Structural Engineering and Mechanics

  • 제27권2호
  • /
  • Pages.117-134
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  • 2007
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of force reduction factor (R) of prefabricated industrial buildings using neural networks

  • Arslan, M. Hakan (Selcuk University) ;
  • Ceylan, Murat (Selcuk University) ;
  • Kaltakci, Yaspr M. (Selcuk University) ;
  • Ozbay, Yuksel (Selcuk University) ;
  • Gulay, Fatma Gulten (Department of Civil Engineering, Istanbul Technical University)
  • 투고 : 2006.03.20
  • 심사 : 2007.05.04
  • 발행 : 2007.09.30
⟨ 이전 논문 다음 논문 ⟩

초록

The force (load) reduction factor, R, which is one of the most important parameters in earthquake load calculation, is independent of the dimensions of the structure but is defined on the basis of the load bearing system of the structure as defined in earthquake codes. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last three major earthquakes in Turkey (Adana 1998, Kocaeli 1999, Duzce 1999) and the experts are still discussing the main reasons of those failures. Most of them agreed that they resulted mainly from the earthquake force reduction factor, R that is incorrectly selected during design processes, in addition to all other detailing errors. Thus this wide spread damages caused by the earthquake to prefabricated structures aroused suspicion about the correctness of the R coefficient recommended in the current Turkish Earthquake Codes (TEC - 98). In this study, an attempt was made for an approximate determination of R coefficient for widely utilized prefabricated structure types (single-floor single-span) with variable dimensions. According to the selecting variable dimensions, 140 sample frames were computed using pushover analysis. The force reduction factor R was calculated by load-displacement curves obtained pushover analysis for each frame. Then, formulated artificial neural network method was trained by using 107 of the 140 sample frames. For the training various algorithms were used. The method was applied and used for the prediction of the R rest 33 frames with about 92% accuracy. The paper also aims at proposing the authorities to change the R coefficient values predicted in TEC - 98 for prefabricated concrete structures.

키워드

참고문헌

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