Improving the utility of MIP analysis for cementitious systems through Gaussian process regression modeling to predict electrical resistivity. (February 2021)

• Record Type: Journal Article
• Authors: Tibbetts, Caitlin M.; Watts, Benjamin E.; Ferraro, Christopher C.; Huber, Ethan D.
• Is Part Of: Cement & concrete composites. Volume 116(2021)
• Publication Date: 2021-02
• Subjects: Electrical resistivity -- Mercury intrusion porosimetry (MIP) -- Chloride diffusion -- Transport properties -- Katz-Thompson -- Gaussian process regression (GPR); Composite-reinforced concrete -- Periodicals; Concrete -- Periodicals; Composite materials -- Periodicals; Composites de ciment -- Périodiques; Béton -- Périodiques; Composites -- Périodiques; Béton léger -- Périodiques; Cement composites; Composite materials; Composite-reinforced concrete; Concrete; Lightweight concrete; Periodicals; Electronic journals; 620.135
• DOI: 10.1016/j.cemconcomp.2020.103870
• Languages: English
• ISSNs: 0958-9465

Abstract

Abstract: Durability of concrete is a key design parameter and is strongly related to the penetrability of the cementitious binder matrix. This research evaluated methods to estimate the durability of concrete in service, using different procedures to characterize the penetrability of concrete, mortar, and paste. Mercury intrusion porosimetry (MIP) was compared to several standardized testing methods (surface resistivity, bulk resistivity, and bulk diffusion) on specimens made from cementitious paste, mixed mortar, sieved mortar, and concrete. MIP measurements were compared to results from standardized methods, with the goal of developing useful correlations. Gaussian process regression (GPR) modeling was applied to predict the electrical resistivity of concrete using MIP measurements obtained from mortar specimens, in combination with chemical oxide compositions of the mortar components. The resulting GPR model was compared to the Katz-Thompson method for correlating MIP data to electrical resistivity measurements. In this study, GPR modeling offered an improvement over the Katz-Thompson method and was able to predict concrete surface resistivity using porosity data obtained from mortar, providing a potentially useful tool for rapid durability screening of supplementary cementitious materials. Highlights: GPR models can predict concrete surface resistivity from mortar MIP results. GPR model was better at predicting electrical resistivity than Katz-Thompson equation. Mortar more adequately characterized concrete porosity and resistivity than paste. Non-conductive, impermeable aggregate influenced resistivity of composite materials. Differences in pozzolanic reaction were observed with MIP pore size distributions.