Automated extraction of damage features through genetic programming

Dustin Y. Harvey; Michael D. Todd

doi:10.1117/12.2009739

17 April 2013 Automated extraction of damage features through genetic programming

Dustin Y. Harvey, Michael D. Todd

Proceedings Volume 8695, Health Monitoring of Structural and Biological Systems 2013; 86950J (2013) https://doi.org/10.1117/12.2009739
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2013, San Diego, California, United States

Abstract

Robust damage detection algorithms are a fundamental requirement for development of practical structural health monitoring systems. Typically, structural health-related decisions are made based on measurements of structural response. Data analysis involves a two-stage process of feature extraction and classification. While classification methods are well understood, feature design is difficult, time-consuming, and requires application experts and domain-specific knowledge. Genetic programming, a method of evolutionary computing closely related to genetic algorithms, has previously shown promise when adapted to problems involving structured data such as signals and images. Genetic programming evolves a population of candidate solutions represented as computer programs to perform a well-defined task. Importantly, genetic programming conducts an efficient search without specification of the size of the desired solution. In this study, a novel formulation of genetic programming is introduced as an automated feature extractor for supervised learning problems related to structural health monitoring applications. Performance of the system is evaluated on signal processing problems with known optimal solutions.

Citation Download Citation

Dustin Y. Harvey and Michael D. Todd "Automated extraction of damage features through genetic programming", Proc. SPIE 8695, Health Monitoring of Structural and Biological Systems 2013, 86950J (17 April 2013); https://doi.org/10.1117/12.2009739

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