Synthetic Damage Assessment for RC Structure Based on Fuzzy Logic
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Diagnosis of damage in RC structures based on structural static response with the ANN technique
ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering
Time and Frequency Approaches to Non Destructive Testing in Concrete Pillars Using Neural Networks
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
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Typical defects exist in reinforced concrete structures include honeycomb, crack, and scaling and strength deduction for concrete, and corrosion and decreased section area for steel members. These defects are a result of many factors such as unproper construction and maintenance, overloading and environmental impact. The existing of defects certainly weakens the structures and reduces the expected life time of structures. Diagnosis and repair in time would be the responsibility of civil engineers. The purpose of this study is to develop a diagnosing model for reinforced concrete structures by using of backpropagation neural network technique to assess the severity and location of defects. Theoretical analysis of a simply-supported reinforced concrete beam in specified size (i.e., rectangular cross section and 4 meter span) by finite element program is performed to generate training and testing samples for neural network assessing task. The efficiency of the developed neural network model for the damage assessment is verified.