Detection of Damage Conditions in RC Building Structures using a Technique of Artificial Neural Network and Genetic Algorithms

C.-H. Tsai (Taiwan)


Artificial Neural Network, Genetic Algorithms, Damage Detection, Reinforced Concrete Structure.


This paper recommends a damage assessment model which can efficiently detect the damage conditions in an existing reinforced concrete (RC) building structure using a technique combining an artificial neural network (ANN) and genetic algorithms (GA). A numerical example of a four-story RC building structure is presented herein to demonstrate how the proposed method will operate step by step to yield the ideal performance. According to the numerical test results, the proposed method can successfully assess the damage conditions on each floor of the presented building. Therefore, the proposed diagnostic method should be an ideal model to perform the detection work for the building structures, and will be useful in the real world.

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