Alchapar Noelia This email address is being protected from spambots. You need JavaScript enabled to view it.1 and Correa Erica1

1Institute of Environment, Habitat and Energy (INAHE). Mendoza Scientific and Technological Centre (CCT-Mendoza). National Scientific and Technical Research Council (CONICET). Av. Ruiz Leal s/n Parque General San Martín. Mendoza - Argentina. CP


Received: December 10, 2020
Accepted: February 15, 2021
Publication Date: July 19, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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The temperature increase of a city in relation to its peripheral areas leads to the formation of an Urban Heat Island. Working on the opto-thermal properties of the building envelope is a viable mitigation strategy to reduce the temperatures of a city. Having quantitative data on energy performance allows the development of precise evaluations and the selection of the most efficient data in relation to energy consumption. The degree of efficiency of a material is calculated with an indicator called Solar Reflectance Index (SRI). Since opto-thermal properties change over time, the standard recommends obtaining the SRI level of both new and three-year-aged material (SRI3). In the present work, 80 facade claddings were evaluated to: (a) determine which qualitative variables significantly influence the SRI3 of the claddings; (b) obtain an equation that calculates the SRI3 without the need to monitor the large number of variables used for its calculation. For this, the following statistical methods were used: multifactorial ANOVA and linear regression model. In this correlational analysis, color, composition and texture were selected as independent variables. The research showed that color is the variable that significantly influences SRI3 in all the evaluated claddings. By means of the equation obtained with the regression model, the SRI3 index was predicted reaching 95% IC. These results significantly save time and simplify the process of obtaining data since it is not necessary to monitor numerous input variables to calculate the indicator.

Keywords: solar reflectance index; building materials; mathematical models


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