首次量化设计行为与BIM质量关系，提升设计优化依据。
What makes a good BIM design: quantitative linking between design behavior and quality
- 通过实时数据采集与机器学习建模，量化分析设计行为
- 极端随机树模型在测试集上达R2=0.88，表现最优
- 设计师技能水平和意图变化是影响质量的关键因素
在建筑、工程与施工（AEC）行业，设计行为如何影响设计质量仍不明确。本研究提出一种新方法，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。通过实时数据采集与日志挖掘获取原始设计行为数据，结合特征工程与多种机器学习模型进行定量建模与解释。结果证实该关系可被不同模型学习，其中使用极端随机树的模型在测试集上达到R²=0.88。研究识别出与设计师技能水平及设计意图变化相关的特征对设计质量有显著影响。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。
原文摘要 · Abstract (English)
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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