LIU H F, LIU H T, LI L Y, et al.Prediction of compressive strength of desert sand concrete after high temperature based on an improved BP model[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(1):179-188.

doi:10.16186/j.cnki.1673-9787.2024030006

Received:2024/03/03

Revised:2024/05/21

Published:2025-12-03

Prediction of compressive strength of desert sand concrete after high temperature based on an improved BP model

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 To investigate the effect of high-temperature history on the compressive strength of desert sand concrete （DSC）， compressive strength tests of DSC after exposure to high temperatures were conducted by considering the effects of desert sand replacement rate （DSRR）， temperature， heating rate， and resting time. Methods X-ray diffraction （XRD） and scanning electron microscopy （SEM） were employed to analyze the changes in the microstructure and phase composition of DSC after high-temperature exposure. Based on the back-propagation （BP） algorithm， an artificial neural network （ANN） for predicting the compressive strength of DSC after high temperature was developed by integrating particle swarm optimization （PSO） and genetic algorithm （GA）. The model was validated using ten-fold cross-validation. Results The results indicated that the compressive strength of DSC decreased with increasing temperature， accompanied by significant decomposition of hydration products and progressive propagation and interconnection of microcracks. Longer resting time led to higher compressive strength， while higher heating rates accelerated the damage of DSC. The compressive strength reached its maximum when the DSRR was 20%. The mean absolute percentage errors （MAPE） of the three predictive models were all within 8%. A higher degree of model optimization resulted in smaller prediction errors. The hybrid PSO-GA neural network model achieved the highest prediction accuracy， with a root mean square error （RMSE） of 1.127 2， MAPE of 3.98%， and a determination coefficient （R²） of 0.9878 for the 28-day compressive strength. Conclusions The proposed method significantly improves the prediction accuracy of the mechanical properties of DSC after exposure to high temperature.

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