Evolving flexible neural tree model for portland cement hydration process

Abstract

The hydration of Portland cement is a complicated process and still not fully understood. Much effort has been accomplished over the past years to get the accurate model to simulate the hydration process. However, currently existing methods using positive derivation from the conditions for physicalchemical reaction are lack of information in real hydration data. In this paper, one model based on Flexible Neural Tree (FNT) with acceptable goodness of fit was applied to the prediction of the cement hydration process from the real microstructure image data of the cement hydration which has been obtained by Micro Computed Tomography (micro-CT) technology. Been prepared on the basis of previous research, this paper used probabilistic incremental program evolution (PIPE) algorithm to optimize the flexible neural tree structure, and particle swarm optimization (PSO) algorithm to optimize the parameters of the model. Experimental results show that this method is efficient.