ArchGPT: harnessing large language models for supporting renovation and conservation of traditional architectural heritage

The renovation of traditional architecture contributes to the inheritance of cultural heritage and promotes the development of social civilization. However, executing renovation plans that simultaneously align with the demands of residents, heritage conservation personnel, and architectural experts...

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Bibliographic Details
Published in:Heritage science 2024-06, Vol.12 (1), p.220-14, Article 220
Main Authors: Zhang, Jiaxin, Xiang, Rikui, Kuang, Zheyuan, Wang, Bowen, Li, Yunqin
Format: Article
Language:English
Subjects:
Architectural heritage
Architecture
Artificial intelligence
Artificial Intelligence agent
Automation
Chatbots
Chemistry and Materials Science
Communication
Conservation
Cultural heritage
Cultural resources
Deep learning
Design
Language
Large language models
Materials Science
Planning
Science
User requirements
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Summary:The renovation of traditional architecture contributes to the inheritance of cultural heritage and promotes the development of social civilization. However, executing renovation plans that simultaneously align with the demands of residents, heritage conservation personnel, and architectural experts poses a significant challenge. In this paper, we introduce an Artificial Intelligence (AI) agent, Architectural GPT (ArchGPT), designed for comprehensively and accurately understanding needs and tackling architectural renovation tasks, accelerating and assisting the renovation process. To address users’ requirements, ArchGPT utilizes the reasoning capabilities of large language models (LLMs) for task planning. Operating under the use of tools, task-specific models, and professional architectural guidelines, it resolves issues within the architectural domain through sensible planning, combination, and invocation. Ultimately, ArchGPT achieves satisfactory results in terms of response and overall satisfaction rates for customized tasks related to the conservation and restoration of traditional architecture.
ISSN:2050-7445
2050-7445
DOI:10.1186/s40494-024-01334-x