Building Image Feature Kinetics for Cement Hydration Using Gene Expression Programming With Similarity Weight Tournament Selection


The physical properties of cement are strongly influenced by the event of microstructure and cement hydration. So, the investigation of microstructure for cement paste enables us to understand the hydration method and to predict the physical properties. However, the unreliability of section classification and segmentation in a picture affect the description of microstructure, likewise as the prediction of properties and the simulation of hydration. This paper studies the dynamic relationship between microstructure and physical properties from the image itself. The relationship between compressive strength and microstructure image options is constructed as the form of image feature kinetics using gene expression programming from observed microtomography pictures. A similarity weight tournament choice is also proposed to increase the variety of population and improve the performance. Experimental results manifest that the evolved image feature kinetics not solely perform well in fitting training data but also exhibit superior generalization ability.

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