AI-Augmented Value Engineering Approach for Integrating Circular Economy Practices in Material Management and Waste Reduction in Heavy Construction

The heavy construction sector faces increasing demands to optimise material use, reduce waste, and achieve sustainability objectives. This study investigates the integration of artificial intelligence–augmented value engineering (AI-VE) with circular economy (CE) practices to improve material management and waste reduction. Building upon the classical VE job plan, the research develops an AI-assisted framework that incorporates data analytics, predictive modelling, and multi-objective optimisation to balance cost, function, and circularity. Using analytical synthesis and evidence from case studies including Crossrail (UK), Barangaroo South (Australia), and asphalt recycling projects, this paper demonstrates that AI-VE significantly enhances decision-making accuracy, material efficiency, and environmental performance. Results indicate measurable reductions in construction waste (15–25 %), cost savings (10–20 %), and embodied-carbon emissions (up to 25 %). The integration of CE indicators such as recyclability and resource intensity into VE evaluation matrices ensures sustainability considerations are embedded from early design to construction execution. Key findings suggest that successful implementation depends on data interoperability, multidisciplinary collaboration, and ethical governance of AI tools. The research concludes that AI-augmented VE constitutes a practical pathway toward sustainable and circular construction by combining value optimisation with data-driven intelligence. This hybrid framework advances both theory and practice, positioning AI-VE as a cornerstone for sustainable infrastructure management.