Abstract
Generally, chemical compositions and physical properties such as the contents of clinkers, surface area and particle size distribution are used to estimate cement compressive strength (CCS). However, the conventional approaches are limited by the high complexity of physical changes and chemical reactions during cement hydration, which perform poorly facing the complex and uncertain evolution of cement paste. Considering that the cement microstructure can directly reflect the state of cement hydration and the information related to CCS at microscale, microtomography, which can image threedimensional microstructure of cement paste, offers scientists another way to study CCS. Moreover, it enables us to understand the formation mechanism of the physical properties of cement and helps design high-performance materials. This paper studies the relationship between cement microstructure and compressive strength using fuzzy neural networks. The fuzzy relationships between CCS and microstructural image features are built as the "if-then" format using the polynomial-based radial basis function networks with Gaussian mixture model (GMM-PRBFNNs) from microtomography images. The GMM-PRBFNNs are proposed to improve the performance by using GMM to generate membership functions for constructing the premise of the fuzzy rules. Moreover, four types of polynomials such as constant, linear, quadratic and modified quadratic are considered as the weights between the hidden layer and the output layer for the consequent of fuzzy rules. Experimental results manifest that the built fuzzy relationships not only perform well in approximating CCS but are also easy to comprehend. (c) 2021 Published by Elsevier B.V.