硅酸盐通报2017，Vol.36Issue(9)：2963-2968,6.
偏高岭土高性能混凝土抗压强度预测研究
Prediction of Metakaolin High-performance Concrete Compressive Strength
摘要
Abstract
With the water-binder ratio , water content , sand rate , strength of cement , cement content and the content of metakaolin , slag and fly ash as input variables , SPSS regression equation analysis and BP neural network model based on Levenberg-Marquart algorithm were developed to predict the compressive strength of metakaolin high-performance concrete and compared with the experimental values .The results show that:compared with the SPSS regression equation analysis forecast results , predictive values of the BP neural network and the measured values have a high fitting degree , the fitting result is 0.997.Ratio between the BP predictive values and the trial values is 0.999, and standard deviation is 0.010.The maximum relative error of the predictive values less than 2.1%.The BP neural network model has high prediction accuracy , reliable prediction results , and provides guidance basis for the compressive strength prediction of metakaolin high-performance concrete .关键词
BP神经网络/SPSS线性回归/抗压强度预测Key words
BP neural network/SPSS linear regression/compressive strength prediction分类
建筑与水利引用本文复制引用
李章建,宋杨会,李世华..偏高岭土高性能混凝土抗压强度预测研究[J].硅酸盐通报,2017,36(9):2963-2968,6.基金项目
云南省科技富民强县计划项目(2014EA004) （2014EA004）
