17 April 2013 Automated extraction of damage features through genetic programming
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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.
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Dustin Y. Harvey, Dustin Y. Harvey, Michael D. Todd, 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); doi: 10.1117/12.2009739; https://doi.org/10.1117/12.2009739
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