LEVERAGING ARTIFICIAL INTELLIGENCE FOR ENHANCED WASTE MANAGEMENT EFFICIENCY IN CIVIL ENGINEERING

Izuako Chimarobi J., Elogu Thaddeus, Adiele Joseph

Abstract


 

 Artificial Intelligence (AI) is reshaping the waste management sector, introducing solutions that significantly enhance waste sorting, recycling, and resource recovery efficiency. This study explores the role of AI-driven systems in automated waste classification, contamination control, and the recovery of valuable materials, with a focus on machine learning techniques such as convolutional neural networks (CNNs) and deep learning. By comparing AI-enhanced systems to traditional methods, this paper highlights the improvements in sorting accuracy, energy efficiency, and operational cost reduction achieved through AI. AI based models are shown to increase material classification accuracy by up to 95% and reduce energy consumption by 25%, while also enabling higher recovery rates of valuable materials such as metals. The integration of AI in waste management not only advances circular economy objectives by diverting waste from landfills but also supports environmental sustainability by optimizing resource recovery. The findings underscore the potential for AI to transform waste management practices, driving efficiency and sustainability in this critical sector.

KEYWORDS: Artificial intelligence, Waste Management, Neural Network


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References


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