AI-Driven Decision Support Using Digital Product Passports for End-of-Life Management in the Circular Built Environment

The transition to the circular economy in the built environment requires decision support systems that can turn product data into operational End-of-Life (EoL) strategies. Digital Product Passports (DPPs), emerging as structured records of product lifecycle data, offer strong potential to support such decisions. However, methods for transforming DPP data into practical EoL recommendations remain an important area for research and development. To fill this gap, this position paper explores the integration of Answer-Set Programming (ASP), which is a declarative AI technique for complex rule-based reasoning that can be applied to DPPs. This study proposes a conceptual framework in which structured ontology- and knowledge graph-based DPP data, along with unstructured data processed using Large Language Models (LLMs), inform ASP-based models to recommend reuse, recycling, or disposal strategies under regulatory and environmental constraints. ASP’s strengths in knowledge representation and optimization are identified as making it a promising candidate for advancing intelligent and practical EoL management. The proposed system provides stakeholders with useful information to improve EoL strategies for building components by targeting measurable outcomes such as higher material recovery rates and better compliance with circular economy regulations.