Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach
Abstract Effective construction waste (CW) management, mainly concrete, brick, and steel, is a critical challenge due to its significant environmental and economic impacts. This study addresses this challenge by proposing multiple linear regression models to predict waste generation in residential b...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86474-1 |
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| author | Mohamed KhairEldin Ahmed Osama Daoud Ahmed Hussein Ibrahim Hossam M. Toma |
| author_facet | Mohamed KhairEldin Ahmed Osama Daoud Ahmed Hussein Ibrahim Hossam M. Toma |
| author_sort | Mohamed KhairEldin |
| collection | DOAJ |
| description | Abstract Effective construction waste (CW) management, mainly concrete, brick, and steel, is a critical challenge due to its significant environmental and economic impacts. This study addresses this challenge by proposing multiple linear regression models to predict waste generation in residential buildings within the Egyptian construction industry, considering the influence of factors such as building design and site management features. Using data from 25 case studies, the models demonstrated high predictive accuracy, with adjusted R² values of 0.877, 0.893, and 0.889 for concrete, bricks, and steel waste, respectively. These R2 values indicate that the models explain approximately 88–89% of the variance in waste generation in residential buildings, highlighting their effectiveness in enhancing resource planning and waste management strategies. The findings suggest that incorporating variables such as total area, design consistency, and site organization significantly improves the accuracy of waste predictions. Although the models show acceptable performance, future research should aim to expand the dataset, incorporate additional variables, and test the models across different types of construction projects to validate further and refine these predictive tools. The models offer valuable insights for enhancing construction practices, minimizing waste, and supporting sustainable development in Egypt’s construction industry. With accurate forecasts of waste generation, the models help project managers and stakeholders to plan CW more effectively, mitigating unnecessary material consumption and reducing environmental impacts. These findings help to adopt sustainable construction practices, such as improved recycling processes and decreased dependence on landfills, to support Egypt’s Vision 2030. |
| format | Article |
| id | doaj-art-4515050f475a47a58b21cfa5a620e18e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4515050f475a47a58b21cfa5a620e18e2025-01-26T12:30:15ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-025-86474-1Predicting construction waste in Egyptian residential projects: a robust multiple regression model approachMohamed KhairEldin0Ahmed Osama Daoud1Ahmed Hussein Ibrahim2Hossam M. Toma3Construction Engineering and Utilities Department, Faculty of Engineering, Zagazig UniversityCivil Engineering Department, Faculty of Engineering, The British University in Egypt (BUE)Construction Engineering and Utilities Department, Faculty of Engineering, Zagazig UniversityConstruction Engineering and Utilities Department, Faculty of Engineering, Zagazig UniversityAbstract Effective construction waste (CW) management, mainly concrete, brick, and steel, is a critical challenge due to its significant environmental and economic impacts. This study addresses this challenge by proposing multiple linear regression models to predict waste generation in residential buildings within the Egyptian construction industry, considering the influence of factors such as building design and site management features. Using data from 25 case studies, the models demonstrated high predictive accuracy, with adjusted R² values of 0.877, 0.893, and 0.889 for concrete, bricks, and steel waste, respectively. These R2 values indicate that the models explain approximately 88–89% of the variance in waste generation in residential buildings, highlighting their effectiveness in enhancing resource planning and waste management strategies. The findings suggest that incorporating variables such as total area, design consistency, and site organization significantly improves the accuracy of waste predictions. Although the models show acceptable performance, future research should aim to expand the dataset, incorporate additional variables, and test the models across different types of construction projects to validate further and refine these predictive tools. The models offer valuable insights for enhancing construction practices, minimizing waste, and supporting sustainable development in Egypt’s construction industry. With accurate forecasts of waste generation, the models help project managers and stakeholders to plan CW more effectively, mitigating unnecessary material consumption and reducing environmental impacts. These findings help to adopt sustainable construction practices, such as improved recycling processes and decreased dependence on landfills, to support Egypt’s Vision 2030.https://doi.org/10.1038/s41598-025-86474-1Construction wasteWaste predictionConcreteBricksSteel |
| spellingShingle | Mohamed KhairEldin Ahmed Osama Daoud Ahmed Hussein Ibrahim Hossam M. Toma Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach Scientific Reports Construction waste Waste prediction Concrete Bricks Steel |
| title | Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach |
| title_full | Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach |
| title_fullStr | Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach |
| title_full_unstemmed | Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach |
| title_short | Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach |
| title_sort | predicting construction waste in egyptian residential projects a robust multiple regression model approach |
| topic | Construction waste Waste prediction Concrete Bricks Steel |
| url | https://doi.org/10.1038/s41598-025-86474-1 |
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