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Open AccessArticle

Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification

1
RNANO Lab—Research Unit of Advanced, Composite, Nano Materials & Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR-15773 Zographos Athens, Greece
2
Innovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Nanomaterials 2020, 10(4), 645; https://doi.org/10.3390/nano10040645
Received: 11 March 2020 / Revised: 27 March 2020 / Accepted: 27 March 2020 / Published: 30 March 2020
(This article belongs to the Special Issue Characterization of Nanomaterials)
Nanoindentation was utilized as a non-destructive technique to identify Portland Cement hydration phases. 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. Data labelling is performed with unsupervised ML with k-means clustering. Supervised ML classification is used in order to predict the hydration products composition and 97.6% accuracy was achieved. Analysis included multiple nanoindentation raw data variables, and required less time to execute than conventional single component probability density analysis (PDA). Also, PDA was less informative than ML regarding information exchange and re-usability of input in design predictions. In principle, ML is the appropriate science for predictive modeling, such as cement phase identification and facilitates the acquisition of precise results. 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. Evaluation of nanomaterials inclusion in composite reinforcement using semi-supervised ML was proved feasible. 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. View Full-Text
Keywords: artificial Intelligence; machine learning; carbon nanotubes; cement microstructure; materials characterisation; nanoanalysis; nanomechanics artificial Intelligence; machine learning; carbon nanotubes; cement microstructure; materials characterisation; nanoanalysis; nanomechanics
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MDPI and ACS Style

Konstantopoulos, G.; Koumoulos, E.P.; Charitidis, C.A. Testing Novel Portland Cement Formulations with Carbon Nanotubes and Intrinsic Properties Revelation: Nanoindentation Analysis with Machine Learning on Microstructure Identification. Nanomaterials 2020, 10, 645.

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