Probabilistic Grading and Classification System for End-of-Life Building Components Toward Circular Economy Loop

Abstract

The longevity and viability of construction components in a circular economy demand a robust, data-informed framework for reuse decision-making. This paper introduces a multi-level grading and classification system that combines Bayesian probabilistic modeling with scenario-based performance thresholds to assess the reusability of end-of-life modular components. By grading components across a five-tier scale, the system supports strategic decisions for reuse, up-use, or down-use, ensuring alignment with engineering standards and sustainability objectives. The model's development is grounded in empirical data from precast concrete wall panels, and its explainability is enhanced through decision tree logic and Sankey visualizations that trace the influence of contextual scenarios on classification outcomes. MGCS addresses the environmental, economic, and operational challenges of EoL management--reducing material waste, optimizing value recovery, and improving workflow efficiency. Through dynamic feature weighting and transparent reasoning, the system offers a practical yet rigorous pathway to embed circular thinking into construction industry practices.