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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Structural Engineering and Mechanics, An Int'l Journal Structural Engineering and Mechanics, An Int'l Journal Vol.57 No.4
- 발행연도
- 2016.1
- 수록면
- 657 - 680 (24page)
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초록· 키워드
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Reinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum R2≈0.97). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.
목차
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참고문헌 (32)
참고문헌 신청
2005 / ACI318-05, Building Code Requirements for Structural Concrete and Commentary / American Concrete Institute
2014 / ACI318-14, Building Code Requirements for Structural Concrete and Commentary / American Concrete Institute
Aguilar, G. / 2002 / Experimental evaluation of design procedures for shear strength of deep reinforced concrete beams / ACI Struct. J 99(4):539~548
Akbas, B. / 2006 / A neural network model to assess the hysteretic energy demand in steel moment resisting frames / Struct. Eng. Mech 23(2):177~193
Anderson, N.S. / 1989 / Detailing of stirrup reinforcement / ACI Struct. J 86(5):507~515
2014 / ACI318-14, Building Code Requirements for Structural Concrete and Commentary / American Concrete Institute
Aguilar, G. / 2002 / Experimental evaluation of design procedures for shear strength of deep reinforced concrete beams / ACI Struct. J 99(4):539~548
Akbas, B. / 2006 / A neural network model to assess the hysteretic energy demand in steel moment resisting frames / Struct. Eng. Mech 23(2):177~193
Anderson, N.S. / 1989 / Detailing of stirrup reinforcement / ACI Struct. J 86(5):507~515
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