Florian Algarin, Luis Fernando

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    Interlacing the evaluation of mechanical properties of mortar cement with near-infrared spectroscopy using multivariate data analysis
    (2022-05-20) Florian Algarin, Luis Fernando; Suárez, O. Marcelo; College of Engineering; Calcagno, Bárbara O.; Romañach, Rodolfo J.; Department of Materials Science and Engineering; Acuña-Fernández, Edgar
    This thesis focuses on the development of near-infrared spectroscopy methods to investigate the mineralogical evolution and compressive strength resistance of Portland cement type I present in cement mortar during the hydration process. Portland cement is constituted by inorganic compounds, mainly calcium silicates and calcium aluminates. Upon contact with water, these compounds begin to hydrate and form calcium silicate hydrates (CSH) and calcium hydroxides, which are responsible for an increase in compressive strength. In this work, the cement hydration reaction was first investigated by near-infrared (NIR) spectroscopy over a period of 14, 21, 28, 90, 365, and 1095 days, with the two leading bands distinguished at approximately 7100 cm-1 and 5200 cm-1. The bands result from the combined OH stretching and bending of water molecules resulting in absorption. The second study applied principal component analysis (PCA) to the near-infrared spectra to separate and classify them into different curing periods. Well-defined clusters were identified in the score plot. The scores of the samples are lined up with the first PC. The third study developed Partial least square regression (PLS) models based on the near-infrared spectra to predict the compressive strength and obtain an alternative non-d  estructive method, PLS model 1 got an average coefficient of variation of 1.28 % for periods 14,21,28 and 90, and PLS model 2 got an average coefficient of variation of 0.47 % for periods 90 and 365 days. The developed models have a high relationship between the predicted values of compressive strength and the reference values of compressive strength. The r2 values for the PLS models are more significant than 0.990, which means that the behavior of the dependent variable is primarily explained by the behavior of the independent variable under study. Keywords: Near-infrared spectroscopy (NIRS); Cement hydration reaction; Compressive strength; Principal component analysis (PCA); Partial least square regression (PLS).