A unified intelligence-augmented framework for building audits via physical and virtual inspection

Abstract

Building audits are essential for informed decision-making in maintenance, renovation, and end-of-life planning. However, current practices remain predominantly manual and time-consuming due to fragmented data, limiting resource-efficient management of existing building stock. This paper presents a unified, intelligence-augmented framework designed to enhance the efficiency and reliability of both physical and virtual building inspection workflows. Five core design principles of adaptability, accessibility, affordability, acceleration, and alignment are derived from a multi-phase formal analysis to guide the development of the R²PIVS pipeline, which transforms the existing audit process into six modules: retrieval, reality capture, prediction, interaction, visualization, and summarization. The framework leverages human-AI collaboration in key building inventory tasks, including geometry measurement, visual assessment, and hazard estimation, through interactive annotation, model refinement, and output validation. Findings from expert elicitation studies indicate that the proposed application schema is promising for improving the efficiency and scalability of existing workflows. By aligning machine learning capabilities with domain-specific requirements, this research lays the foundation for a human-in-the-loop building audit system that enables standardized inspection and inventory information management to support circular construction practices throughout the building life cycle.