摘要
The early-age hydration of Portland cement paste has an important impact on the formation of microstructure and development of strength.However,manual derivation of hydration kinetic equation is very difficult because there are multi-phased,multi-sized and interrelated complex chemical and physical reactions during cement hydration.In this paper,early-age hydration kinetic equation is reversely extracted automatically from the observed time series of hydration degree of Portland cement using evolutionary computation method that combines gene expression programming and particle swarm optimization algorithms.In order to reduce the computing time,GPUs are used for acceleration in parallel.Studies have shown that according to the extracted kinetic equation,simulation curve of early-age hydration is in good accordance with the observed experimental data.Furthermore,this equation still has a good generalization ability even changing chemical composition,particle size and curing conditions.
The early-age hydration of Portland cement paste has an important impact on the formation of microstructure and development of strength.However,manual derivation of hydration kinetic equation is very difficult because there are multi-phased,multi-sized and interrelated complex chemical and physical reactions during cement hydration.In this paper,early-age hydration kinetic equation is reversely extracted automatically from the observed time series of hydration degree of Portland cement using evolutionary computation method that combines gene expression programming and particle swarm optimization algorithms.In order to reduce the computing time,GPUs are used for acceleration in parallel.Studies have shown that according to the extracted kinetic equation,simulation curve of early-age hydration is in good accordance with the observed experimental data.Furthermore,this equation still has a good generalization ability even changing chemical composition,particle size and curing conditions.
作者
WANG Lin1,YANG Bo2,ZHAO XiuYang1,2,CHEN YueHui2 & CHANG Jun3 1 School of Computer Science and Technology,Shandong University,Jinan 250101,China
2 Provincial Key Laboratory for Network-based Intelligent Computing,University of Jinan,Jinan 250022,China
3 School of Material Science and Engineering,University of Jinan,Jinan 250022,China
基金
supported by the National Natural Science Foundation of China (Grant Nos.60873089,60573065)
the Provincial Natural Science Foundation for Outstanding Young Scholars of Shandong (Grant No.JQ200820)
the Program for New Century Excellent Talents in University (Grant No.NCET-10-0863)
