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 development of microstructure and cement hydration. Therefore, the investigation of microstructure for cement paste enables us to understand the hydration process and to predict the physical properties. However, the unreliability of phase classification and segmentation in an image affect the description of microstructure, as well 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 features is built as the form of image feature kinetics using gene expression programming from observed microtomography images. A similarity weight tournament selection is also proposed to increase the diversity of population and improve the performance. Experimental results manifest that the evolved image feature kinetics not only perform well in fitting training data but also exhibit superior generalization ability.