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Article
Open Access
AIstructure-Copilot: assistant for generative AI-driven intelligent design of building structures
1Department of Civil Engineering, Tsinghua University, Beijing 100084, China
2China Electronics Engineering Design Institute Co. Ltd, Beijing 100840, China
  • Volume
  • Citation
    Qin S, Liao W, Huang S, Hu K, Tan Z, Gao Y, Lu X. AIstructure-Copilot: assistant for generative AI-driven intelligent design of building structures. Smart Constr. 2024(1): 0001, https://doi.org/10.55092/sc20240001.
  • DOI
  • Copyright
    Copyright2024 by the authors. Published by ELSP.
Abstract

The rapid advancement of intelligent design technology in building structures has been primarily implemented in engineering practice through the use of local or cloud-based software to offer intelligent design services. However, local intelligent design services are time-consuming and require high-end hardware, whereas cloud-based designs fail to integrate seamlessly with existing design processes. Consequently, providing convenient intelligent design support for engineering practices is challenging. To address these problems, this study proposes a local–cloud collaborative intelligent design technology called AIstructure-Copilot, which serves as a structural intelligent design assistant. In this system, the local end performs routine graphical operations that align with engineers' design habits, whereas the cloud end executes generative artificial intelligence (AI) for intelligent design, thereby enhancing efficiency and effectively combining the strengths of both services. Specifically, this technology achieves a high level of automation and intelligence throughout the entire process, encompassing architectural design, structural design, and the establishment and execution of structural analysis models. This is accomplished by constructing a local–cloud collaborative mode, introducing a comprehensive data transmission format, and developing a cloud interface for generative AI algorithms. The effectiveness of the AIstructure-Copilot model was validated using a typical case study. The results demonstrate that AI design improves design efficiency by more than tenfold, satisfies the regulatory requirements of design schemes, and exhibits a discrepancy of approximately 20% when compared with designs created by competent engineers.

Keywords

intelligent design; local–cloud collaboration; generative AI; shear wall structure; complete design process

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