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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국콘크리트학회 International Journal of Concrete Structures and Materials International Journal of Concrete Structures and Materials Vol.13 No.6
- 발행연도
- 2019.9
- 수록면
- 755 - 767 (13page)
이용수
초록· 키워드
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In this paper, a prediction method based on artificial neural network was developed to rapidly determine the residual shear resistance of reinforced concrete (RC) beams after fire. Firstly, the temperature distribution along the beam section was determined through finite element analysis using software ABAQUS. A residual shear strength calculation model was developed and validated using the test data. Using this model, 384 data entries were derived for training and testing. The input layer of neural network involved parameters of beam height, beam width, fire exposure time, cross-sectional area of stirrup, stirrup spacing, concrete strength, and concrete cover thickness. The output was the shear resistance of RC beams. It was found that use of BP neural network could precisely predict the post-fire shear resistance of RC beams. The predicted data were highly consistent with the target data. Thus, this is a novel method for computing post-fire shear resistance of RC beams. Using this new method, further investigation was also made on the effects of different parameters on the shear resistance of the beams.
목차
Abstract
1. Introduction
2. Post-fire Shear Resistance Reduction Model
3. Artificial Neural Network (ANN)
4. Shear Resistance Predicted by BP Neural Networks
5. Prediction Results and Discussion
6. Parametrical Study on Shear Resistance Using Machine Learning
7. Conclusions
References
참고문헌 (39)
참고문헌 신청
Abbasi, A., & Hogg, P. J. (2005). Prediction of the failure time of glass fiber reinforced plastic reinforced concrete beams under fire conditions. Journal of Composites for Construction, 9(5), 450?457.
Annerel, E., & Taerwe, L. (2011). Evolution of the strains of traditional and selfcompacting concrete during and after fire. Materials and Structures, 44(8), 1369?1380.
Bengar, H. A., Abdollahtabar, M., & Shayanfar, J. (2016). Predicting the ductility of RC beams using nonlinear regression and ANN. Iranian Journal of Science & Technology Transactions of Civil Engineering, 40(4), 1?14.
BSI. (2004). BS EN 1992-1-2:2004, Eurocode 2?Design of concrete structures-part 1?2: General rules-structural fire design. Structural fire design. London: BSI.
BSI. (2013). BS ENV 1994-1-2, Eurocode 4?Design of composite steel and concrete structures?part 1?2: General rules. Structural fire design. London: BSI.
Annerel, E., & Taerwe, L. (2011). Evolution of the strains of traditional and selfcompacting concrete during and after fire. Materials and Structures, 44(8), 1369?1380.
Bengar, H. A., Abdollahtabar, M., & Shayanfar, J. (2016). Predicting the ductility of RC beams using nonlinear regression and ANN. Iranian Journal of Science & Technology Transactions of Civil Engineering, 40(4), 1?14.
BSI. (2004). BS EN 1992-1-2:2004, Eurocode 2?Design of concrete structures-part 1?2: General rules-structural fire design. Structural fire design. London: BSI.
BSI. (2013). BS ENV 1994-1-2, Eurocode 4?Design of composite steel and concrete structures?part 1?2: General rules. Structural fire design. London: BSI.
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