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

Bin Cai,Feng Fu,Long-Fei Xu

doi:10.1186/s40069-019-0358-8

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.

Highlights

When fire occurs, reinforced concrete (RC) beams are affected by high temperatures and their mechanical properties are significantly deteriorated with the temperature rise (Felicetti et al 2009; Annerel and Taerwe 2011)
The neural network model was trained, tested and validated using the data obtained from the post-fire residual shear resistance model
The machine learning was employed to investigate the effects of beam width, beam height, stirrup sectional area, stirrup spacing, concrete tensile strength, concrete cover thickness, and fire exposure time on the shear resistance of beams

Summary

Introduction

When fire occurs, reinforced concrete (RC) beams are affected by high temperatures and their mechanical properties are significantly deteriorated with the temperature rise (Felicetti et al 2009; Annerel and Taerwe 2011). The parameters needed for computing the shear resistance of post-fire RC beams include fire exposure time, specific heat, temperature distribution, section sizes and material strength. ANN has been rarely used to study the post-fire shear resistance of RC beams. Temperature distribution of the beams was determined using the finite element analysis software ABAQUS, and the strength reduction factor of materials was detected. The post-fire shear resistance of RC beams was calculated via section equilibrium analysis. The post-fire shear resistance of RC beams was predicted using machine learning based on BP neural networks. 2.2 Temperature Distribution of Beams Under Fire When determine the shear resistance of beam under fire, only maximum temperature of beams at different locations is needed, the ISO834 fire curve (ISO 1999) was used here, and the temperature–time relation could be expressed as follows:. The temperatures at all points were marching well, indicating the temperature distribution simulated by ABAQUS were correct and effective

Tensile Strengths of Concrete and Reinforced Steel After Fire

Computation of Residual Resistance Based on Section Equilibrium

Conclusions