Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study
Lately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The impo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/16/14/4977 |
| _version_ | 1797588518769262592 |
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| author | Vitor Pereira Silva Ruan de Alencar Carvalho João Henrique da Silva Rêgo Francisco Evangelista |
| author_facet | Vitor Pereira Silva Ruan de Alencar Carvalho João Henrique da Silva Rêgo Francisco Evangelista |
| author_sort | Vitor Pereira Silva |
| collection | DOAJ |
| description | Lately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The importance of discussing its use in the Brazilian context is inserted in an international context in which this methodology is already being applied, and it is necessary to verify the applicability of these techniques with national databases or what is created from national input data. In this research, one of these techniques, an artificial neural network (ANN), is used to determine the compressive strength of conventional Brazilian concrete at 7 and 28 days by using a database built through publications in congresses and academic works and comparing it with the reference database of Yeh. The data were organized into nine variables in which the data samples for training and test sets vary in five different cases. The eight possible input variables were: consumption of cement, blast furnace slag, pozzolana, water, additive, fine aggregate, coarse aggregate, and age. The response variable was the compressive strength of the concrete. Using international data as a training set and Brazilian data as a test set, or vice versa, did not show satisfactory results in isolation. The results showed a variation in the five scenarios; however, when using the Brazilian and the reference data sets together as test and training sets, higher R<sup>2</sup> values were obtained, showing that in the union of the two databases, a good predictive model is obtained. |
| first_indexed | 2024-03-11T00:53:12Z |
| format | Article |
| id | doaj.art-c734e5f81a754dc4a5b29e1786180f19 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-11T00:53:12Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-c734e5f81a754dc4a5b29e1786180f192023-11-18T20:16:02ZengMDPI AGMaterials1996-19442023-07-011614497710.3390/ma16144977Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset StudyVitor Pereira Silva0Ruan de Alencar Carvalho1João Henrique da Silva Rêgo2Francisco Evangelista3Department of Civil and Environmental Engineering, SG-12, University of Brasília (UnB), Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, SG-12, University of Brasília (UnB), Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, SG-12, University of Brasília (UnB), Brasilia 70910-900, BrazilDepartment of Civil and Environmental Engineering, SG-12, University of Brasília (UnB), Brasilia 70910-900, BrazilLately, several machine learning (ML) techniques are emerging as alternative and efficient ways to predict how component properties influence the properties of the final mixture. In the area of civil engineering, recent research already uses ML techniques with conventional concrete dosages. The importance of discussing its use in the Brazilian context is inserted in an international context in which this methodology is already being applied, and it is necessary to verify the applicability of these techniques with national databases or what is created from national input data. In this research, one of these techniques, an artificial neural network (ANN), is used to determine the compressive strength of conventional Brazilian concrete at 7 and 28 days by using a database built through publications in congresses and academic works and comparing it with the reference database of Yeh. The data were organized into nine variables in which the data samples for training and test sets vary in five different cases. The eight possible input variables were: consumption of cement, blast furnace slag, pozzolana, water, additive, fine aggregate, coarse aggregate, and age. The response variable was the compressive strength of the concrete. Using international data as a training set and Brazilian data as a test set, or vice versa, did not show satisfactory results in isolation. The results showed a variation in the five scenarios; however, when using the Brazilian and the reference data sets together as test and training sets, higher R<sup>2</sup> values were obtained, showing that in the union of the two databases, a good predictive model is obtained.https://www.mdpi.com/1996-1944/16/14/4977concrete strengthmachine learningpredictionartificial neural networksconcretePortland cement |
| spellingShingle | Vitor Pereira Silva Ruan de Alencar Carvalho João Henrique da Silva Rêgo Francisco Evangelista Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study Materials concrete strength machine learning prediction artificial neural networks concrete Portland cement |
| title | Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study |
| title_full | Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study |
| title_fullStr | Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study |
| title_full_unstemmed | Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study |
| title_short | Machine Learning-Based Prediction of the Compressive Strength of Brazilian Concretes: A Dual-Dataset Study |
| title_sort | machine learning based prediction of the compressive strength of brazilian concretes a dual dataset study |
| topic | concrete strength machine learning prediction artificial neural networks concrete Portland cement |
| url | https://www.mdpi.com/1996-1944/16/14/4977 |
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