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Abstract

Construction activities cause significant environmental impacts such as waste generation, pollution, resource consumption, and land use, which are expected to increase with global construction demands. To mitigate these environmental problems, the circular building occurs as an emerging concept depending on in the circular economy, which aims to minimize waste and redefine the “end-of-life” concept by reducing, reusing, recycling, and recovering materials. The reuse of building materials and components (BMCs) from existing structures represents a crucial strategy for achieving circularity in the built environment. Within this framework, design plays a key role. However, designing with reclaimed BMCs remains complex due to non-standardized dimensions, uncertain geometry, and insufficiency in accurate and reliable digital data regarding damage conditions, leading to limitations in quality, cost, and timing etc. There are knowledge-sharing platforms such as the Building as Material Bank Project and Madaster provide material passports containing information on material origin, performance, environmental impact, and recyclability. Despite these platforms, reuse implementation still remains limited, labour-intensive and technically demanding. Addressing these research gaps, this study focuses the research questions as: How can architects take design decisions effectively for reusing BMCs?; How can architects decide the best matching donor for BMCs while preserving design decisions and aesthetic considerations? To answer these questions, this study develops a Decision Support Model for Building Material and Component Reuse (DSM_BMCR) guiding architects in making appropriate design decisions aiming to reuse of building materials and components. DSM_BMCR is tested through a façade cladding design scenario using Multi-Criteria Decision-Making Methods integrated into the model. The results show that DSM_BMCR is effective in producing transparent, data-driven, and iterative decisions. Future integration with Building Information Modelling, digital twins, and AI-supported systems is expected to enhance process optimization and real-time material matching, strengthening its potential as a decision-support tool for circular buildings.

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