Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach
This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. This study’s novelty...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/23/10780 |
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| author | Rafael Travincas Maria Paula Mendes Isabel Torres Inês Flores-Colen |
| author_facet | Rafael Travincas Maria Paula Mendes Isabel Torres Inês Flores-Colen |
| author_sort | Rafael Travincas |
| collection | DOAJ |
| description | This study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. This study’s novelty lies in predicting the mortar’s porosity considering the substrate’s influence on which this mortar is applied. For this purpose, an experimental database comprising 1592 datapoints of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated using an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of mortar with good prediction accuracy, and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF = 0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, the bulk density and open porosity of the substrate are significant factors. Furthermore, this study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations. |
| format | Article |
| id | doaj-art-4ad53e23e462479791d82904137fe172 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4ad53e23e462479791d82904137fe1722024-12-13T16:21:42ZengMDPI AGApplied Sciences2076-34172024-11-0114231078010.3390/app142310780Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning ApproachRafael Travincas0Maria Paula Mendes1Isabel Torres2Inês Flores-Colen3Department of Materials Science, Military Institute of Engineering-IME, Praça General Tiburcio, 80, Urca, Rio de Janeiro 22290-270, BrazilCERENA, Centre of Natural Resources and Environment, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon, PortugalCERIS, Department of Civil Engineering, University of Coimbra, Rua Luís Reis Santos—Pólo II, 3030-788 Coimbra, PortugalCERIS, Department of Civil Engineering, Architecture and Environment, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, PortugalThis study aims to evaluate the potential of machine learning algorithms (Random Forest and Support Vector Machine) in predicting the open porosity of a general-use industrial mortar applied to different substrates based on the characteristics of both the mortar and substrates. This study’s novelty lies in predicting the mortar’s porosity considering the substrate’s influence on which this mortar is applied. For this purpose, an experimental database comprising 1592 datapoints of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated using an experimental program. The samples were characterized by bulk density, open porosity, capillary water absorption coefficient, drying index, and compressive strength. This database was then used to train and test the machine learning algorithms to predict the open porosity of the mortar. The results indicate that it is possible to predict the open porosity of mortar with good prediction accuracy, and that both Random Forest (RF) and Support Vector Machine (SVM) algorithms (RF = 0.880; SVM = 0.896) are suitable for this task. Regarding the main characteristics that influence the open porosity of the mortar, the bulk density and open porosity of the substrate are significant factors. Furthermore, this study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.https://www.mdpi.com/2076-3417/14/23/10780random forestsupport vector machineindustrial mortarsubstrateprediction |
| spellingShingle | Rafael Travincas Maria Paula Mendes Isabel Torres Inês Flores-Colen Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach Applied Sciences random forest support vector machine industrial mortar substrate prediction |
| title | Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach |
| title_full | Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach |
| title_fullStr | Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach |
| title_full_unstemmed | Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach |
| title_short | Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach |
| title_sort | predicting the open porosity of industrial mortar applied on different substrates a machine learning approach |
| topic | random forest support vector machine industrial mortar substrate prediction |
| url | https://www.mdpi.com/2076-3417/14/23/10780 |
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