Shear Resistance Prediction of Post-fire Reinforced Concrete Beams Using Artificial Neural Network

• Main Authors: Bin Cai, Long-Fei Xu, Feng Fu
• Format: Article
• Language: English
• Published: SpringerOpen 2019-09-01
• Series: International Journal of Concrete Structures and Materials
• Subjects: reinforced concrete; fire; shear resistance; sectional analysis; BP neural networks
• Online Access: http://link.springer.com/article/10.1186/s40069-019-0358-8
• ISSN: 1976-0485; 2234-1315

Abstract 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.