機電（MEP）系統結構複雜，且經常與其他建築構件發生衝突，容易產生問題。這些衝突不僅影響施工效率，還可能導致不符合法規規定，進而直接影響建築性能與居住者安全。在目前的實務操作中，針對機械法規條文的解讀與合規檢查仍屬人工作業，需投入大量時間與專業知識。雖然已有研究探索大型語言模型（LLMs）在建築領域的應用，但針對如法規解讀與合規檢查等專業任務進行客製化開發的研究仍屬少數。本研究針對特定領域配置一套專用的大型語言模型，以提升建築法規檢核流程的效率與一致性。研究提出一種新方法，結合生成式人工智慧進行法規條文解釋，以及以 BIM 為基礎的自動化規則執行流程。此方法特別針對暖通空調（HVAC）系統中常見的空間衝突問題進行探討，因其管線複雜且尺寸龐大，最容易違反設計規範。選擇 HVAC 設計作為研究焦點，係因其合規性對於建築安全與運作功能至關重要。透過結合生成式 AI 與 BIM 工作流程，本研究旨在降低法規解讀所需的人工投入，並提供一套實用的方法，於設計階段即進行自動化合規檢查。

Mechanical, Electrical, and Plumbing (MEP) systems are complex and prone to issues since they often face clashes with other building elements. These clashes not only affect construction efficiency but can also lead to non-compliance with building codes, which directly impacts building performance and occupant safety. In current practice, interpreting mechanical code clauses and performing compliance checks remain manual tasks, requiring significant time and expert effort. While the use of Large Language Models (LLMs) has been explored in various construction-related studies, few have focused on developing tailored or customized LLMs for domain-specific tasks such as regulation interpretation and compliance checking. In this research, a domain-specific LLM was configured to improve the efficiency and consistency of the building code compliance checking process. The study proposes a new approach that integrates generative AI for interpreting building clauses with BIM-based automation for rule execution. The study explores how generative AI can assist in interpreting building code clauses, particularly those related to HVAC systems, which are often involved in spatial conflicts due to their complex routing and size. HVAC design is chosen as the focus because code compliance in these systems is critical to ensuring safe and functional building operation. By combining generative AI with BIM workflows, this research aims to reduce the manual effort in regulatory interpretation and offer a practical method for automating compliance checks during the design phase.

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