Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural Network
Self-compacting mortar and concrete are high-performance building materials used in the construction industry because of their excellent rheological and mechanical properties. However, the absence of specific standards for mix design presents hindrance for researchers, motivating this study. A predi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Pouyan Press
2025-10-01
|
| Series: | Journal of Soft Computing in Civil Engineering |
| Subjects: | |
| Online Access: | https://www.jsoftcivil.com/article_203916_a0e60c52a71edec604b3b1baa9239571.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849467104301940736 |
|---|---|
| author | Andreas Kounadis Angelos Galatis Agapoula Papakonstantinou Efstratios Badogiannis |
| author_facet | Andreas Kounadis Angelos Galatis Agapoula Papakonstantinou Efstratios Badogiannis |
| author_sort | Andreas Kounadis |
| collection | DOAJ |
| description | Self-compacting mortar and concrete are high-performance building materials used in the construction industry because of their excellent rheological and mechanical properties. However, the absence of specific standards for mix design presents hindrance for researchers, motivating this study. A prediction model was developed in this study to assess the suitability of mix designs to produce robust and stable SCC with desired viscosity and yield stress characteristics. Utilizing artificial neural network technique, a powerful machine learning tool for solving complex nonlinear problems, bibliographic and experimental data on composition proportions and material properties were collected. The model architecture was optimized through multiparametric analysis, testing around 22,000 models to achieve approximately 85% prediction accuracy. The particle size distribution of fine aggregates, along with the content and specific surface area of fine filler materials, emerged as the most significant predictive variables. This model could serve as a reliable tool for researchers and industries to design self-compacting mixtures, conserving laboratory time, as well as financial and natural resources. |
| format | Article |
| id | doaj-art-b6045a8fe7a543d481b2a3eefb9fe4f3 |
| institution | Kabale University |
| issn | 2588-2872 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Pouyan Press |
| record_format | Article |
| series | Journal of Soft Computing in Civil Engineering |
| spelling | doaj-art-b6045a8fe7a543d481b2a3eefb9fe4f32025-08-20T03:34:52ZengPouyan PressJournal of Soft Computing in Civil Engineering2588-28722025-10-019412110.22115/scce.2024.415097.1711203916Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural NetworkAndreas Kounadis0Angelos Galatis1Agapoula Papakonstantinou2Efstratios Badogiannis3Ph.D. Student, School of Civil Engineering, National Technical University of Athens, NTUA, Athens, GreeceStudent, School of Civil Engineering, National Technical University of Athens, NTUA, Athens, GreeceM.Sc. Student, School of Chemical Engineering, National Technical University of Athens, NTUA, Athens, GreeceAssociate Professor, School of Civil Engineering, National Technical University of Athens, NTUA, Athens, GreeceSelf-compacting mortar and concrete are high-performance building materials used in the construction industry because of their excellent rheological and mechanical properties. However, the absence of specific standards for mix design presents hindrance for researchers, motivating this study. A prediction model was developed in this study to assess the suitability of mix designs to produce robust and stable SCC with desired viscosity and yield stress characteristics. Utilizing artificial neural network technique, a powerful machine learning tool for solving complex nonlinear problems, bibliographic and experimental data on composition proportions and material properties were collected. The model architecture was optimized through multiparametric analysis, testing around 22,000 models to achieve approximately 85% prediction accuracy. The particle size distribution of fine aggregates, along with the content and specific surface area of fine filler materials, emerged as the most significant predictive variables. This model could serve as a reliable tool for researchers and industries to design self-compacting mixtures, conserving laboratory time, as well as financial and natural resources.https://www.jsoftcivil.com/article_203916_a0e60c52a71edec604b3b1baa9239571.pdfdeep learningself-compacting mortarself-compacting concretefine filler materialviscosityyield stress |
| spellingShingle | Andreas Kounadis Angelos Galatis Agapoula Papakonstantinou Efstratios Badogiannis Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural Network Journal of Soft Computing in Civil Engineering deep learning self-compacting mortar self-compacting concrete fine filler material viscosity yield stress |
| title | Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural Network |
| title_full | Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural Network |
| title_fullStr | Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural Network |
| title_full_unstemmed | Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural Network |
| title_short | Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural Network |
| title_sort | predicting the rheological performance of self compacting mortar and concrete using artificial neural network |
| topic | deep learning self-compacting mortar self-compacting concrete fine filler material viscosity yield stress |
| url | https://www.jsoftcivil.com/article_203916_a0e60c52a71edec604b3b1baa9239571.pdf |
| work_keys_str_mv | AT andreaskounadis predictingtherheologicalperformanceofselfcompactingmortarandconcreteusingartificialneuralnetwork AT angelosgalatis predictingtherheologicalperformanceofselfcompactingmortarandconcreteusingartificialneuralnetwork AT agapoulapapakonstantinou predictingtherheologicalperformanceofselfcompactingmortarandconcreteusingartificialneuralnetwork AT efstratiosbadogiannis predictingtherheologicalperformanceofselfcompactingmortarandconcreteusingartificialneuralnetwork |