کاربرد روش های یادگیری ماشینی در حوزه طراحی محیط مصنوع

[1] Haenlein, M. Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. 61(4). https://doi.org/10.1177/0008125619864925
[2] Mohammed, M. Badruddin Khan, M. Mohammed Bashier, E.B. (2017). Machine Learning: Algorithms and Applications. London; Taylor & Francis Group. https://doi.org/10.1201/9781315371658
[3]Eslamirad, N. Kolbadinejad S.M. Mahdavinejad, M. Mehranrad, M. (2020). Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran. Smart and Sustainable Built Environment.9(4):361-374. https://doi.org/10.1108/SASBE-03-2019-0028
[4] Beale, R. Jackson, T. (1990) . Neural Computing-An Introduction. Florida: CRC Press, Boca Raton. https://doi.org/10.1201/9781420050431
[5] Jordan, M.I. Mitchell, T.M. (2015). Machine learning: Trends,perspectives, and prospects. Science. 349. 255-260. http://dx.doi.org/10.1126/science.aaa8415
[6]Amini, M. Mahdavinejad, M. Bemanian, M.(2019) Future of Interactive Architecture in Developing Countries: Challenges and Opportunities in Case of Tehran. Journal of Construction in Developing Countries. 24(1):163-84. https://doi.org/10.21315/jcdc2019.24.1.9
[7] Goodarzi, P. Ansari, M. Rahimian, F.P. Mahdavinejad, M. Park, C.(2023).Incorporating sparse model machine learning in designing cultural heritage landscapes. Automation in Construction. 155. https://doi.org/10.1016/j.autcon.2023.105058
[8] Shalev-Shwartz, S. Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge. Cambridge University Press. https://doi.org/10.1017/CBO9781107298019.
[9] D. Penney, D. Chen, L. (2019). A Survey of Machine Learning Applied to Computer Architecture Design. Arxiv. 1-14. https://doi.org/10.48550/arXiv.1909.12373
[10] Rahbar, M, Mahdavinejad, M, Markazi, A.H.D. Bemanian, M. (2022) Architectural layout design through deep learning and agent-based modeling: A hybrid approach. Journal of Building Engineering.47, 103822. https://doi.org/10.1016/j.jobe.2021.103822
[11] Shabbir, J. Anwer, T. (2015). Artificial Intelligence and its Role in Near Future. Journal of Latex Class Files. 14(8). 1-11. https://doi.org/10.48550/arXiv.1804.01396
[12] Rahbar, M. Mahdavinejad, M. Bemanian, M. Davaie-Markaz, A.(2020). Generating space layout heat maps with cGAN algorithms in artificial intelligence. Armanshahr Architecture. Urban Development. 13(32):131-142. https://doi.org/10.22034/aaud.2020.154406.1717
[13] Rahbar, M. Mahdavinejad, M. Bemanian, M. Markazi,D. (2020) A. Artificial neural network for outlining and predicting environmental sustainable parameters. Journal of Sustainable Architecture and Urban Design.7(2):169-182. https://doi.org/10.22061/jsaud.2019.4501.1333 .
