Diagnosis of damage in RC structures based on structural static response with the ANN technique

Authors:
C. H. Tsai;D. S. Hsu
Affiliations:
Department of Civil Engineering, National Cheng Kung University, Tainan, Taiwan, R.O. China;Department of Civil Engineering, National Cheng Kung University, Tainan, Taiwan, R.O. China
Venue:
ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering
Year:
2001

Citing 2
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Machine learning: neural networks, genetic algorithms, and fuzzy systems

Machine learning: neural networks, genetic algorithms, and fuzzy systems
Reinforced concrete structural damage diagnosis by using artificial neural network

IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)

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Abstract

This work develops a feasible diagnostic model for reinforced concrete structures through the artificial neural network technique, based on structural static responses, to detect the damage conditions. A simply supported RC beam with a specified size and assumed defect is theoretically analyzed to produce the structural static responses. The structural responses are then combined with relative damage conditions to generate training and testing numerical examples, necessary to assess the damage to the RC structure by using the artificial neural network. On the other hands, a test sample of RC beams with various extents of artificial damage is constructed and tested to diagnose the damage condition by using well-trained neural networks. Therefore, this work successfully fabricates a feasible diagnostic model, which will be needed for real world damage assessment applications.