Predicting construction waste in Egyptian residential projects: a robust multiple regression model approach.

Area of Science:

• Environmental Engineering
• Construction Management
• Sustainable Development

Background:

• Construction waste (CW) poses significant environmental and economic challenges.
• Effective management of concrete, brick, and steel waste is crucial for the construction industry.
• The Egyptian construction sector faces challenges in managing substantial waste generation.

Purpose of the Study:

• To develop predictive models for construction waste generation in Egyptian residential buildings.
• To identify key factors influencing waste generation, including building design and site management.
• To enhance resource planning and waste management strategies through accurate waste forecasting.

Main Methods:

• Utilized multiple linear regression models.
• Analyzed data from 25 case studies of residential buildings.
• Incorporated variables such as total area, design consistency, and site organization.

Main Results:

• Models achieved high predictive accuracy for concrete (Adj. R²=0.877), bricks (Adj. R²=0.893), and steel waste (Adj. R²=0.889).
• The models explain approximately 88-89% of the variance in residential construction waste generation.
• Factors like total area, design consistency, and site organization significantly improve prediction accuracy.

Conclusions:

• The developed models are effective tools for predicting construction waste in Egypt.
• Accurate waste forecasts enable better resource planning, reduced material consumption, and minimized environmental impact.
• Findings support the adoption of sustainable construction practices and contribute to Egypt's Vision 2030 goals.