Oral Presentation World Sustainable Built Environment Conference 2026

Exploring Decision Support Systems in Construction and Demolition Waste Management: Current Status and Research Directions (132315)

Rannulu Sanjana De Zoysa 1 2 , Chintha Jayasinghe 2 , Indunil Erandi Ariyaratne 2 , Ruwini Edirisinghe 3 , Sujeeva Setunge 1
  1. Civil & Infrastructure Engineering, School of Engineering, RMIT University, Melbourne, Victoria 3001 , Australia
  2. Department of Civil Engineering, Faculty of Engineering, University of Moratuwa, Moratuwa 10400 , Sri Lanka
  3. School of Property Construction and Project Management, RMIT University, Melbourne, Victoria 3001, Australia

Construction and demolition waste (CDW) is identified as one of the largest global waste streams, accounting for approximately 30-40% of total solid waste. Current CDW management primarily relies on direct disposal to landfills, accounting for an estimated global average of 35% of the CDW stream. Sustained reliance on direct landfill disposal leads to significant environmental, economic, and social issues. The Circular Economy (CE) approach has emerged as a sustainable solution to traditional waste management, aiming to close resource loops through strategies such as waste reduction, reuse, and recycling. Decision support systems (DSS) are increasingly being adopted as tools that guide stakeholders in identifying optimal CDW management strategies that promote CE principles. Making decisions in this context involves evaluating multiple alternatives. Decision-making is a complex process that requires identifying effective waste management strategies that adhere to regulations, conserve resources, remain economically feasible, and minimize environmental impacts. However, existing research indicates that many DSS often lack integration of real-time data and rarely consider CE principles. There is a notable gap in the literature regarding a systematic evaluation of existing DSS, such as Cost-Benefit Analysis (CBA), Life Cycle Assessment (LCA), and Multi-Criteria Decision Analysis (MCDA). Existing studies rarely assess their methodologies, assessment criteria and overall effectiveness in CDW management. The present study aims to contribute to the development of a more resource-efficient and sustainable CDW management framework through a comprehensive literature review. Furthermore, this study identifies key limitations in existing DSS and explores opportunities for their enhancement, incorporating CE indicators and advanced technologies, such as artificial intelligence. The findings on such improvements are expected to support the transition toward a circular construction industry.

Keywords: Artificial Intelligence, Circular Economy, Construction & Demolition Waste, Decision Support Systems, Optimization