Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete
The determination of the concrete compressive strength remains a challenging task in the concrete industry. Machine learning (ML) algorithms offer an alternative and this study presents a comparative analysis of five ML regression models; Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sakarya University
2024-08-01
|
| Series: | Sakarya University Journal of Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/3643078 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849724188131065856 |
|---|---|
| author | Omobolaji Opafola Abisola Olayiwola Ositola Osifeko Adekunle David Ajibola Oyedejı |
| author_facet | Omobolaji Opafola Abisola Olayiwola Ositola Osifeko Adekunle David Ajibola Oyedejı |
| author_sort | Omobolaji Opafola |
| collection | DOAJ |
| description | The determination of the concrete compressive strength remains a challenging task in the concrete industry. Machine learning (ML) algorithms offer an alternative and this study presents a comparative analysis of five ML regression models; Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Linear Regression (LR) on a dataset of 1030 concrete samples. The findings indicate that the GB model achieved the best performance. The developed GB model achieved R-squared values of 91.60%, 91.43%, and 90.18% for the 10-fold, 5-fold, and 3-fold cross-validations, respectively, with mean absolute error, root mean squared error, and mean absolute percentage error values of 2.6776, 4.3523, and 9.19%, respectively. The GB model trained and evaluated was deployed to a web application using Streamlit for real-time prediction of the concrete compressive strength. The results of this research offer a precise and practical method for judging the quality of concrete constructions. |
| format | Article |
| id | doaj-art-e8e41c74c5294088a71bb1bfdb3fafdf |
| institution | DOAJ |
| issn | 2636-8129 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya University Journal of Computer and Information Sciences |
| spelling | doaj-art-e8e41c74c5294088a71bb1bfdb3fafdf2025-08-20T03:10:49ZengSakarya UniversitySakarya University Journal of Computer and Information Sciences2636-81292024-08-017212713710.35377/saucis...141558328Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field ConcreteOmobolaji Opafola0https://orcid.org/0000-0003-4896-512XAbisola Olayiwola1https://orcid.org/0000-0002-1585-0863Ositola Osifeko2https://orcid.org/0000-0002-9350-6056Adekunle David3https://orcid.org/0000-0002-5803-708XAjibola Oyedejı4https://orcid.org/0000-0002-0180-492XOlabisi Onabanjo UniversityOlabisi Onabanjo UniversityOlabisi Onabanjo UniversityOlabisi Onabanjo UniversityOlabisi Onabanjo UniversityThe determination of the concrete compressive strength remains a challenging task in the concrete industry. Machine learning (ML) algorithms offer an alternative and this study presents a comparative analysis of five ML regression models; Gradient Boosting (GB), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Linear Regression (LR) on a dataset of 1030 concrete samples. The findings indicate that the GB model achieved the best performance. The developed GB model achieved R-squared values of 91.60%, 91.43%, and 90.18% for the 10-fold, 5-fold, and 3-fold cross-validations, respectively, with mean absolute error, root mean squared error, and mean absolute percentage error values of 2.6776, 4.3523, and 9.19%, respectively. The GB model trained and evaluated was deployed to a web application using Streamlit for real-time prediction of the concrete compressive strength. The results of this research offer a precise and practical method for judging the quality of concrete constructions.https://dergipark.org.tr/en/download/article-file/3643078machine learningconcrete compressive strengthpredictionregression modelsweb application |
| spellingShingle | Omobolaji Opafola Abisola Olayiwola Ositola Osifeko Adekunle David Ajibola Oyedejı Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete Sakarya University Journal of Computer and Information Sciences machine learning concrete compressive strength prediction regression models web application |
| title | Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete |
| title_full | Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete |
| title_fullStr | Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete |
| title_full_unstemmed | Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete |
| title_short | Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete |
| title_sort | comparative analysis of machine learning techniques for prediction of the compressive strength of field concrete |
| topic | machine learning concrete compressive strength prediction regression models web application |
| url | https://dergipark.org.tr/en/download/article-file/3643078 |
| work_keys_str_mv | AT omobolajiopafola comparativeanalysisofmachinelearningtechniquesforpredictionofthecompressivestrengthoffieldconcrete AT abisolaolayiwola comparativeanalysisofmachinelearningtechniquesforpredictionofthecompressivestrengthoffieldconcrete AT ositolaosifeko comparativeanalysisofmachinelearningtechniquesforpredictionofthecompressivestrengthoffieldconcrete AT adekunledavid comparativeanalysisofmachinelearningtechniquesforpredictionofthecompressivestrengthoffieldconcrete AT ajibolaoyedejı comparativeanalysisofmachinelearningtechniquesforpredictionofthecompressivestrengthoffieldconcrete |