ARCEAK: An Automated Rule Checking Framework Enhanced with Architectural Knowledge
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arXiv
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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909465590104064 |
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| author | Chen, Junyong Wu, Ling-I Chen, Minyu Qian, Xiaoying Zhu, Haoze Zhang, Qiongfang Li, Guoqiang |
| author_facet | Chen, Junyong Wu, Ling-I Chen, Minyu Qian, Xiaoying Zhu, Haoze Zhang, Qiongfang Li, Guoqiang |
| contents | Automated Rule Checking (ARC) plays a crucial role in advancing the construction industry by addressing the laborious, inconsistent, and error-prone nature of traditional model review conducted by industry professionals. Manual assessment against intricate sets of rules often leads to significant project delays and expenses. In response to these challenges, ARC offers a promising solution to improve efficiency and compliance in design within the construction sector. However, the main challenge of ARC lies in translating regulatory text into a format suitable for computer processing. Current methods for rule interpretation require extensive manual labor, thereby limiting their practicality. To address this issue, our study introduces a novel approach that decomposes ARC into two distinct tasks: rule information extraction and verification code generation. Leveraging generative pre-trained transformers, our method aims to streamline the interpretation of regulatory texts and simplify the process of generating model compliance checking code. Through empirical evaluation and case studies, we showcase the effectiveness and potential of our approach in automating code compliance checking, enhancing the efficiency and reliability of construction projects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14735 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ARCEAK: An Automated Rule Checking Framework Enhanced with Architectural Knowledge Chen, Junyong Wu, Ling-I Chen, Minyu Qian, Xiaoying Zhu, Haoze Zhang, Qiongfang Li, Guoqiang Software Engineering Artificial Intelligence Automated Rule Checking (ARC) plays a crucial role in advancing the construction industry by addressing the laborious, inconsistent, and error-prone nature of traditional model review conducted by industry professionals. Manual assessment against intricate sets of rules often leads to significant project delays and expenses. In response to these challenges, ARC offers a promising solution to improve efficiency and compliance in design within the construction sector. However, the main challenge of ARC lies in translating regulatory text into a format suitable for computer processing. Current methods for rule interpretation require extensive manual labor, thereby limiting their practicality. To address this issue, our study introduces a novel approach that decomposes ARC into two distinct tasks: rule information extraction and verification code generation. Leveraging generative pre-trained transformers, our method aims to streamline the interpretation of regulatory texts and simplify the process of generating model compliance checking code. Through empirical evaluation and case studies, we showcase the effectiveness and potential of our approach in automating code compliance checking, enhancing the efficiency and reliability of construction projects. |
| title | ARCEAK: An Automated Rule Checking Framework Enhanced with Architectural Knowledge |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2501.14735 |