[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125939-en":3,"doc-seo-125939-105":31,"detail-sidebar-cat-0-en-105":97},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125939,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Leveraging Machine Learning for Designing Sustainable Mortars with Non-Encapsulated PCMs","Construction materials research faces substantial complexity due to wide variability in raw inputs, which becomes even more difficult when functional materials like phase-change materials (PCMs) are incorporated. Advances in artificial intelligence enable predictive modeling of composite material behavior. This study leverages machine learning to predict the mechanical and physical performance of mortars with direct PCM incorporation using experimental databases. Data mining followed an industry standard workflow, and seven models were evaluated. Results indicate strong predictive fit for compressive strength, flexural strength, and water absorption.","sustainability   \nArticle  \nLeveraging Machine Learning for Designing Sustainable Mortars with Non-Encapsulated PCMs  \nSandra Cunha 1, *, Manuel Parente 2, Joaquim Tinoco 2 and José Aguiar 1  \nCitation: Cunha, S.; Parente, M.; Tinoco, J.; Aguiar, J. Leveraging Machine Learning for Designing Sustainable Mortars with NonEncapsulated PCMs. Sustainability 2024, 16, 6775. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/su16166775](10.3390/su16166775)  \nAcademic Editor: Muhammad Junaid Munir  \nReceived: 18 June 2024  \nRevised: 3 August 2024  \nAccepted: 5 August 2024  \nPublished: 7 August 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Centre for Territory, Environment and Construction (CTAC), Department of Civil Engineering, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal; [aguiar@civil.uminho.pt](aguiar@civil.uminho.pt)  \n2 Institute for Sustainability and Innovation in Structural Engineering (ISISE), ARISE, Department of Civil Engineering, University of Minho, 4800-058 Guimarães, Portugal; [map@civil.uminho.pt](map@civil.uminho.pt) (M.P.); [jtinoco@civil.uminho.pt](jtinoco@civil.uminho.pt) (J.T.)  \n* Correspondence: [sandracunha@civil.uminho.pt](sandracunha@civil.uminho.pt)  \nAbstract: 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.  \nKeywords: machine learning; sustainable mortars; phase change materials; mechanical properties; physical properties  \n1. Introduction  \nThe development of construction materials is extremely complex due to the enormous amount of different raw materials that constitute them and the influence that these have on their properties. If functional materials are added, the degree of complexity increases significantly, as these can largely influence their basic properties and play a leading role in their performance in buildings. Thus, it becomes essential to resort to techniques that help us in decision-making during the formulation and development of new and advanced construction materials.  \nPhase-change materials (PCM) incorporated into construction materials are still a developing area, confirmed by the increasing number of scientific publications on this subject in different topics. Also, in the construction industry, PCM has been attracting enormous interest from the scientific community, once again related","cbCainqWnkpCWZZV","https://ap.wps.com/l/cbCainqWnkpCWZZV","pdf",2052523,5,1,20,"English","en",105,"# Introduction\n# Materials and Methods\n## Data Preparation and Modeling\n## Machine Learning Models\n# Results and Discussion\n# Conclusions","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To explore how machine learning can predict the mechanical and physical behavior of mortars with direct incorporation of phase-change materials (PCMs) using experimental databases.\"},{\"question\":\"How was the data used for modeling?\",\"answer\":\"The authors used a cross-industry standard process for data mining to prepare data and guide the modeling workflow before evaluating multiple machine learning approaches.\"},{\"question\":\"Which machine learning models were implemented?\",\"answer\":\"Seven models were tested: multiple regression, decision trees, principal component regression, extreme gradient boosting, random forests, artificial neural networks, and support vector machines.\"},{\"question\":\"What properties did the models predict and how accurate were they?\",\"answer\":\"The models were used to predict compressive strength, flexural strength, water absorption by immersion, and water absorption by capillarity, with random forests and artificial neural networks showing very good fits in the reported results.\"}]","Leveraging Machine Learning for Designing Sustainable Mortars with Non-Encapsulated PCMs | 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is the main goal of the study?","Question",{"text":77,"@type":78},"To explore how machine learning can predict the mechanical and physical behavior of mortars with direct incorporation of phase-change materials (PCMs) using experimental databases.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How was the data used for modeling?",{"text":82,"@type":78},"The authors used a cross-industry standard process for data mining to prepare data and guide the modeling workflow before evaluating multiple machine learning approaches.",{"name":84,"@type":75,"acceptedAnswer":85},"Which machine learning models were implemented?",{"text":86,"@type":78},"Seven models were tested: multiple regression, decision trees, principal component regression, extreme gradient boosting, random forests, artificial neural networks, and support vector machines.",{"name":88,"@type":75,"acceptedAnswer":89},"What properties did the models predict and how accurate were they?",{"text":90,"@type":78},"The models were 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