الگوریتم سی‌گَن در تولید نقشه حرارتی جانمایی فضایی در طراحی معماری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه معماری، دانشکده معماری و شهرسازی، دانشگاه علم و صنعت ایران، تهران، ایران.

2 استاد گروه معماری، دانشکده هنر و معماری، دانشگاه تربیت مدرس، تهران، ایران.

3 استاد گروه معماری، دانشکده هنر و معماری، دانشگاه تربیت مدرس، تهران، ایران

4 استاد گروه مکاترونیک، دانشکده مهندسی مکانیک، دانشگاه علم و صنعت ایران، تهران، ایران.

چکیده

نقشه‌های جانمایی فضایی همواره به‌عنوان یکی از اولین مراحل فرآیند طراحی معماری مورد توجه معماران بوده است. چارچوب نظریه معماری سرآمد تأکید دارد که ساختار توپولوژیک و هندسی این نقشه‌ها برگرفته از مفاهیم پنهانی است که خود تحت تأثیر متغیرهای عینی و ذهنی شکل گرفته‌اند. بر اساس فرض پژوهش، نقشه‌های جانمایی فضایی تابع الگوهای نهانی هستند که مبانی شکل‌گیری آن‌هاست. استفاده از قدرت محاسباتی رایانه‮ها برای کمک در پیش‌بینی چیدمان جانمایی فضاها همواره از موضوعات چالش برانگیز در معماری معاصر و چشم‌اندازی برای معماری آینده بوده است. در این مقاله، از روش‌های داده‌محور هوش مصنوعی برای تولید نقشه‌های حرارتی جانمایی فضایی استفاده شده است. روش طراحی شده، برخلاف روش‌های متداول که سعی در تعریف پلان‌های جانمایی بر اساس روابط ریاضیاتی محض دارند، سعی دارد تا با رویکردی طراحی‮مبنا، تابع تولید چیدمان فضایی را برگرفته از تجربه طراحی الگوهای موفق قرار دهد. در این راستا یک مجموعه سیصد پلان جانمایی آپارتمان‌های تهران جمع‌آوری شده است. پلان‌ها در ابعاد مساحتی متفاوت و همه منطبق بر ضوابط طراحی انتخاب شده‌اند. سپس نقشه‌های حرارتی جانمایی این پلان‌ها تهیه شده و چهار نوع تصویر ورودی مختلف برای آموزش مدل هوش مصنوعی تهیه شده است. در این پژوهش از الگوریتم سی‌گَن به‌عنوان یکی از کارآمدترین الگوریتم‌های مولد هوش مصنوعی استفاده، و بر اساس الگوهای جانمایی تهیه شده آموزش داده شده است. این الگوریتم توانایی تنظیم تابع نگاشت، جهت تولید تصویر هدف بر اساس تصویر ورودی را دارد. پس از تکمیل فرآیند آموزش مدل هوش مصنوعی، نقشه‌های حرارتی چیدمان فضایی ده آپارتمان جدید توسط هوش مصنوعی تولید شده و کیفیت جواب‌های پیش‌بینی شده بر اساس پنج ضابطه از پیش تعریف شده ارزیابی شده است. الگوی پیشنهادی پژوهش با انگاره رویکرد طراحی مبنا به فناوری‌های نوین ساختمانی مانند: کاربرد ابرداده‌ها، یادگیری عمیق، یادگیری ماشینی، بهره‌وری و مصرف هوشمندانه انرژی، و انرژی- دیدکارایی هماهنگی دارد.

کلیدواژه‌ها


عنوان مقاله [English]

Training cGan Algorithm for Generating Architectural Layout Heat Map

نویسندگان [English]

  • Morteza Rahbar 1
  • Mohammadjavad Mahdavinejad 2
  • mohammadreza Bemanian 3
  • Amirhossein Davaei Markazi 4
1 Assistant Professor of Architecture, School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran.
2 Professor of Architecture, Faculty of Art and Architecture, Tarbiat Modares University, Tehran, Iran
3 Professor of Architecture, Faculty of Art and Architecture, Tarbiat Modares University, Tehran, Iran.
4 Professor of Control & Mechatronics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran.
چکیده [English]

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.

کلیدواژه‌ها [English]

  • Artificial intelligence
  • High-performance Architecture
  • Data-Oriented Design
  • Future Architecture
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