Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques
In this study, Response Surface Methodology (RSM) and machine learning models were used to predict the mechanical properties of recycled aggregate concrete (RAC) containing ground granulated blast furnace slag (GGBS) and polypropylene fibers (PPFs). The investigation focused on compressive strength...
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MDPI AG
2024-12-01
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| author | Anjali Jaglan Rati Ram Singh |
| author_facet | Anjali Jaglan Rati Ram Singh |
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| description | In this study, Response Surface Methodology (RSM) and machine learning models were used to predict the mechanical properties of recycled aggregate concrete (RAC) containing ground granulated blast furnace slag (GGBS) and polypropylene fibers (PPFs). The investigation focused on compressive strength (CS) and split tensile strength (STS) tests at curing periods of 7, 28, 56, and 90 days, with variations in the percentages of GGBS (0–50%), recycled aggregate (RA) (0–100%), and PPF (0–1%). The RSM model showed high accuracy in predicting both CS and STS, with statistically significant results (<i>p</i>-value < 0.0001). Among the machine learning models, the Gradient Boosting Machine (GBM) exhibited the highest performance, achieving an R<sup>2</sup> value of 0.98961 during the training and testing phases for CS prediction. It also demonstrated strong results for STS prediction, with an MSE of 0.02773, MAPE of 2.69775, and R<sup>2</sup> value of 0.99404 in the training phase, and an MSE of 0.14141, MAPE of 5.71691, and R<sup>2</sup> value of 0.96947 during testing. The Stacked Ensemble Learning model performed similarly to GBM, with an R<sup>2</sup> of 0.99251 during training for STS and 0.96619 during testing. However, GBM consistently outperformed the other models in terms of balancing low error rates and high R<sup>2</sup> values across both datasets. The Distributed Random Forest model also provided strong performance but slightly higher error rates and lower R<sup>2</sup> values than GBM. Overall, both GGBS and PPF significantly enhanced the mechanical properties and workability of the concrete, highlighting the importance of these additives in optimizing concrete performance. |
| format | Article |
| id | doaj-art-c4a5763173f44a7584d7591373e58080 |
| institution | Kabale University |
| issn | 2075-5309 |
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| publishDate | 2024-12-01 |
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| series | Buildings |
| spelling | doaj-art-c4a5763173f44a7584d7591373e580802025-01-10T13:15:56ZengMDPI AGBuildings2075-53092024-12-011516610.3390/buildings15010066Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning TechniquesAnjali Jaglan0Rati Ram Singh1Department of Civil Engineering, Punjab Engineering College (Deemed to be University), Chandigarh 160012, IndiaDepartment of Civil Engineering, Punjab Engineering College (Deemed to be University), Chandigarh 160012, IndiaIn this study, Response Surface Methodology (RSM) and machine learning models were used to predict the mechanical properties of recycled aggregate concrete (RAC) containing ground granulated blast furnace slag (GGBS) and polypropylene fibers (PPFs). The investigation focused on compressive strength (CS) and split tensile strength (STS) tests at curing periods of 7, 28, 56, and 90 days, with variations in the percentages of GGBS (0–50%), recycled aggregate (RA) (0–100%), and PPF (0–1%). The RSM model showed high accuracy in predicting both CS and STS, with statistically significant results (<i>p</i>-value < 0.0001). Among the machine learning models, the Gradient Boosting Machine (GBM) exhibited the highest performance, achieving an R<sup>2</sup> value of 0.98961 during the training and testing phases for CS prediction. It also demonstrated strong results for STS prediction, with an MSE of 0.02773, MAPE of 2.69775, and R<sup>2</sup> value of 0.99404 in the training phase, and an MSE of 0.14141, MAPE of 5.71691, and R<sup>2</sup> value of 0.96947 during testing. The Stacked Ensemble Learning model performed similarly to GBM, with an R<sup>2</sup> of 0.99251 during training for STS and 0.96619 during testing. However, GBM consistently outperformed the other models in terms of balancing low error rates and high R<sup>2</sup> values across both datasets. The Distributed Random Forest model also provided strong performance but slightly higher error rates and lower R<sup>2</sup> values than GBM. Overall, both GGBS and PPF significantly enhanced the mechanical properties and workability of the concrete, highlighting the importance of these additives in optimizing concrete performance.https://www.mdpi.com/2075-5309/15/1/66recycled aggregatesground granulated blast furnace slagpolypropylene fiberworkabilitycompressive strength |
| spellingShingle | Anjali Jaglan Rati Ram Singh Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques Buildings recycled aggregates ground granulated blast furnace slag polypropylene fiber workability compressive strength |
| title | Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques |
| title_full | Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques |
| title_fullStr | Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques |
| title_full_unstemmed | Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques |
| title_short | Recycled Aggregate Concrete Incorporating GGBS and Polypropylene Fibers Using RSM and Machine Learning Techniques |
| title_sort | recycled aggregate concrete incorporating ggbs and polypropylene fibers using rsm and machine learning techniques |
| topic | recycled aggregates ground granulated blast furnace slag polypropylene fiber workability compressive strength |
| url | https://www.mdpi.com/2075-5309/15/1/66 |
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