Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach - Mohamed KhairEldin; Ahmed Osama Daoud; Ahmed Hussein Ibrahim; Hossam M. Toma (Scientific Reports)
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.