Training cGan Algorithm for Generating Architectural Layout Heat Map

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RAHBAR MORTEZA; MAHDAVINEJAD MOHAMMADJAVAD; BEMANIAN MOHAMMADREZA; Davaei Markazi Amirhossein

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Journal Paper

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Journal: ARMANSHAHR Year:2020 | Volume:13 | Issue:32 Page(s): 131-142

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Information Journal Paper

Title

Training cGan Algorithm for Generating Architectural Layout Heat Map

Author(s)

RAHBAR MORTEZA | MAHDAVINEJAD MOHAMMADJAVAD | BEMANIAN MOHAMMADREZA | Davaei Markazi Amirhossein | Issue Writer Certificate

Keywords

Artificial IntelligenceQ1

High-PerformanceQ1

ArchitectureQ2

Data-Oriented DesignQ1

Future ArchitectureQ1

Abstract

The maps of the space layout have been considered by the architects as one of the first steps of the architectural design process. The theoretical framework of the High-Performance Architecture emphasizes that the topological and geometrical structure of these maps is adopted from the latent concepts. These concepts were formed under the influence of the subjective and objective variables. According to the research hypothesis, the space layout maps are subject to the latent patterns that are the basis for their formation. Using the computational strength for contributing to predicting the space layouts has always been a controversial issue in contemporary Architecture and has been the prospect for future Architecture. The current paper used the data-driven Artificial Intelligence methods for generating the heat maps of the space layout. Despite the conventional methods that try to define the layout plans based on the absolute mathematical relations, the designed method tries to take the spatial layout generator function from the experience of designing successful patterns with a designed based approach. Therefore, a set of 300 plans of the apartments in Tehran has been provided, and four types of different inputs have been supplied for training the Artificial Intelligence model. In the present research, cGan algorithm was used as one of the most efficient algorithms. This algorithm creates Artificial Intelligence and has been trained based on the provided layout patterns. This algorithm can regulate the mapping function to generate the target image based on the input image. After completing the process of training the cGAN model, the heat maps of the space layouts of 10 new apartments were tested. Also, the quality of the predicted answers was evaluated based on the predetermined five regulations. The suggested model based on the design-based approach is following modern construction technologies, such as the application of metadata, deep learning, machine learning, efficiency and smart consumption of energy, and energy-view optimization.

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APA: Copy

RAHBAR, MORTEZA, MAHDAVINEJAD, MOHAMMADJAVAD, BEMANIAN, MOHAMMADREZA, & Davaei Markazi, Amirhossein. (2020). Training cGan Algorithm for Generating Architectural Layout Heat Map. ARMANSHAHR, 13(32 ), 131-142. SID. https://sid.ir/paper/399333/en

Vancouver: Copy

RAHBAR MORTEZA, MAHDAVINEJAD MOHAMMADJAVAD, BEMANIAN MOHAMMADREZA, Davaei Markazi Amirhossein. Training cGan Algorithm for Generating Architectural Layout Heat Map. ARMANSHAHR[Internet]. 2020;13(32 ):131-142. Available from: https://sid.ir/paper/399333/en

IEEE: Copy

MORTEZA RAHBAR, MOHAMMADJAVAD MAHDAVINEJAD, MOHAMMADREZA BEMANIAN, and Amirhossein Davaei Markazi, “Training cGan Algorithm for Generating Architectural Layout Heat Map,” ARMANSHAHR, vol. 13, no. 32 , pp. 131–142, 2020, [Online]. Available: https://sid.ir/paper/399333/en

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