KhairEldin Mohamed, Daoud Ahmed Osama, Ibrahim Ahmed Hussein, Toma Hossam M
Construction Engineering and Utilities Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt.
Civil Engineering Department, Faculty of Engineering, The British University in Egypt (BUE), El Sherouk City, Cairo, 11837, Egypt.
Sci Rep. 2025 Jan 23;15(1):2972. doi: 10.1038/s41598-025-86474-1.
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 R 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.
有效的建筑垃圾（主要是混凝土、砖块和钢材）管理是一项严峻挑战，因为其对环境和经济有重大影响。本研究通过提出多元线性回归模型来应对这一挑战，以预测埃及建筑业住宅建筑中的垃圾产生量，同时考虑建筑设计和场地管理特征等因素的影响。利用来自25个案例研究的数据，这些模型显示出较高的预测准确性，混凝土、砖块和钢材垃圾的调整后R²值分别为0.877、0.893和0.889。这些R值表明，这些模型解释了住宅建筑垃圾产生量中约88%-89%的方差，突出了它们在加强资源规划和垃圾管理策略方面的有效性。研究结果表明，纳入总面积、设计一致性和场地组织等变量可显著提高垃圾预测的准确性。尽管这些模型表现出可接受的性能，但未来的研究应旨在扩大数据集、纳入更多变量，并在不同类型的建设项目中测试这些模型，以进一步验证和完善这些预测工具。这些模型为改进埃及建筑业的施工做法、减少垃圾和支持可持续发展提供了宝贵的见解。通过对垃圾产生量的准确预测，这些模型有助于项目经理和利益相关者更有效地规划建筑垃圾管理，减少不必要材料消耗并降低环境影响。这些研究结果有助于采用可持续的施工做法，如改进回收流程和减少对填埋场的依赖，以支持埃及的《2030年愿景》。