[14] Muhammad, I. Yan, Z. (2015). Supervised Machine Learning Approaches : a Survay. ICTACT Journal on Soft Computing. 5(3). 945-952. https://doi.org/10.48550/arXiv.1904.01460
[15] Kotsiantis, S.B.(2007) Supervised Machine Learning: A Review of Classification Techniques. Informatica.31(3). 249-268. ISSN: 0350-5596 https://dl.acm.org/doi/10.5555/1566770.1566773
[16] Usama, M. Qadir, J. Raza,A. Arif,H. Alvin yau,K. Elkhatib, Y. Hussain,A. Al-fuqaha, A.(2019). Unsupervised Machine Learning for Networking:Techniques, Applications and Research Challenges. Journal of IEEE Access. 7. 65579-65615. https://doi.org/10.48550/arXiv.1709.06599
[17] Khanum, M. Mahboob, T. Imtiaz, W. Abdul Ghafoor, H. Sehar.R. (2015). A Survey on Unsupervised Machine Learning Algorithms for Automation, Classification and Maintenance. International Journal of Computer Applications. 119(13). 34-39. http://dx.doi.org/10.5120/21131-4058
[18] Simeone,S. (2017). Brief Introduction to Machine Learning for Engineers. London : King’s College London. https://doi.org/10.48550/arXiv.1709.02840
[19] Sharma, D.& Kumar,N. A Review on Machine Learning Algorithms, Tasks and Applications. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET). (2017) 6(10). 1353-1360. https://www.scirp.org/reference/referencespapers?referenceid=2918741
[20] Llamas, J. M. Lerones, P. Medina, R. Zalama, E. García-Bermejo, J.G.(2017). Classification of Architectural Heritage Images Using Deep Learning Techniques. Applied Sciences. 7(10). 1-26. http://dx.doi.org/10.3390/app7100992
[21] LeCun, Y. Bengio, Y. Hinton, G. (2015) G. Deep learning. Nature. 521. 436-444. http://dx.doi.org/10.1038/nature14539
[22] Cioffi, R. Travaglioni,M. Piscitelli, G. Petrillo, A. De Felice, F. (2020). Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions. Sustainability. 12(2). 1-26. https://doi.org/10.3390/su12020492
[23] Khean, K. Fabbri,A. Haeusler.M.H. (2018). Learning Machine Learning as an Architect, How to?. eCAADe 36(1). 95-102. http://dx.doi.org/10.52842/conf.ecaade.2018.1.095 .
[24] Papasotiriou, T. (2019). Identifying the Landescape of Machine Learning Aided Architectural Design a Term Clustering and Scientometrics Study. International Conference of CAADRIA. 815-824. http://dx.doi.org/10.52842/conf.caadria.2019.2.815
[25] Tamke, M. Nicholas, P. Zwierzycki, M. (2018). Machine learning for architectural design. practices and infrastructure. International Journal of Architectural Computing. 16(2). 123-143. http://dx.doi.org/10.1177/1478077118778580
[26] Das, S. (2018) . Interactive Artificial Life Based Systems, Augmenting Design Generation and Evaluation by Embedding Expert Opinion A Human Machine dialogue for form finding. ECAADe. 36(1). 85-94. https://doi.org/10.52842/conf.ecaade.2018.1.085
[27] Racec,E. Budulan, S. Vellido,A. (2016). Computational Intelligence in architectural and interior design:a state-of-the-art and outlook on the field. Department of Computer Science (CS) and to the Universitat Politècnica de Catalunya,Barcelona, Spain. 1-10. https://www.cs.upc.edu/~avellido/research/RacecBudulanVellido_CCIA16.pdf
[28] Okhoya, V. (2020). Machine Learning for Multi-Discipline Parametric Analysis in Architectural Practice. Pennsylvania: Carnegie Mellon University. https://kilthub.cmu.edu/articles/thesis/Machine_Learning_for_Multi-Discipline_Parametric_Analysis_in_Architectural_Practice/11926932
[29] Michalek, J. Choudhary, R. Papalambros, P. (2002). Architectural Layout Design Optimization. Engineering Optimization 34(5). 461-484. http://dx.doi.org/10.1080/03052150214016
[30] Merrell, P. Schkufza, E. Koltun, V. (2010). Computer-Generated Residential Building Layouts. 29(6). 1-13. http://dx.doi.org/10.1145/1882261.1866203.
