Prediction of the mechanical behavior of mortars incorporating phase change materials using data mining techniques

Nowadays, it is imperative to reduce the energy bill in order to contribute to a more sustainable planet. In this sense, the use of materials that contribute to the energy efficiency of buildings is a very important contribution to achieve this goal. Mortars incorporating phase change materials (PCM) can make an important contribution to this end, due to its thermal storage capacity, increasing the energy efficiency of buildings. In this work several mortars with different PCM contents were developed, using different binders (cement, aerial lime, hydraulic lime and gypsum). The aim of this study was to apply data mining techniques such as artificial neural networks (ANN), support vector machines (SVM) and multiple linear regressions (MLR) to forecast the compressive and flexural strengths of these mortars at different exposure temperatures. It was concluded that ANN models have the best predictive capacity both for compressive strength and flexural strength. However, the SVM models have a flexural strength forecasting capacity very close to ANN models.