什么构成了好的BIM设计：设计行为与质量之间的定量联系
What makes a good BIM design: quantitative linking between design behavior and quality
AI总结：
本研究首次基于BIM定量分析了设计行为与设计质量的关系，通过特征工程和机器学习建模，发现极随机树模型在测试集上R2达0.88，并识别出设计师技能和设计意图变化对质量有显著影响。
摘要：
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清楚。本研究提出了一种新方法，首次基于建筑信息模型（BIM）识别并定量描述设计行为与设计质量之间的关系。该方法集成了实时收集和日志挖掘来收集设计行为的原始数据。随后利用特征工程和各种机器学习模型进行定量建模和解释。结果证实存在可量化的关系，且可被各种模型学习。使用极随机树的最佳模型在测试集上取得了0.88的R2值。与设计师技能水平和设计意图变化相关的行为特征被识别出对设计质量有显著影响。这些发现加深了我们对设计过程的理解，有助于形成质量更好的BIM设计。
英文摘要：
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.
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