[31] Ferrando, C. (2018). Towards a Machine Learning Framework in Spatial Analysis, Master of Science (MS). Pennsylvania: Carnegie Mellon University , Pittsburgh. https://kilthub.cmu.edu/articles/thesis/Towards_a_Machine_Learning_Framework_in_Space_Syntax/7178417
[32] Al-Wattar,A. Areibi,S. Grewal,G. (2015). An Efficient Framework for Floor-plan Prediction of Dynamic Runtime Reconfigurable Systems. International Journal of Reconfigurable and Embedded Systems. 4(2) 99-121. https://doi.org/10.48550/arXiv.1611.05438
[33] M¨uller, P. Zeng,G. Wonka, P. Van Gool, L. (2007) Image-based Procedural Modeling of Facades. ACM Transactions on Graphics. 26(3). http://dx.doi.org/10.1145/1275808.1276484
[34] Udousoro, I. C. (2020). Machine Learning: A Review. Semiconductor Science and Information Devices, 2(2), 5–14. https://doi.org/10.30564/ssid.v2i2.1931
[35] Perez, J.A. Deligianni, F. Ravi, D. Yang, J.Z. (2018) . Artificial Intelligence and Robotics , Published by Arxiv in Cornell University.https://doi.org/10.48550/arXiv.1803.10813 .
[36] Chahal1, A. Gulia, P. (2019). Machine Learning and Deep Learning. International Journal of Innovative Technology and Exploring Engineering (IJITEE). 8 (12). 4910-4914. http://dx.doi.org/10.35940/ijitee.L3550.1081219
[37] Janiesch, C. Zschech, P. Heinrich, K. (2021). Machine learning and deep learning. International Journal of Electronic Market. 31. 685–695. https://doi.org/10.1007/s12525-021-00475-2
[38] Naeem,M. Hussain Rizvi ,S.T. Coronato, A.(2020). A Gentle Introduction to Reinforcement Learning and its Application in Different Fields. IEEE Access. 8. 209320 – 209344. https://doi.org/10.1109/ACCESS.2020.3038605
[39] Cudzik, J. Radziszewski, K. (2018). Artificial Intelligence Aided Architectural Design. ECAADe .36(1). 77-84. https://doi.org/10.52842/conf.ecaade.2018.1.077 .
[40] Yang, L. Cervone,G. (2019). Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event. Journal Soft Computing. 23(24). 13393-13408. http://dx.doi.org/10.1007/s00500-019-03878
[41] Hao, Z. (2019). Deep learning review and discussion of its future development. MATEC Web Conf. 277. https://doi.org/10.1051/matecconf/201927702035
[42] Yang, L. , MacEachren, A.M. Mitra, P. Onorati, T.(2018). Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review. International Journal of Geo-Information. 7(2). 1-38. https://doi.org/10.3390/ijgi7020065
[43] Yoshimura,Y. Zhoutong Wang, B. Ratti , C. (2018). Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence. Arxiv in Cornell University. 1-22. https://doi.org/10.48550/arXiv.1812.01714
[44] Goodman, G. (2019). A Machine Learning Approach to Artifificial Floorplan Generation. Kentucky : Lexington, Kentucky. https://doi.org/10.13023/etd.2019.391
[45] Llamas, J. M. Lerones, P. Medina, R. Zalama, E. Gómez, G.B. (2017). Classification of Architectural Heritage Images Using Deep Learning Techniques. Applied Sciences. 7(10). 1-25. http://dx.doi.org/10.3390/app7100992
[46] Jankovi, R. (2019). Machine Learning Models for Cultural Heritage Image Classification: Comparison Based on Attribute Selection. Information 2020, 11(1), 12. 1-13. https://doi.org/10.3390/info11010012
[47] Do, L. Swamy, M.N.S. (2013). Neural Networks and Statistical Learning. London. Springer. ISBN 978-1-4471-5571-3. https://doi.org/10.1007/978-1-4471-5571-3
[48] Popescu,M. Balas, V.E. Mastorakis, N.E. Popescu. L.P. (2009). Multilayer perceptron and neural networks. Journal of WSEAS Transactions on Circuits and Systems. 7(8). 579-588. https://doi.org/10.5555/1639537.1639542
