Version 1
: Received: 29 September 2024 / Approved: 30 September 2024 / Online: 30 September 2024 (04:45:03 CEST)
How to cite:
Travincas, R.; Mendes, M. P.; Torres, I.; Flores-Colen, I. Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach. Preprints2024, 2024092357. https://doi.org/10.20944/preprints202409.2357.v1
Travincas, R.; Mendes, M. P.; Torres, I.; Flores-Colen, I. Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach. Preprints 2024, 2024092357. https://doi.org/10.20944/preprints202409.2357.v1
Travincas, R.; Mendes, M. P.; Torres, I.; Flores-Colen, I. Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach. Preprints2024, 2024092357. https://doi.org/10.20944/preprints202409.2357.v1
APA Style
Travincas, R., Mendes, M. P., Torres, I., & Flores-Colen, I. (2024). Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach. Preprints. https://doi.org/10.20944/preprints202409.2357.v1
Chicago/Turabian Style
Travincas, R., Isabel Torres and Inês Flores-Colen. 2024 "Predicting the Open Porosity of Industrial Mortar Applied on Different Substrates: A Machine Learning Approach" Preprints. https://doi.org/10.20944/preprints202409.2357.v1
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. For this purpose, an experimental database comprising 1592 samples of industrial mortar applied to five different substrates (hollowed ceramic brick, solid ceramic brick, concrete block, concrete slab, and lightweight concrete block) was generated by 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 the 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, bulk density and the open porosity of the substrate are significant factors. Furthermore, the study employs a straightforward methodology with a machine learning no-code platform, enhancing the replicability of its findings for future research and practical implementations.
Keywords
random forest; support vector machine; industrial mortar; substrate; prediction
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
