Keywords: reinforced concrete structure, non-destructive test, damage diagnosis, neural network, static displacement.

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.

1

Adeli, H. and Hung, S. L., "Machine learning: neural networks, genetic algorithms, and fuzzy systems", John Wiley & Sons, New York, 1995.

2

Garrett, J. H. Jr., Ghaboussi, J., Xu, X., and Ranjithan, S., "Neural Networks and Their Application to Civil Engineering," Expert Systems in Civil Engineering- Knowledge Representation. R. H. Allen, ed., Expert Systems Committee of ASCE. 1992.

3

Hajela, P. and Berke, L., "Neurobiological computational models in structural analysis and design," Computers and Structures, 41, 1991. doi:10.1016/0045-7949(91)90178-O

4

Yao, G. C., Chang, K. C., and Lee, G. C., "Damage diagnosis of steel frames using vibrational signature analysis, "J. Engineering. Mechanical, ASCE, 1l8(9),1949-1961 , 1992. doi:10.1061/(ASCE)0733-9399(1992)118:9(1949)

5

Chen, H. M., Tsai, K. H., Qi, G. J., Yang, J. C. S., and Amini, F., "Neural network for structural control." , J. of Computing in Civil Engineering, 9 (2), 168-176, 1995. doi:10.1061/(ASCE)0887-3801(1995)9:2(168)

6

Yeh, Y. C., "Modeling concrete strength with augment-neuron networks," J. of Materials in Civil Engineering, 10 (4), 263-268, 1998. doi:10.1061/(ASCE)0899-1561(1998)10:4(263)

7

Tsai, C. H. and Hsu, D. S., "Reinforced Concrete Structural Damage Diagnosis by Using Artificial Neural Network," Proceedings of Intelligent Information System, Dec. Bahamas, 149-155, 1997.

8

Tsai, C. H. and Hsu, D. S., "Damage Diagnosis of Existing Reinforced Concrete Structures", Proceedings of The Fifth International Conference on The Application of Artificial Intelligence to Civil and Structural Engineering, Edinburgh, 85-92., 1999.

9

Tsai, C. H. and Hsu, D. S. , "Diagnosis of RC Structural Damage Based on Displacement Time History Using the Back-Propagation Neural Network Technique", Journal of Computing in Civil Engineering, ASCE, accepted to be published.

10

Hsu, D. S., and Ju, S. H., "Micro SAP-Finite Element Structural Analysis Package." Unalis Corporation, 1988.

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