Conceptual Framework for Integration of AI and ML in Construction and Demolition Waste Management to Achieve the Goals of Circular Economy: The Case of India

Purpose The ever-growing construction industry in India and proportional increase in Construction and Demolition (C&D) waste are causing significant environmental and economic challenge. The concept of a circular economy offers a sustainable and restorative alternative to the traditional linear "take-make-dispose" model by emphasizing waste reduction, reuse, and recycling. The aim of this paper is to explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) as transformative technologies to overcome the multifaceted hurdles of C&D waste management in India and catalyse the transition to a circular economy. Study Design/Methodology/Approach The qualitative research methodology deployed involves a comprehensive and systematic review of existing literature, including peer-reviewed academic papers, technical reports from industry consortiums, government publications, and case studies of AI adoption in waste management from around the world, highlighting the systemic challenges, including logistical inefficiencies, poor source segregation, and the prevalence of an informal labour sector. Findings Based on the synthesis of literature informed data, including examining the specific applications of AI/ML in critical areas such as automated waste segregation using computer vision, predictive modelling for waste quantification, route optimization for collection logistics, and advanced sensor-based quality assessment for recycled materials, a comprehensive conceptual framework for integrating these AI/ML solutions into the C&D waste value chain within the unique Indian context is proposed as an outcome from this study. Originality/Value The novelty of this research includes proposing an integrative conceptual framework which is contextualised for India. The proposed conceptual framework attempts a harmonizing synthesis between the manual informal sector and AI/ML based robotic automation to achieve sustainable C&D waste management eco-system in Indian context. Research Limitations/Implications Scope of the study is literature-informed, conceptual and illustrative in nature and does not include empirical analysis. Practical Implications The study has revealed that the adoption of AI and ML in C&D waste management can help in conserving natural resources like sand, stone, and water by reducing the amount of waste generation. The paper concludes with an in-depth discussion on the significant practical implementation challenges such as high initial capital investment and data infrastructure gaps. Social Implications The potential socio-economic impacts, including job creation and formalization, and actionable policy recommendations designed to foster the adoption of these advanced technologies for sustainable C&D waste management in India are also highlighted in the paper.