A Revit-Based Platform for Building Component Library Management, Retrieval, and Reuse

Efficient reuse of Building Information Modeling (BIM) components is essential for construction detailing. However, reusable resources are frequently dispersed across project files and conventional folder-based repositories. This fragmentation increases retrieval effort and repetitive modeling. This study develops a Revit-based platform that transforms dispersed component resources into a structured and reusable component library within the native Revit environment. The platform integrates component archiving, hierarchical classification, metadata-indexed retrieval, and rapid loading and placement. It also supports offline library sharing and synchronization, quantity statistics, and completeness verification. A unified component data model supports both individual Revit families and multi-element assemblies and connects resource archiving, retrieval, reuse, sharing, and post-use verification into a continuous management–reuse–verification workflow. The platform was evaluated using Bailey trusses and other construction components from a building construction-machine project. A randomized, counterbalanced within-participant experiment involving 12 engineering technicians showed that the platform reduced mean task-completion time from 299.5 s to 87.3 s, representing a 70.9% reduction and an approximately 3.43-fold increase in modeling efficiency. A paired-samples t-test confirmed a statistically significant difference between the two workflows (t(11) = 71.03, p < 0.001). Retrieval tests further demonstrated strong scalability: as the library size increased twentyfold from 50 to 1000 components, platform retrieval time increased from 5 s to 19 s, whereas conventional folder-based retrieval increased from 35 s to 769 s. At 1000 components, the proposed platform was approximately 40.5 times faster. Within the evaluated Revit 2020 tasks and library sizes, these findings support an integrated approach to improving component retrieval and repetitive modeling efficiency; broader validation remains necessary.