Volume 13, Issue 2 (2023)                   Naqshejahan 2023, 13(2): 125-144 | Back to browse issues page

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1- Department of Architecture, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2- Department of Architecture, College of Fine Arts, University of Tehran, Tehran, Iran , darabdiba@gmail.com
Abstract:   (624 Views)
Aims: The aim of the research is to use and apply the artificial intelligence network and data mining of the non-form pattern in the ten valuable landmark buildings of Tehran (1330s to 1350s) in the direction of modernization.

Methods: In the present study, the research method used in terms of purpose is applied-developmental and the method of study is descriptive-survey in terms of method and nature. In this research, the MLP (Multilayer perceptron) artificial intelligence network and clustering have been used to validate the non-form analysis of residential building plans in the period 1330-1350. The data were randomly divided into three sets, 70% of the data were used for training, 15% for validation, and 15% for testing.

Results: According to the analysis and matching with non-formal analysis, the results show that plans have 15, 14, 13 and 11 components in terms of non-form. which exactly corresponds to the plan's amorphous analytical tables. Therefore, the results of the non-form analysis of the plans have been validated by artificial intelligence.

Conclusion: Modernization of buildings and preservation of historical buildings are important for the majority of people and the results of this research showed that by using modern technology such as creating an artificial intelligence network, it is possible to find the invisible and hidden components in the plans of the mentioned period and use them in today's residential plans. The use of modern technologies such as artificial intelligence in order to cluster and identify the hidden relationships of plans can be very helpful.
Article number: 7
Full-Text [PDF 1144 kb]   (657 Downloads)    
Article Type: Original Research | Subject: Highperformance Architecture
Received: 2023/03/28 | Accepted: 2023/06/22 | Published: 2023/06/22

