通过大语言模型驱动的功能匹配和组合,将规范条款转化为可执行的建筑设计检查代码

IF 8 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Zhe Zheng , Jin Han , Ke-Yin Chen , Xin-Yu Cao , Xin-Zheng Lu , Jia-Rui Lin
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引用次数: 0

摘要

将子句转换为可执行代码是自动规则检查(ARC)的一个重要阶段,对于有效的建筑设计遵从性检查至关重要,特别是对于具有隐式属性或需要领域知识的复杂逻辑的规则。因此,通过系统地分析构建子句,首先定义66个原子函数来封装公共计算逻辑。然后,提出了LLM- funcmapper,一种基于大型语言模型(LLM)的方法,采用基于规则的自适应提示将子句与原子函数相匹配。最后,通过llm组合函数生成可执行代码。实验表明,LLM-FuncMapper在函数匹配方面比微调方法高出19%,同时显著减少了手工标注的工作量。实例研究表明,LLM-FuncMapper能够自动组合多个原子函数生成可执行代码,提高了规则检查效率。据我们所知,这项研究代表了llm在将复杂设计条款解释为可执行代码方面的首次应用,这可能会为llm在建筑领域的进一步采用提供启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Translating regulatory clauses into executable codes for building design checking via large language model driven function matching and composing
Translating clauses into executable code is a vital stage of automated rule checking (ARC) and is essential for effective building design compliance checking, particularly for rules with implicit properties or complex logic requiring domain knowledge. Thus, by systematically analyzing building clauses, 66 atomic functions are defined first to encapsulate common computational logics. Then, LLM-FuncMapper is proposed, a large language model (LLM)-based approach with rule-based adaptive prompts that match clauses to atomic functions. Finally, executable code is generated by composing functions through the LLMs. Experiments show LLM-FuncMapper outperforms fine-tuning methods by 19 % in function matching while significantly reducing manual annotation efforts. Case study demonstrates that LLM-FuncMapper can automatically compose multiple atomic functions to generate executable code, boosting rule-checking efficiency. To our knowledge, this research represents the first application of LLMs for interpreting complex design clauses into executable code, which may shed light on further adoption of LLMs in the construction domain.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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