## 8 Discussion
Moreover, the proposed system surpasses the analytical focus of BIM-GIS integration studies, such as that by [PERSON] and [PERSON] [51], by offering a scalable generative design tool applicable to diverse VVA typologies--including communal and beam houses. The significance of this research lies not only in providing a technical solution but also in bridging Vietnam's architectural heritage with future-oriented development. By digitizing traditional components and automating design processes, this study supports heritage preservation while promoting sustainable construction practices. This aligns with ecological design principles emphasized by [PERSON] [31] and [PERSON] [32] in their respective studies on vernacular architecture. Importantly, the generated BIM models serve as comprehensive data hubs throughout the building lifecycle, encompassing historical data, material specifications, structural integrity, and environmental performance. This centralized information repository enhances decision-making, reduces errors and rework, and improves collaboration among stakeholders across disciplines. In the context of VVA, such a data-driven approach not only ensures long-term preservation and authenticity but also integrates traditional architecture into a contemporary, manageable, and efficient framework. Furthermore, the findings pave the way for the development of specialized BIM libraries for VVA and promote interdisciplinary collaboration, as also advocated by [PERSON] and [PERSON] [51] in their work on BIM-GIS synergies. Despite these contributions, the study has several limitations that warrant consideration. First, by focusing predominantly on prevalent forms, especially those influenced by Northern Delta wooden architecture [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65], the model risks oversimplifying the rich diversity of VVA and underrepresenting regional variants. This concern echoes critiquesby [PERSON] et al. [50], regarding the limitations of rigid shape libraries in HBIM. Moreover, this narrow focus diverges from calls for broader regional exploration of Vietnamese architecture, as emphasized by [PERSON] and [PERSON] [26], underscoring the need for future studies to incorporate a wider range of regional styles and construction traditions. Second, operational limitations of the automation model must be acknowledged. The current model depends heavily on user-defined parameters, which can create accessibility barriers for practitioners lacking deep knowledge of VVA. This issue, also noted by [PERSON], [PERSON] and [PERSON] [48], highlights the challenge of ensuring usability for non-experts. Furthermore, while the automation algorithm developed herein represents a significant step forward, it remains limited in sophistication relative to the potential of more advanced systems. Future iterations could benefit from the integration of artificial intelligence (AI) and machine learning (ML) to enable autonomous identification and recommendation of architectural components based on historical datasets and learned design patterns, moving towards a more intelligent, user-friendly system. Lastly, the current study lacks empirical evaluations or real-world applications that could demonstrate the practical effectiveness of the proposed system. The absence of case studies limits the ability to assess performance in realistic contexts or benchmark against traditional methods. To address these limitations, several strategies for future research are proposed. First, the research scope should be significantly broadened to include diverse VVA styles from across Vietnam. This expansion should involve detailed documentation of region-specific construction methods, material usage, and cultural adaptations, ultimately supporting the creation of a more comprehensive national BIM library for Vietnamese heritage architecture.