Leveraging Machine Learning for Designing Sustainable Mortars with Non-Encapsulated PCMs

Sandra Cunha (), Manuel Parente, Joaquim Tinoco and José Aguiar
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Sandra Cunha: Centre for Territory, Environment and Construction (CTAC), Department of Civil Engineering, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal
Manuel Parente: Institute for Sustainability and Innovation in Structural Engineering (ISISE), ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, Portugal
Joaquim Tinoco: Institute for Sustainability and Innovation in Structural Engineering (ISISE), ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, Portugal
José Aguiar: Centre for Territory, Environment and Construction (CTAC), Department of Civil Engineering, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal

Sustainability, 2024, vol. 16, issue 16, 1-20

Abstract: The development and understanding of the behavior of construction materials is extremely complex due to the great variability of raw materials that can be used, which becomes even more challenging when functional materials, such as phase-change materials (PCM), are incorporated. Currently, we are witnessing an evolution of advanced construction materials as well as an evolution of powerful tools for modeling engineering problems using artificial intelligence, which makes it possible to predict the behavior of composite materials. Thus, the main objective of this study was exploring the potential of machine learning to predict the mechanical and physical behavior of mortars with direct incorporation of PCM, based on own experimental databases. For data preparation and modelling process, the cross-industry standard process for data mining, was adopted. Seven different models, namely multiple regression, decision trees, principal component regression, extreme gradient boosting, random forests, artificial neural networks, and support vector machines, were implemented. The results show potential, as machine learning models such as random forests and artificial neural networks were demonstrated to achieve a very good fit for the prediction of the compressive strength, flexural strength, water absorption by immersion, and water absorption by capillarity of the mortars with direct incorporation of PCM.

Keywords: machine learning; sustainable mortars; phase change materials; mechanical properties; physical properties (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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