ScholarWorks@성균관대학교

초록

This study proposes an automated Task-level BIM-LogNet Analysis for Design and Drawing Generation Efficiency (T-BADGE) method. While BIM implementation promises productivity gains, current analyses rely on qualitative surveys or manual methods. Recent BIM log mining studies have focused only on command-level analysis, limiting meaningful task-level design productivity assessment. T-BADGE analyzes design time, quantity, and quality metrics through automated task identification from a BIM-LogNet—a network constructed from BIM log data. Network clustering is employed to identify coherent task communities corresponding to distinct design work units, enabling automated quantitative productivity analysis that supports decision-making in design and engineering process management at both task and project levels. Validation on 20 real-world BIM-driven design projects demonstrates that BIM-LogNet clustering enables automated productivity pattern analysis without manual efforts. The method achieved 94.9% time savings compared to manual productivity analysis and 93.9% task clustering fitness, confirming its practical applicability for quantitative productivity evaluation.

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