In the Architecture Engineering & Construction (AEC) industry, how design behaviors impact design quality remains unclear. This study proposes a novel approach, which, for the first time, identifies and quantitatively describes the relationship between design behaviors and quality of design based on Building Information Modeling (BIM). Real-time collection and log mining are integrated to collect raw data of design behaviors. Feature engineering and various machine learning models are then utilized for quantitative modeling and interpretation. Results confirm an existing quantifiable relationship which can be learned by various models. The best-performing model using Extremely Random Trees achieved an R2 value of 0.88 on the test set. Behavioral features related to designer's skill level and changes of design intentions are identified to have significant impacts on design quality. These findings deepen our understanding of the design process and help forming BIM designs with better quality.

翻译：在建筑、工程与施工（AEC）行业中，设计行为如何影响设计质量仍不明确。本研究提出一种创新方法，首次基于建筑信息模型（BIM）识别并量化描述设计行为与设计质量之间的关系。通过整合实时采集与日志挖掘技术收集设计行为的原始数据，继而运用特征工程与多种机器学习模型进行量化建模与解释。结果证实存在可通过多种模型学习的量化关系，其中采用极端随机树的最佳模型在测试集上达到了0.88的R²值。研究发现，与设计者技能水平及设计意图变更相关的行为特征对设计质量具有显著影响。这些发现深化了对设计过程的理解，有助于形成更高质量的BIM设计。