Research on intelligent aided design of architectural space based on deep learning

Xi Chen

doi:10.1117/12.3005872

10 October 2023 Research on intelligent aided design of architectural space based on deep learning

Xi Chen

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279922 (2023) https://doi.org/10.1117/12.3005872
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Information technology has been developing rapidly in recent years, the application of computer aided design (CAD) in architectural design is more and more. Based on the computer-aided design method, designers can parameterize the design scheme in the software, and use a series of variables to control and transform the design scheme, which brings great convenience for the architect to optimize the process of architectural scheme. But the building performance optimization method based on intelligent algorithm is mainly used in scientific research and is difficult to put into practical engineering. Therefore, for the purpose of solving the above problems, this paper carries on the mathematical modeling to the building performance prediction problem which takes the building spatial parameters as the optimization variables. Based on BP neural network, this paper builds an accurate and efficient building performance prediction platform to assist designers to make decisions. The simulation results show that the prediction and optimization proposed here can give accurate and reliable optimal solutions, and the optimal construction scheme has strong rationality and high engineering application value.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xi Chen "Research on intelligent aided design of architectural space based on deep learning", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279922 (10 October 2023); https://doi.org/10.1117/12.3005872

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