Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach
Year: 2024
Type: article
Abstract: 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.
Source: Applied Sciences
DOI: 10.3390/app142310780
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Awards: G1516346500, G1763632427, G1982436498, Estudo experimental e numérico da interface argamassa-suporte, G3840324753, Centro de Recursos Naturais e Ambiente, G5224927334, Fatores de influência no comportamento de argamassas aplicadas em diferentes suportes, G5454278309, Instituto de Investigação e Inovação em Engenharia Civil para a Sustentabilidade, G6997658380, G7992351835
OpenAlex ID: W4404595647