References
1. Diba D. Contemporary architecture of Iran. Architectural Design. 2012 May;82(3):70-9. https://doi.org/10.1002/ad.1406 [Article] [DOI]
2. Heydari Delgarm M, Bemanian M, Ansari M. Purposes and Elements of Stylistic Narrative in the Works of Mohammad-Karim Pirnia and Donald Wilber. Journal of Iranian Architecture Studies, 2022; 5(10): 31-48. Available at: https://jias.kashanu.ac.ir/article_111768.html?lang=en [Article]
3. Hamejani Y, Bayzidi, Q., Sahabi, J. A Qualitative Study of Implications of Meaning in Hawraman-Takht Architecture from Semiotics Perspective. The Monthly Scientific Journal of Bagh-e Nazar, 2018; 14(57): 45-62. Available at: https://www.bagh-sj.com/article_57877.html?lang=en [Article]
4. Ahmadi M, Ansari M, Bemanian M. Geometric Data Mining and Shape Grammar of Relationship between House and Islamic Iranian Lifestyle. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2021 Apr 10;11(1):1-14. [Persian] https://dorl.net/dor/20.1001.1.23224991.1400.11.1.6.9 [Article]
5. Koosheshgaran S A A, Golvardi M. A Classification of Traditional Period Architecture an Introduction to the Order of Rural Architecture (According to explaining and criticizing Christian Norberg- Schulz’s thought). JHRE 2013; 32 (143):101-120. Available at: http://jhre.ir/article-1-195-fa.html [Article]
6. Talebi, H., Hojjat, E., farzian, M. Studying Roles of Government, Public, and Architects in Emersion of Low-rise Housing Complexes During the Second Pahlavi Period. Honar-Ha-Ye-Ziba: Memary Va Shahrsazi, 2014; 19(1): 23-32. doi: 10.22059/jfaup.2014.55373 [Article]
7. Latifi M, Diba D. Data Mining of the Spatial Structure of Qajar Native Housing; Case Study: Jangjouyan House of Isfahan. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2020 Oct 10;10(3):163-71. [Persian] https://dorl.net/dor/20.1001.1.23224991.1399.10.3.7.7 [Article]
8. Mahdavinejad M. Discourse of High-Performance Architecture: A Method to Understand Contemporary Architecture. Hoviatshahr, 2017 Aug 23;11(2):53-67. [Persian] Available at: http://hoviatshahr.srbiau.ac.ir/article_10930_79f91b76bac9a77aba9d4aff60465705.pdf [Article]
9. Mahdavinejad M. High-Performance Architecture: Search for Future Legacy in Contemporary Iranian Architecture. Armanshahr Architecture & Urban Development, 2017 Mar 14;9(17):129-138. [Persian] Available at: http://www.armanshahrjournal.com/article_44611_955a20b5cfd1f32308e627ddc8528b91.pdf [Article]
10. Mahdavinejad M. Designerly Approach to Energy Efficiency in High-Performance Architecture Theory. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2020 Sep 10;10(2):75-83. [Persian] https://dorl.net/dor/20.1001.1.23224991.1399.10.2.7.5 [Article]
11. Amini M, Mahdavinejad M, Bemanian M. Future of Interactive Architecture in Developing Countries: Challenges and Opportunities in Case of Tehran. Journal of Construction in Developing Countries. 2019;24(1):163-84. https://doi.org/10.21315/jcdc2019.24.1.9 [Article] [DOI]
12. Mahdavinejad M, Amini M. Public participation for sustainable urban planning in in case of Iran. Procedia engineering, 2011; 21: 405-13. https://doi.org/10.1016/j.proeng.2011.11.2032 [Article] [DOI]
13. Ansari S, Andalib A. An Evaluation Framework for Measuring Participation in Urban Renovation Projects and it’s Application in The Special Renovation Project of Shahid-Khoob-Bakht Neighborhood. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2016 Jul 10;6(1):5-17. [Persian] https://dorl.net/dor/20.1001.1.23224991.1395.6.1.5.3 [Article]
14. Kamelnia H. Community Architecture Approach in Cluster Housing Design; Assessing Methods of Participation in Contemporary Housing Design. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2013 Oct 10;3(2):63-73. [Persian] https://dorl.net/dor/20.1001.1.23224991.1392.3.2.1.0 [Article]
15. Maghsoud M, Nasr T. ITC-based Technologies and Green Strategy for Contemporization of Tehran Silo. Naqshejahan-Basic studies and New Technologies of Architecture and Planning. 2022 Mar 10;12(1):1-9. https://dorl.net/dor/20.1001.1.23224991.1401.12.1.2.2 [Article]
16. Mansouri R, Nasr T. Study of Impact of Virtual Site Survey in Understanding Architectural Value by Students; Case Study: Peter Behrens Building in Tehran Gewerbeschule. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2022; 12(3):122-140. https://dorl.net/dor/20.1001.1.23224991.1401.12.3.6.0 [Article]
17. Dehghanbanadaki A, Sotoudeh MA, Golpazir I, Keshtkarbanaeemoghadam A, Ilbeigi M. Prediction of geotechnical properties of treated fibrous peat by artificial neural networks. Bulletin of Engineering Geology and the Environment. 2019 Apr 3;78(3):1345-58. https://doi.org/10.1007/s10064-017-1213-2 [Article] [DOI]
18. Najafi Teroujeni, S. M., Nahibi, S., Moosavi Fatemi, H. Presenting Strategies for the Revitalization of the Safavid Gardens of Behshahr, Emphasizing the Components of Green Landscape Design. Sustainability, Development & Environment. 2022; 3(2): 1-24. Available at: https://jsde.srbiau.ac.ir/article_20485.html?lang=en [Article]
19. Latifi M, Mahdavinejad M, Diba D. The Home Architecture Data Mining from a Spatial Structure Perspective (Case Study: Jangjouyan House). International Journal of Applied Arts Studies (IJAPAS). 2020 Aug 16;5(1):57-76. Available at: http://www.ijapas.com/index.php/ijapas/article/view/279 [Article]
20. Latifi M, Mahdavinejad M. Contemporization of Isfahan Indigenous Housing Model based on Analysis of non-Morphic Relationships of Plan, Case Study: Jangjouian House. Journal of Iranian Architecture Studies. 2022 Sep 13; 11(21): 185-203. doi: 10.22052/jias.2022.245859.0 [Article] [DOI]
