Exploring artificial intelligence applications in construction and demolition waste management: a review of existing literature

Abstract

This study presents a comprehensive analysis of artificial intelligence (AI) applications in construction and demolition waste management (CDWM), examining current trends, limitations, and opportunities for enhanced sustainability. Through a systematic literature review and bibliometric analysis across multiple academic databases, the research identifies eight major subfields where AI significantly impacts CDWM processes, particularly in planning, design, forecasting, and monitoring activities. The findings reveal that while AI demonstrates considerable potential in various aspects of waste management, its application in waste collection remains constrained by dependence on physical machinery. The study highlights the versatility of machine learning and natural language processing technologies, while emphasising the need for expanded research into innovative recycling approaches to maximise material reuse. Despite limitations regarding literature selection bias and context-specific generalisability, this research provides valuable insights for practitioners and policymakers by illustrating how AI technologies can improve operational efficiency, minimise environmental impact, and enhance resource recovery in construction projects. The study's unique contribution lies in its comprehensive review of AI applications in CDWM, addressing research gaps while proposing new perspectives on optimising waste management practices through emerging technologies. This work serves as a foundation for future research, particularly in exploring AI applications for recycling processes and examining their implications for sustainable waste management practices across all operational stages.