Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction Models
The focus of this paper is on the use of machine learning for the prediction of the strength outcomes of basalt fiber-reinforced concrete (BFRC), based on its mechanical properties. These target properties are compressive, flexural, and tensile strengths, estimated with knowledge of 10 variables, in...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/acis/9961816 |
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| author | Mohanad A. Deif Hani Attar Waleed Alomoush Mohamed A. Hafez |
| author_facet | Mohanad A. Deif Hani Attar Waleed Alomoush Mohamed A. Hafez |
| author_sort | Mohanad A. Deif |
| collection | DOAJ |
| description | The focus of this paper is on the use of machine learning for the prediction of the strength outcomes of basalt fiber-reinforced concrete (BFRC), based on its mechanical properties. These target properties are compressive, flexural, and tensile strengths, estimated with knowledge of 10 variables, including cement and aggregate content, among other fiber characteristics. Models explored for regression in this paper include linear regression, K-nearest neighbors (KNN), random forest (RF), XGBoost (Extreme Gradient Boosting), support vector machine (SVM), and artificial neural networks (ANN). The highest performance among these was observed for the KNN at flexural strength with a R2 score of 0.8737, XGBoost for compressive strength with a R2 score of 0.8963, and RF for tensile strength with a R2 score of 0.9420. Bayesian optimization was employed to tune hyperparameters to enhance the accuracy of the model. This study also applied Synthetic Minority Oversampling Technique (SMOTE) to generate 1000 synthetic concrete mix designs for the data to increase its diversity and allow the investigation on optimal performances regarding strength. The findings of this study contribute to advancing sustainable manufacturing practices by leveraging machine learning techniques to optimize material properties, thereby supporting the development of resilient infrastructure and enhancing industrial innovation. |
| format | Article |
| id | doaj-art-2c1a8745e7a94e5280d7c9d46327f267 |
| institution | OA Journals |
| issn | 1687-9732 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-2c1a8745e7a94e5280d7c9d46327f2672025-08-20T02:34:31ZengWileyApplied Computational Intelligence and Soft Computing1687-97322025-01-01202510.1155/acis/9961816Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction ModelsMohanad A. Deif0Hani Attar1Waleed Alomoush2Mohamed A. Hafez3Research Institute of Sciences and EngineeringFaculty of EngineeringSchool of ComputingDepartment of Civil EngineeringThe focus of this paper is on the use of machine learning for the prediction of the strength outcomes of basalt fiber-reinforced concrete (BFRC), based on its mechanical properties. These target properties are compressive, flexural, and tensile strengths, estimated with knowledge of 10 variables, including cement and aggregate content, among other fiber characteristics. Models explored for regression in this paper include linear regression, K-nearest neighbors (KNN), random forest (RF), XGBoost (Extreme Gradient Boosting), support vector machine (SVM), and artificial neural networks (ANN). The highest performance among these was observed for the KNN at flexural strength with a R2 score of 0.8737, XGBoost for compressive strength with a R2 score of 0.8963, and RF for tensile strength with a R2 score of 0.9420. Bayesian optimization was employed to tune hyperparameters to enhance the accuracy of the model. This study also applied Synthetic Minority Oversampling Technique (SMOTE) to generate 1000 synthetic concrete mix designs for the data to increase its diversity and allow the investigation on optimal performances regarding strength. The findings of this study contribute to advancing sustainable manufacturing practices by leveraging machine learning techniques to optimize material properties, thereby supporting the development of resilient infrastructure and enhancing industrial innovation.http://dx.doi.org/10.1155/acis/9961816 |
| spellingShingle | Mohanad A. Deif Hani Attar Waleed Alomoush Mohamed A. Hafez Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction Models Applied Computational Intelligence and Soft Computing |
| title | Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction Models |
| title_full | Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction Models |
| title_fullStr | Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction Models |
| title_full_unstemmed | Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction Models |
| title_short | Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction Models |
| title_sort | optimizing concrete mix designs with synthetic data generation and machine learning prediction models |
| url | http://dx.doi.org/10.1155/acis/9961816 |
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