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This paper implements machine learning (ML) classification algorithms on microstructural chemical maps to predict the constituent phases. Intensities of chemical species (Ca, Al, Si, etc.), and in some cases the nanomechanical properties measured at the corresponding points, form the input to the ML model, which predicts the phase label (LD or HD C-S-H, clinker etc.) belonging to that location. Artificial neural networks (ANN) and forest ensemble methods are used for classification. Confusiondoi:10.22115/scce.2021.302400.1357 doaj:c79dd694882744c4a5afd8680a2948dc fatcat:4kxowwmhr5ag5cbeaiwknvd6ay