Prediction of the Characteristics of Concrete Containing Crushed Brick Aggregate
The construction industry faces the challenge of conserving natural resources while maintaining environmental sustainability. This study investigates the feasibility of using recycled materials, particularly crushed clay bricks, as replacements for conventional aggregates in concrete. The research a...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-07-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/68/1/24 |
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| Summary: | The construction industry faces the challenge of conserving natural resources while maintaining environmental sustainability. This study investigates the feasibility of using recycled materials, particularly crushed clay bricks, as replacements for conventional aggregates in concrete. The research aims to optimize the performance of both single regression tree models and ensembles of regression trees in predicting concrete properties. The study focuses on optimizing key parameters like the minimum leaf size in the models. By testing various minimum leaf sizes and ensemble methods such as Random Forest and TreeBagger, the study evaluates metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R<sup>2</sup>). The analysis indicates that the most influential factors on concrete characteristics are the concrete’s age, the amount of superplasticizer used, and the size of crushed brick particles exceeding 4 mm. Additionally, the water-to-cement ratio significantly impacts the predictions. The regression tree models showed optimal performance with a minimum leaf size, achieving an RMSE of 4.00, an MAE of 2.95, an MAPE of 0.10, and an R<sup>2</sup> of 0.96. |
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| ISSN: | 2673-4591 |