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  • تبیین مدل نظری تولید و توسعه پلان های معماری در تعامل الگوریتم های یادگیری ماشین و ژنتیک

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کد مقاله : JEST-2102-5295 (R1) بازدید : 175 صفحه: 1 - 15

10.30495/jest.2022.58808.5295

نوع مقاله: پژوهشی

تبیین مدل نظری تولید و توسعه پلان های معماری در تعامل الگوریتم های یادگیری ماشین و ژنتیک

محورهای موضوعی : معماری و شهرسازی

رضا باباخانی 1 , آزاده شاهچراغی 2 , حسین ذبیحی 3

1 - دکتری معماری، پژوهشگر، گروه معماری، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.
2 - دکتری معماری، دانشیار گروه معماری ، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.*(مسوول مکاتبات)
3 - دکتری شهرسازی، دانشیار گروه شهرسازی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

تاریخ دریافت : 1399/11/15 تاریخ پذیرش : 1399/12/18 تاریخ انتشار : 1401/03/01

کلید واژه: تولید پلان, تولید خودکار پلان, الگوریتم ژنتیک, یادگیری ماشین,

چکیده مقاله :

زمینه و هدف: مساله این پژوهش تبیین مدل نظری در جهت یافتن راهکاری نوین برای تولید و توسعه چیدمان فضایی پلان های معماری مبتنی بر روش های تعاملی و تلفیقی با کمک الگوریتم های یادگیری ماشین و ژنتیک است، در واقع هدف رسیدن به یک مدل نظری است که بیان می دارد الگوریتم های تکاملی به تنهایی مثمر ثمر نیستند، بلکه الگوریتم های یادگیری ماشین می توانند پلان ها را فراگیری کرده و مبنای مدل عملی شوند که به واسطه استفاده از الگوریتم های ژنتیک می توانند توسعه و تولید کننده نمونه های جدید باشند.روش بررسی: در همین راستا روش پژوهش ترکیبی شامل مطالعات کتابخانه ای، گردآوری داده های خام، بررسی نمونه های موردی و استفاده از فرمول های محاسباتی به صورت تابع های هدف و جریمه است.یافته ها: مطالعات این تحقیق نشان می دهد که الگوریتم ژنتیک توانایی حافظه سپاری ندارد و از طرفی مبنای محاسبات آن جهش و تصادفی عمل نمودن است که این فرآیند در تولید پلان های معماری به تنهایی اثربخش نخواهد بود.نتیجه گیری: نتایج پژوهش نشان می دهد که براساس مدل نظری ارائه شده، الگوریتم یادگیری ماشین به واسطه ساختار نمونه پذیر خود می تواند نمونه هایی را ذخیره و بازشناسی نماید و الگوریتم ژنتیک که یک الگوریتم جستجوگر و توسعه پذیر است، هر بار نمونه های بیشتری را از پلان های معماری براساس مدل ریاضی ارائه شده تولید نماید.

چکیده انگلیسی:

Background and Objective: The aim of this study is to explain to the theoretical model in order to find a new solution for the production and development of spatial arrangement of architectural plans based on interactive and integrated methods with the help of machine learning and genetic algorithms. Evolutionary algorithms alone are not effective, but machine learning algorithms can learn plans and form the basis of practical models that can develop and generate new samples through the use of genetic algorithms.Material and Methodology: In this regard, the combined research method includes library studies, collecting raw data, reviewing case samples, and using computational formulas as objective and penalty functions.Findings: Studies show that the genetic algorithm does not have the ability to store memory and on the other hand, the basis of its calculations is jumping and random action that this process is not effective in the production of architectural plans alone and research.Discussion and Conclusion:  findings show that the algorithm Machine learning, due to its exemplary structure, can store and recognize examples, and the genetic algorithm, which is a searchable and scalable algorithm, can produce more examples of architectural plans each time based on the proposed mathematical model.

