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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.09481 |
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| _version_ | 1866929591499620352 |
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| author | Ni, Xiang-Rui Pan, Peng Lin, Jia-Rui |
| author_facet | Ni, Xiang-Rui Pan, Peng Lin, Jia-Rui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_09481 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What makes a good BIM design: quantitative linking between design behavior and quality Ni, Xiang-Rui Pan, Peng Lin, Jia-Rui Machine Learning 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. |
| title | What makes a good BIM design: quantitative linking between design behavior and quality |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.09481 |