[49] As, I. Pal,S. Basu,P. (2018). Artificial intelligence in architecture:Generating conceptual design via deep learning. International Journal of Architectural Computing, 16(4) 306-327. http://dx.doi.org/10.1177/1478077118800982
[50] Radziszewski, K. (2017). Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital. IOP Conference Series: Materials Science and Engineering. 245(6). 1-8. http://dx.doi.org/10.1088/1757-899X/245/6/062030
[51]Ganji Kheybari A, Diba D, Mahdavinejad M, Shahcheraghi A.(2015). Algorithmic Design of Palekane in Order to Increase Efficiency of Daylighting in Buildings. Armanshahr Architecture & Urban Development, Spring 8(1):35-52. [Persian] Available at: http://www.armanshahrjournal.com/article_39305_6474c6c97b35674314f99f744a694497.pdf
[52] Goodarzi, P. Ansari, M. Mahdavinejad, M. Russo A, Haghighatbin, M. Rahimian, F.P.(2023) Morphological analysis of historical landscapes based on cultural DNA approach. Digital Applications in Archaeology and Cultural Heritage.1;30:e00277. https://doi.org/10.1016/j.daach.2023.e00277
[53] Kasraei, M.H. Nourian, Y. Mahdavinejad, M.(2016) Girih for domes: analysis of three Iranian domes. Nexus Network Journal. 18(1):311-21. https://doi.org/10.1007/s00004-015-0282-4
[54] Mashhadi Abolghasem Shirazi, M. Diba, D. Mahdavinejad, M. (2023). Economy-based Contemporization and Preservation of Contemporary Architectural Heritage; Strategies for Action in Residential Buildings from the 1950s to the 1970s. The Monthly Scientific Journal of Bagh-e Nazar. 20(126): 69-80. (doi: 10.22034/bagh.2023.374094.5297) https://www.bagh-sj.com/article_181097.html?lang=en
[55] Tadayon, K. Mahdavinejad, M. Shahcheraghi, A. (2021) Advanced mathematical algorithms to outline integrated architectural design process. Journal of Sustainable Architecture and Urban Design.23;9(1):1-12. https://doi.org/10.22061/JSAUD.2020.6603.1686
[56] Kouchaki, M. Mahdavinejad, M. Zali, P. Ahmadi, S. (2016). Magnet-based Interactive Kinetic Bricks. eCAADe 2016 - 34th Annual Conference. University of Oulu, Oulu, Finland.1.213-218. https://papers.cumincad.org/data/works/att/ecaade2016_215.pdf
[57] Mansourimajoumerd, P. Mahdavinejad, M. (2018) . Kinetic Architecture: Reinterpreting Persian Mathematics and Astronomy. eCAADe 2018 - 36th Annual Conference.Lodz, Poland.1.605-612. http://dx.doi.org/10.52842/conf.ecaade.2018.1.605
[58] Haghighi, M.Y. Mahdavinejad, M. (2020). Heliophilia Intelligent Kinetic Canopy. , eCAADe 2020: 38th Annual Conference on Education and research in Computer Aided Architectural Design in Europe, TU-Berlin, Germany. 2. 243-250. http://papers.cumincad.org/data/works/att/ecaade2020_197.pdf
[59] Javidannia, G. Bemanian, B. Mahdavinejad, M. (2020). Performance Oriented Design Framework for Early Tall Building form Development: Seismic architecture view. eCAADe 2020: 38th Annual Conference on Education and research in Computer Aided Architectural Design in Europe, TU-Berlin, Germany.2. 381-390. (ID: D2.T9.S1. ecaade2020_069). http://papers.cumincad.org/data/works/att/ecaade2020_069.pdf
[60] Rezaeicherati, A. Mahdavinejad, M. (2020). SoRo Responsive Wall: Soft robotics for human-oriented architecture. eCAADe 2020: 38th Annual Conference on Education and research in Computer Aided Architectural Design in Europe, TU-Berlin, Germany. 2 .623-630. (ID: D2.T10.S2. ecaade2020_335). http://papers.cumincad.org/data/works/att/ecaade2020_335.pdf