Title: Shear Resistance Prediction of Post-fire Reinforced Concrete Beams Using Artificial Neural Network
Author(s): Bin Cai, Long-Fei Xu and Feng Fu
Appears on pages(s):
Keywords: reinforced concrete, ﬁre, shear resistance, sectional analysis, BP neural networks
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.