Mechanical properties prediction of geopolymer concrete subjected to high temperature by BP neural network

Abstract

High temperatures can deteriorate the mechanical properties of geopolymer buildings. Rapid prediction of the residual mechanical properties and failure mode of geopolymer concrete (GPC) after thermal treatment is important to ensure the safety of buildings. In this study, a method combining BP neural network with numerical simulation is proposed to predict the residual mechanical properties and failure mode of GPC subjected to high temperature simultaneously. 15 GPC specimens with 65 % aggregate content were prepared by mixing metakaolin-based geopolymer and limestone aggregates. These GPC specimens were divided into 5 groups and heated at 25 °C, 200 °C, 400 °C, 600 °C and 800 °C, respectively. The uniaxial compression tests were conducted on the GPC after thermal treatment. The variation in the peak stress, peak strain and elastic modulus of GPC with temperature was further analyzed. The prediction method by combining the BP neural network with numerical simulation was constructed to predict the mechanical properties and failure mode of GPC subjected to high temperature. The reliability of the prediction method was verified by comparing the predicted results and the experimental results. The results show that as the temperature increases, the peak stress and elastic modulus decrease and the peak strain increases, showing a deterioration of the mechanical properties of GPC after thermal treatment. The prediction method combining the artificial neural networks and numerical simulation has high accuracy, with prediction errors of less than 10 % for peak stress and less than 20 % for peak strain and elastic modulus. The results also show that under uniaxial compression load, the predicted failure mode of GPC after thermal treatment is consistent with the experiment results. The method proposed in this study can simultaneously predict the mechanical properties and failure mode of GPC with low error, which have potential application prospects in building safety assessment.