منابع و مأخذ:


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_||_

  1. Zifeng Guon, Biao Li, 2016. Evolutionary approach for spatial architecture layout design enhanced by an agent-based topology finding system, Received 7 June 2016; received in revised form 22 October; accepted 7 November 2016, http://dx.doi.org/10.1016/j.foar.2016.11.003.
    2.
  2. R. S. Liggett and W. J. Mitchell, 1981. Optimal space planning in practice. Computer-Aided Design, 13(5): 277 {288, Sept.. ISSN 00104485. doi:10.1016/0010-4485(81)90317-1.
    3.
  3. Maurice Hanan and Jerome M. Kurtzberg, 1972. A Review of the Placement and Quadratic Assignment Problems, Vol. 14, No. 2 (Apr., 1972), pp. 324-342 (19 pages).
    4.
  4. J. F. Brotchie B. C.E., D. Eng.∗ M. P. T. Linzey B.E., M.E, 1971. A model for integrated building design, Building Science,Volume 6, Issue 3, September 1971, Pages 89-96, https://doi.org/10.1016/0007-3628(71)90020-X.
    5.
  5. Gil Bozer, James C Sarros, Joseph C Santora, 2013. The role of coachee characteristics in executive coaching for effective sustainability, March Journal of Management Development 32(3): 277-294, DOI: 10.1108/02621711311318319
    6.
  6. Lee D, et al, 2005. The proteasome regulatory particle alters the SAGA coactivator to enhance its interactions with transcriptional activators. Cell 123(3):423-36.
    7.
  7. Kai-Ping Huang 1*, Chih-Hsing Wang2, Meng-Chun Tseng4 and Karen Yuan Wang3,2010. A study on entrepreneurial orientation and resource acquisition: The effects of social capital, African Journal of Business Management Vol.4 (15), pp. 3226-3231, 4 November.
    8.
  8. JEREMY J. MICHALEKa, *, 2002. RUCHI CHOUDHARYb and PANOS Y. PAPALAMBROS, ARCHITECTURAL LAYOUT DESIGN OPTIMIZATION, Eng. Opt., Vol. 34(5), pp. 461–484, DOI: 10.1080=0305215021000033735
    9.
  9. Brian FinucanePatricia MaitaPatricia MaitaWilliam H. Isbell, 2006. Human and Animal Diet at Conchopata, Peru: Stable Isotope Evidence for Maize Agriculture and Animal Management Practices During the Middle Horizon, December 2006Journal of Archaeological Science 33(12):1766-1776, DOI: 10.1016/j.jas.03.012
    10.
  10. Luisa CaldasLeslie K. NorfordLeslie K. Norford, 2013. Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems, August Journal of Solar Energy Engineering 125(3) DOI: 10.1115/1.1591803
    11.
  11. A Papapavlou, A Turner ,2009. Structural evolution: a genetic algorithm method to generate structurally optimal delaunay triangulated space frames for dynamic loads, Computation: The New Realm of Architectural Design [27th eCAADe Conference Proceedings / ISBN 978-0-9541183-8-9] Istanbul (Turkey) 16-19 September 2009, pp. 173-180
    12.
  12. Li Li, 2012. The optimization of architectural shape based on Genetic Algorithm, received 6 April 2012; received in revised form 19 July 2012; accepted 23 July 2012, http://dx.doi.org/10.1016/j.foar.07.005
    13.
  13. Eug´enio Rodrigues, 2014.  Automated Floor Plan Design: Generation, Simulation, and Optimization Submitted to the Department of Mechanical Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy July.
    14.
  14. Maciej Nisztuk and Paweł B. Myszkowski, 2019. Hybrid Evolutionary Algorithm applied to Automated Floor Plan Generation, International Journal of Architectural Computing1–24 The Author(s), https://doi.org/10.1177/1478077119832
    15.
  15. Doulgerakis, 2017. Genetic Programming + Unfolding Embryology in Automated Layout Planning. Msc thesis, Bartlett School of Graduate Studies, University College London, September.
    16.
  16. Manisha Verma, Manish K Thakur, 2010. Architectural Space Planning using Genetic Algorithms, 978-1-4244-5586-7/10/$26.00IEEE C
    17.
  17. G. Zimmermann, 2005. From floor plan drafting to building simulation - an efficient software supported process. In I. Beausoleil-Morrison and M. Bernier, editors, International IBPSA Conference Building Simulation, pages 1441{1448, Montreal, Quebec, Canada.
    18.
  18. Maciej Nisztuk, Paweł Myszkowski,2019. Tool for evolutionary aided architectural design. Hybrid Evolutionary Algorithm applied to Multi-Objective Automated Floor Plan Generation, Design - GENERATIVE SYSTEMS - Volume 1 - eCAADe 37 / SIGraDi 23 | 61
    19.
  19. Garcia, Edel, 2018. (Cosine Similarity Tutorial. independent scientist.08-26.
    20.

 

 

 

 

 

 

 

 

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