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자료유형
학술저널
저자정보
Sirshendu Hore (Hooghly Engineering&Technology College Chinsurah) Sankhadeep Chatterjee (University of Calcutta) Sarbartha Sarkar (Hooghly Engineering & Technology College Chinsurah) Nilanjan Dey (Techno India College of Technology) Amira S. Ashour (Tanta University) Dana Bălas-Timar (Aurel Vlaicu University of Arad) Valentina E. Balas (Aurel Vlaicu University of Arad)
저널정보
국제구조공학회 Structural Engineering and Mechanics, An Int'l Journal Structural Engineering and Mechanics, An Int'l Journal Vol.58 No.3
발행연도
2016.1
수록면
459 - 473 (15page)

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Various vague and unstructured problems encountered the civil engineering/ designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.

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