Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling
Abstract Concrete production heavily relies on natural aggregates, leading to environmental concerns due to resource depletion and extraction impacts. This study explores the feasibility of using demolished concrete aggregate (DA) as a sustainable alternative. Despite being a major global waste prod...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Civil Engineering |
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| Online Access: | https://doi.org/10.1007/s44290-025-00230-y |
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| author | Hyginus Obinna Ozioko Emmanuel Ebube Eze |
| author_facet | Hyginus Obinna Ozioko Emmanuel Ebube Eze |
| author_sort | Hyginus Obinna Ozioko |
| collection | DOAJ |
| description | Abstract Concrete production heavily relies on natural aggregates, leading to environmental concerns due to resource depletion and extraction impacts. This study explores the feasibility of using demolished concrete aggregate (DA) as a sustainable alternative. Despite being a major global waste product, DA’s effects on concrete properties remain underexplored, particularly in regions with limited natural aggregates. This research examines DA’s impact on workability, density, and compressive strength, alongside machine learning-based performance predictions. Concrete mixes with 0–30% DA replacement was evaluated. The results showed decrease in workability as the DA increases, with a maximum slump reduction of 72.7%. Bulk density showed a slight decline, while compressive strength decreased significantly, reaching a 56.7% reduction at 30% DA replacement. ANOVA results (F = 12.97, p = 1.84E− 07) confirmed significant strength differences, with post-hoc tests indicating no significant effect at 2–7% replacement but notable reductions at ≥ 10% (p < 0.05). Three machine learning models: linear regression, polynomial regression, and artificial neural networks, were developed to predict compressive strength. Linear regression (R2 = 0.801, MAE = 0.663, RMSE = 0.757, SI = 0.060) outperformed polynomial regression and ANN models. The findings underscore DA’s potential as a sustainable aggregate alternative, emphasizing the importance of optimizing replacement levels to maintain concrete performance. |
| format | Article |
| id | doaj-art-82b1d054f97d46e9bee66fd1dbf37113 |
| institution | OA Journals |
| issn | 2948-1546 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Civil Engineering |
| spelling | doaj-art-82b1d054f97d46e9bee66fd1dbf371132025-08-20T02:12:02ZengSpringerDiscover Civil Engineering2948-15462025-04-012112010.1007/s44290-025-00230-yEvaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modelingHyginus Obinna Ozioko0Emmanuel Ebube Eze1Department of Civil Engineering, Michael Okpara University of AgricultureDepartment of Civil Engineering, Michael Okpara University of AgricultureAbstract Concrete production heavily relies on natural aggregates, leading to environmental concerns due to resource depletion and extraction impacts. This study explores the feasibility of using demolished concrete aggregate (DA) as a sustainable alternative. Despite being a major global waste product, DA’s effects on concrete properties remain underexplored, particularly in regions with limited natural aggregates. This research examines DA’s impact on workability, density, and compressive strength, alongside machine learning-based performance predictions. Concrete mixes with 0–30% DA replacement was evaluated. The results showed decrease in workability as the DA increases, with a maximum slump reduction of 72.7%. Bulk density showed a slight decline, while compressive strength decreased significantly, reaching a 56.7% reduction at 30% DA replacement. ANOVA results (F = 12.97, p = 1.84E− 07) confirmed significant strength differences, with post-hoc tests indicating no significant effect at 2–7% replacement but notable reductions at ≥ 10% (p < 0.05). Three machine learning models: linear regression, polynomial regression, and artificial neural networks, were developed to predict compressive strength. Linear regression (R2 = 0.801, MAE = 0.663, RMSE = 0.757, SI = 0.060) outperformed polynomial regression and ANN models. The findings underscore DA’s potential as a sustainable aggregate alternative, emphasizing the importance of optimizing replacement levels to maintain concrete performance.https://doi.org/10.1007/s44290-025-00230-yDemolished concrete aggregate (DA)ConcreteSustainable constructionMachine learningCompressive strength |
| spellingShingle | Hyginus Obinna Ozioko Emmanuel Ebube Eze Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling Discover Civil Engineering Demolished concrete aggregate (DA) Concrete Sustainable construction Machine learning Compressive strength |
| title | Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling |
| title_full | Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling |
| title_fullStr | Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling |
| title_full_unstemmed | Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling |
| title_short | Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling |
| title_sort | evaluating the impact of demolished concrete aggregates on workability density and strength with predictive modeling |
| topic | Demolished concrete aggregate (DA) Concrete Sustainable construction Machine learning Compressive strength |
| url | https://doi.org/10.1007/s44290-025-00230-y |
| work_keys_str_mv | AT hyginusobinnaozioko evaluatingtheimpactofdemolishedconcreteaggregatesonworkabilitydensityandstrengthwithpredictivemodeling AT emmanuelebubeeze evaluatingtheimpactofdemolishedconcreteaggregatesonworkabilitydensityandstrengthwithpredictivemodeling |