Structural Engineering and Mechanics

  • 제58권3호
  • /
  • Pages.459-473
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  • 2016
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Neural-based prediction of structural failure of multistoried RC buildings

  • Hore, Sirshendu (Department of Computer Science&Engineering, Hooghly Engineering&Technology College Chinsurah) ;
  • Chatterjee, Sankhadeep (Department of Computer Science & Engineering, University of Calcutta) ;
  • Sarkar, Sarbartha (Department of Civil Engineering, Hooghly Engineering & Technology College Chinsurah) ;
  • Dey, Nilanjan (Department of Information Technology, Techno India College of Technology) ;
  • Ashour, Amira S. (Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University) ;
  • Balas-Timar, Dana (Faculty of Educational Sciences, Psychology and Social Sciences, Aurel Vlaicu University of Arad) ;
  • Balas, Valentina E. (Faculty of Engineering, Aurel Vlaicu University of Arad)
  • 투고 : 2015.09.19
  • 심사 : 2016.02.13
  • 발행 : 2016.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

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