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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sandra Cunha & Manuel Parente & Joaquim Tinoco & José Aguiar, 2024. "Leveraging Machine Learning for Designing Sustainable Mortars with Non-Encapsulated PCMs," Sustainability, MDPI, vol. 16(16), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:6775-:d:1451851
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    References listed on IDEAS

    as
    1. D'Alessandro, Antonella & Pisello, Anna Laura & Fabiani, Claudia & Ubertini, Filippo & Cabeza, Luisa F. & Cotana, Franco, 2018. "Multifunctional smart concretes with novel phase change materials: Mechanical and thermo-energy investigation," Applied Energy, Elsevier, vol. 212(C), pages 1448-1461.
    2. Kheradmand, Mohammad & Azenha, Miguel & de Aguiar, José L.B. & Castro-Gomes, João, 2016. "Experimental and numerical studies of hybrid PCM embedded in plastering mortar for enhanced thermal behaviour of buildings," Energy, Elsevier, vol. 94(C), pages 250-261.
    3. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
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