10.1117/12.815856

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available