Since concrete is extensively studied, the design parameters are well-known in order to deliver suitable mechanical properties in the relevant application field. “Smart” sensing, self-healing, and self-sealing properties are in the spotlight and are engineered by nanomaterials addition, contributing also to materials design revolution for key industrial applications. This methodology is expected to contribute to design informatics due to the high prediction metrics, which holds promise for the transfer learning potential of these models for studying other novel cement formulations.Ĭement is considered as the most important hydraulic material in the modern construction field. Evaluation of nanomaterials inclusion in composite reinforcement using semi-supervised ML was proved feasible. This study introduces unbiased structure-property relations with ML to monitor cement durability based on cement phases nanomechanics compared to PDA, which offers a solution based on local optima of a multidimensional space solution.
In principle, ML is the appropriate science for predictive modeling, such as cement phase identification and facilitates the acquisition of precise results. Also, PDA was less informative than ML regarding information exchange and re-usability of input in design predictions. Analysis included multiple nanoindentation raw data variables, and required less time to execute than conventional single component probability density analysis (PDA). Supervised ML classification is used in order to predict the hydration products composition and 97.6% accuracy was achieved. Data labelling is performed with unsupervised ML with k-means clustering. Artificial Intelligence (AI) and semi-supervised Machine Learning (ML) were used for knowledge gain on the effect of carbon nanotubes to nanomechanics in novel cement formulations. Nanoindentation was utilized as a non-destructive technique to identify Portland Cement hydration phases.
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