Machine learning-based automated waste sorting in the construction industry: A comparative competitiveness case study.

Abstract

This article presents a comparative analysis of the circularity and cost-efficiency of two distinct construction material recycling processes: ML-based automated sorting (MLAS) and conventional sorting technologies. Empirical data was collected from two Finnish companies, providing a robust foundation for this comparison. Our study examines the operational specifics, economic implications, and environmental impacts of each method, highlighting the advantages and drawbacks. By leveraging data-driven insights, we aim to illustrate how MLAS can enhance recycling efficiency and sustainability compared to traditional methods. In our cost modeling over a seven-year period, MLAS achieved a cumulative cost of €12.76 million, significantly lower than CS, which incurred €21.47 million, underscoring the long-term cost efficiency of MLAS. The findings underscore the potential for advanced AI technologies to revolutionize waste management practices, offering significant improvements in sorting accuracy, material recovery rates, and overall cost-effectiveness. This analysis provides valuable perspectives for stakeholders in the construction and waste management industries, emphasizing the importance of integrating innovative technologies to achieve higher circularity and sustainability goals.