LIU H F,LIU H T,LI L Y,et al．Prediction of compressive strength of desert sand concrete (DSC) after high temperature based on improved BP model [J].Journal of Henan Polytechnic University( Natural Science) ，doi:10.16186/j.cnki.1673-9787. 2024030006.

doi:10.16186/j.cnki.1673-9787.2024030006

Received：2024-03-03

Revised：2024-05-21

Online：2024-09-09

Prediction of compressive strength of desert sand concrete (DSC) after high temperature based on improved BP model (Online)

LIU Haifeng,LIU Haotian, LI Luoying, CHEN Xiaolong, CHE Jialing, YANG Weiwu

 ( School of Civil and Hydraulic Engineering,Ningxia University,Yinchuan, 750021,Ningxia,China)

Abstract: Objectives In order to investigate the effect of high temperature history on the compressive strength of desert sand concrete (DSC),taking into account of factors such as desert sand replacement rate (DSRR),temperature,heating rate and settling time, Methods the compressive strength test of DSC after high temperature was carried out.At the same time,X-ray diffraction (XRD) and scanning electron microscope (SEM) was used to analyze the change rule of microscopic morphology and phase composition of DSC after experiencing high temperature environment.On the basis of Back-Propagation (BP) algorithm,the particle swarm optimization (PSO) and genetic algorithm (GA) were used to train the artificial neural network (ANN) and the compressive strength prediction model of DSC after high temperature was established,which was validated by ten-fold cross-validation. Results Research results indicated that with increasing temperature,the compressive strength showed a decreasing trend, the hydration products in the specimen was decomposed greatly and the micro-cracks gradually expanded and connected with each other.The longer the settling time, the higher the compressive strength was.The faster the heating rate,the greater the failure rate of DSC was.DSC compressive strength reached the maximum value with the DSRR of 20%.The average absolute percentage error between the predicted results from the three prediction models and measured values was controlled within 8%.The higher the degree of optimisation of the model,the smaller the error range was.The predicated results from the particle swarm optimization-genetic hybrid algorithm (PSO-GA) neural network model was more accurate,with a mean squared error (RMSE) of 1.1272,a mean absolute percentage error (MAPE) of 3.98% and coefficient of determination (R²) for 28d compressive strength predication of 0.9878, Conclusions which significantly improved the accuracy of predictions for the mechanical properties of DSC after high temperature.

Key words: desert sand concrete (DSC); compressive strength; high temperature; neural network model