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    Piezoresistive Prediction of CNTs-Embedded Cement Composites via Machine Learning Approaches

    Jinho Bang1, SongEe Park2, Haemin Jeon2,*

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1503-1519, 2022, DOI:10.32604/cmc.2022.020485

    Abstract Conductive cementitious composites are innovated materials that have improved electrical conductivity compared to general types of cement, and are expected to be used in a variety of future infrastructures with unique functionalities such as self-heating, electromagnetic shielding, and piezoelectricity. In the present study, machine learning methods that have been recently applied in various fields were proposed for the prediction of piezoelectric characteristics of carbon nanotubes (CNTs)-incorporated cement composites. Data on the resistivity change of CNTs/cement composites according to various water/binder ratios, loading types, and CNT content were considered as training values. These data were applied to numerous machine learning techniques… More >

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