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Crack Identification Based on Synthetic Artificial Intelligent Technique
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 Title & Authors
Crack Identification Based on Synthetic Artificial Intelligent Technique
Sim, Mun-Bo; Seo, Myeong-Won;
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 Abstract
It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack in a structure, a method is presented in this paper which uses synthetic artificial intelligent technique, that is, Adaptive-Network-based Fuzzy Inference System(ANFIS) solved via hybrid learning algorithm(the back-propagation gradient descent and the least-squares method) are used to learn the input(the location and depth of a crack)-output(the structural eigenfrequencies) relation of the structural system. With this ANFIS and a continuous evolutionary algorithm(CEA), it is possible to formulate the inverse problem. CEAs based on genetic algorithms work efficiently for continuous search space optimization problems like a parameter identification problem. With this ANFIS, CEAs are used to identify the crack location and depth minimizing the difference from the measured frequencies. We have tried this new idea on a simple beam structure and the results are promising.
 Keywords
Crack Identification;Structure Analysis;Inverse Analysis Method;Adaptive-Network-based Fuzzy Inference System;Continuous Evolutionary Algorithm;
 Language
Korean
 Cited by
 References
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