روش ها و کاربردهای هوش مصنوعی در معماری
DOI:
https://doi.org/%20/fa9ptk03Keywords:
هوش مصنوعی, یادگیری ماشین, معماری هوشمند, طراحی پایدارAbstract
با پیشرفت سریع فناوریهای هوش مصنوعی (AI) و یادگیری ماشین (ML)، حوزه معماری نیز دچار تحولات بنیادین شده است. این مقاله به بررسی روشها و کاربردهای متنوع هوش مصنوعی در طراحی، تحلیل و مدیریت پروژههای معماری میپردازد. در بخش نخست، الگوریتمهای AI شامل یادگیری ماشین و یادگیری عمیق معرفی شده و تواناییهای آنها در پردازش دادههای پیچیده و پشتیبانی از تصمیمگیری طراحی توضیح داده میشود. در بخش دوم، کاربردهای عملی این فناوریها در حوزههای مختلف معماری از جمله طراحی مفهومی و سهبعدی، بهینهسازی فرم و مصالح، شبیهسازی بررسی میشود. نتایج نشان میدهد که هوش مصنوعی نه تنها میتواند کارایی و خلاقیت در طراحی معماری را افزایش دهد، بلکه زمینه را برای توسعه معماری هوشمند، پایدار و مبتنی بر داده فراهم میکند. این مطالعه چارچوبی جامع برای پژوهشگران و معماران جهت بهرهگیری مؤثر از AI در تمامی مراحل طراحی و ساخت ارائه میدهد.
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References
[1] I. N. da Silva, D. H. Spatti, R. A. Flauzino, L. H. B. Liboni, and S. F. dos Reis Alves,
"Artificial neural network architectures and training processes," in Artificial Neural
Networks, ed: Springer, 2017, pp. 21-28.
[2] S. Mirjalili, S. Z. M. Hashim, and H. M. Sardroudi, "Training feedforward neural
networks using hybrid particle swarm optimization and gravitational search algorithm,"
Applied Mathematics and Computation, vol. 218, pp. 11125-11137, 2012.
[3] Y.-C. Harn. (2017). An overview of gradient descent optimization algorithm.
[4] S. Ruder, "An overview of gradient descent optimization algorithms," arXiv preprint
arXiv:1609.04747, 2016.
[5] G. Hinton, N. Srivastava, and K. Swersky, "Neural networks for machine learning
lecture 6a overview of mini-batch gradient descent," Cited on, p. 14, 2012.
[6] D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint
arXiv:1412.6980, 2014.
[7] J. Sohl-Dickstein, B. Poole, and S. Ganguli, "Fast large-scale optimization by unifying
stochastic gradient and quasi-Newton methods," in International Conference on Machine
Learning, 2014, pp. 604-612.
[8] J. H. Jo and J. S. Gero, "Space layout planning using an evolutionary approach,"
Artificial Intelligence in Engineering, vol. 12, pp. 149-162, 1998.
[9] P. H. Levin, "Use of graphs to decide the optimum layout of buildings," The Architects'
Journal, vol. 7, pp. 809-815, 1964.
[10] J. Grason, "An approach to computerized space planning using graph theory," in
Proceedings of the 8th Design Automation Workshop, 1971, pp. 170-178.
[11] H. E. Sanli and F. Eldemir, "Spiral facility Layout Generation and Improvement
Algorithm," in Proceedings of the International Material Handling Research Colloquium,
2010.
[12] J. M. Seehof and W. Evans, "Automated layout design program," Journal of Industrial
Engineering, vol. 18, pp. 690-695, 1967.
[13] R. S. Liggett and W. J. Mitchell, "Optimal space planning in practice," ComputerAided Design, vol. 13, pp. 277-288, 1981.
[14] G. Weinzapfel and S. Handel, "IMAGE: computer assistant for architectural design,"
Spatial synthesis in computer-aided building design, pp. 61-68, 1975.
[15] G. Stiny, "Two exercises in formal composition," Environment and Planning B:
Planning and Design, vol. 3, pp. 187-210, 1976.
[16] P. Galle, "An algorithm for exhaustive generation of building floor plans,"
Communications of the ACM, vol. 24, pp. 813-825, 1981.
[17] E. Shaviv, "Layout design problems: systematic approaches," in Computer-Aided
Architectural Design Futures, 1986, pp. 28-52.
[18] A. Schwarz, D. M. Berry, and E. Shaviv, "Representing and solving the automated
building design problem," Computer-aided design, vol. 26, pp. 689-698, 1994.
[19] C. A. Baykan, "Formulating spatial layout as a disjunctive constraint satisfaction
problem," 1992.
[20] K. Yoon and R. Coyne, "Reasoning about spatial constraints," Environment and
Planning B: Planning and Design, vol. 19, pp. 243-266, 1992.
[21] H. Kim, C. Jun, Y. Cho, and G. Kim, "Indoor Spatial Analysis Using Space Syntax,
The," in International Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences. Vol. XXXVII, Part B2, 2008.
[22] K. Dutta and S. Sarthak, "Architectural space planning using evolutionary computing
approaches: a review," Artificial Intelligence Review, vol. 36, p. 311, 2011.
[23] J. S. Gero and V. A. Kazakov, "Learning and re-using information in space layout
planning problems using genetic engineering," Artificial Intelligence in Engineering, vol.
11, pp. 329-334, 1997.
[24] F. Ramtin, M. Abolhasanpour, H. Hojabri, A. Hemmati, and A. Jaafari, "Optimal
multi floor facility layout," in Proceedings of the international multiConference of
engineers and computer scientists, 2010.
[25] S.-i. Nakano, "Enumerating floorplans with n rooms," IEICE transactions on
fundamentals of electronics, communications and computer sciences, vol. 85, pp. 1746-
1750, 2002.
[26] E. Rodrigues, A. R. Gaspar, and Á. Gomes, "An evolutionary strategy enhanced with
a local search technique for the space allocation problem in architecture, Part 2: Validation
and performance tests," Computer-Aided Design, vol. 45, pp. 898-910, 2013.
[27] B. C. Arabacioglu, "Using fuzzy inference system for architectural space analysis,"
Applied Soft Computing, vol. 10, pp. 926-937, 2010.
[28] Z. Guo and B. Li, "Evolutionary approach for spatial architecture layout design
enhanced by an agent-based topology finding system," Frontiers of Architectural Research,
vol. 6, pp. 53-62, 2017.
[29] J. Doran and I. Parberry, "Controlled procedural terrain generation using software
agents," IEEE Transactions on Computational Intelligence and AI in Games, vol. 2, pp.
111-119, 2010.
