Micromechanics and microstructure based machine learning approach: Unveiling the role of porosity and hydrated phases on the tensile behaviour of cement pastes

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The fracture process and the mechanical performance of the cement paste rely significantly on its tensile behaviour. Machine learning methods have been recently developed to predict this behaviour based on numerous experimental measurements. Due to multi-phase heterogeneous nature of cement paste microstructure, evaluation of the role of each chemical phase on the cracking of cement paste is necessary to develop optimized binder systems. Numerous experimental tests would be necessary to determine the best cement pastes in a considered environment. Therefore, in this study, a hybrid approach based on an ensemble machine learning model and a microstructure informed micromechanical model is proposed for predicting the tensile behaviour of hydrated cement pastes. The model can be used as a quick prediction tool for tensile strength of a given microstructure or for deep analysis on the optimization of binder system. A Deep Neural Networks (DNN) model was developed by training and validating a large dataset detailing the chemical phase composition, phase volume fractions and the tensile strength of the cement pastes. This dataset covers an extensive range of Portland cements, hydration ages and water-to-cement ratios. It was developed by simulating cement thermodynamics and tensile behaviour using a damage micromechanical model; which is otherwise almost impossible to obtain through experimental means. Additionally, two generative algorithms, tabular generative adversial networks (TGAN) and conditional generative adversial networks (CTGAN), were employed for dataset augmentation as a sampling method for the sensitivity analysis. This machine learning study provides an in-depth investigation to identify the role of volume fraction of solid phases (C-S-H, ettringite, portlandite and hydrogarnet) and micro porosity (dry and filled) in affecting the tensile behaviour of hydrated cement paste. Optimization assessment of the microstructure was then performed using micromechanical analysis and damage propagation in hydrated cement paste phases and porous network.