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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 74
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO CIVIL AND STRUCTURAL ENGINEERING
Edited by: B.H.V. Topping and B. Kumar
Paper 15

Diagnosis of Damage in RC Structures Based on Structural Static Response with the ANN Technique

C.H. Tsai and D.S. Hsu

Department of Civil Engineering, National Cheng Kung University, Tainan, Taiwan, R.O. China

Full Bibliographic Reference for this paper
C.H. Tsai, D.S. Hsu, "Diagnosis of Damage in RC Structures Based on Structural Static Response with the ANN Technique", in B.H.V. Topping, B. Kumar, (Editors), "Proceedings of the Sixth International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 15, 2001. doi:10.4203/ccp.74.15
Keywords: reinforced concrete structure, non-destructive test, damage diagnosis, neural network, static displacement.

Summary
This work develops a feasible diagnostic model for reinforced concrete (RC) structures through the artificial neural network (ANN) technique, based on structural static responses (i.e., static displacement, SD), to detect the damage conditions. The study aims one of series of approaches in which are investigated and applied to structural damage diagnosis according to various responses. A simply supported RC beam with a specified size and assumed defects is theoretically analyzed by a finite element program to produce the structural 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 ANN. Two stages of diagnostic procedure are then used for the ANN application to identify the damage scenarios of the relevant structures.

On the other hand, structural responses are measured from tests that also try to demonstrate the ANN base diagnostic model as presented herein, and whether it can be successfully applied to real structures. A test sample of RC beams with various extents of artificial damage is constructed and tested to diagnose the magnitude and location of damage by using well-trained neural networks. Therefore, this study successfully fabricates a feasible and efficient diagnostic model, which will be needed for real world damage assessment applications.

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