21. Rahbar M, Mahdavinejad M, Markazi A.H.D., Bemanian M. Architectural layout design through deep learning and agent-based modeling: A hybrid approach. Journal of Building Engineering. 2022 April15; 47, 103822. https://doi.org/10.1016/j.jobe.2021.103822 [Article] [DOI]
22. Latifi M, Daneshjoo K. The Creation of an Architectural Work within the Creation of the Universe Regarding the Holy Quran. Naqshejahan - Basic studies and New Technologies of Architecture and Planning. 2016 Sep 10; 6(2): 5-15. [Persian] https://dorl.net/dor/20.1001.1.23224991.1395.6.2.3.3 [Article]
23. Iranishad A, Habib F, Mahdavinejad M. Contemporization of Historical Neighborhoods with the Aim of Urban Spaces Place Making. Urban Management Studies. 2019 Feb 20; 10(36): 41-60. [Persian] Available at: http://ums.srbiau.ac.ir/article_13939.html?lang=en [Article]
24. Iranishad A, Habib F. Reconnection to Context: Place-based Contemporization and Reuse of Tehran Valuable Houses. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2021 Jun 10;11(2):1-26. [Persian] https://dorl.net/dor/20.1001.1.23224991.1400.11.2.1.6 [Article]
25. Bolouhari S, Barbera L, Etessam I. Learning Traditional Architecture for Future Energy-Efficient Architecture in the Country; Case Study: Yazd City. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning, 2020 Sep 10;10(2):85-93. [Persian] https://dorl.net/dor/20.1001.1.23224991.1399.10.2.3.1 [Article]
26. Esmaeilian Toussi H, Etessam E. Analysis of the Architecture of the Industrial Heritage Using a Combined Method of Typology and Analytical Shape Grammar (Case Study of Textile Factories of Isfahan and Yazd in the Pahlavi Era). Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2019 Mar 10;9(1):1-12. [Persian] https://dorl.net/dor/20.1001.1.23224991.1398.9.1.6.5 [Article]
27. Esmaeilian Toussi H, Etesam I, Mahdavinejad M. The Application of Evolutionary Algorithms and Shape Grammar in the Design Process Based upon Traditional Structures. The Monthly Scientific Journal of Bagh-e Nazar, 2021 May;18(95):19-36. https://doi.org/10.22034/BAGH.2019.161797.3914 [Article] [DOI]
28. Hanachi P, Diba D, Mahdavinejad M. Development and Conservation in the Case of Valuable Districts of Iranian Historic Cities. Journal of Faculty of Fine Arts 2008; 32(32): 51-60. Available at: https://journals.ut.ac.ir/article_18864.html?lang=en [Article]
29. Kalantari Khalilabad H, Haghi M, Dadkhah M. Social Components of Islamic-Iranian Urban Planning Pattern. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2014;4(1):17-26. [Persian] https://dorl.net/dor/20.1001.1.23224991.1393.4.1.6.0 [Article]
30. Keshtkar GA, Ansari M, Nazi DS. Developing green roof system in accordance with sustainable development. Hoviateshahr, 2010; 4(6): 15-28. [Persian] Available at: http://hoviatshahr.srbiau.ac.ir/article_1119.html [Article]
31. Mahdavinejad M, Bemanian M, Abolvardi G, Elhamian SM. Analyzing the state of seismic consideration of architectural non‐structural components (ANSCs) in design process (based on IBC). International Journal of Disaster Resilience in the Built Environment, 2012 Jul 13;3(2):133-147. https://doi.org/10.12691/rse-2-1-7 [Article] [DOI]
32. Mahdavinejad M, Hosseini SA. Data mining and content analysis of the jury citations of the Pritzker Architecture prize (1977–2017). Journal of Architecture and Urbanism. 2019 Feb 1;43(1):71-90. https://doi.org/10.3846/jau.2019.5209 [Article] [DOI]
33. Nguyen AT, Reiter S, Rigo P. A review on simulation-based optimization methods applied to building performance analysis. Applied energy. 2014 Jan 1;113:1043-58. https://doi.org/10.1016/j.apenergy.2013.08.061 [Article] [DOI]
34. Pan Y, Zhang L. Data-driven estimation of building energy consumption with multi-source heterogeneous data. Applied Energy. 2020 Jun 15;268:114965. https://doi.org/10.1016/j.apenergy.2020.114965 [Article] [DOI]
35. Walker S, Khan W, Katic K, Maassen W, Zeiler W. Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings. Energy and Buildings. 2020 Feb 15;209:109705. https://doi.org/10.1016/j.enbuild.2019.109705 [Article] [DOI]
36. Mohandes SR, Zhang X, Mahdiyar A. A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing. 2019 May 7;340:55-75. https://doi.org/10.1016/j.neucom.2019.02.040 [Article] [DOI]
37. Beccali M, Ciulla G, Brano VL, Galatioto A, Bonomolo M. Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy. Energy. 2017 Oct 15;137:1201-18. https://doi.org/10.1016/j.energy.2017.05.200 [Article] [DOI]
38. Olofsson T, Andersson S. Overall heat loss coefficient and domestic energy gain factor for single-family buildings. Building and Environment. 2002 Nov 1;37(11):1019-26. https://doi.org/10.1016/S0360-1323(01)00094-4 [Article] [DOI]
39. Rahbar M, Mahdavinejad M, Bemanian M, Davaie Markazi AH, Hovestadt L. Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs. Applied Artificial Intelligence. 2019 Jul 3;33(8):689-705. https://doi.org/10.1080/08839514.2019.1592919 [Article] [DOI]
40. [40]. Rahbar M, Mahdavinejad M, Bemanian M, Davaie-Markazi A. Generating space layout heat maps with cGAN algorithms in artificial intelligence. Armanshahr Architecture & Urban Development. 2020;13(32):131-142. https://doi.org/10.22034/aaud.2020.154406.1717
41. Goodarzi P, Ansari M, Mahdavinejad M, Haghighatbin M. Explain the Methodology for Decision Support Systems in the Early Stages of the Landscape Architecture Design Process Based on the Systemic Design Approach. EasyChair; 2021 Jun 15. Available at: https://easychair.org/publications/preprint/5dF1 [Article]
42. Mahdavinejad M, Bitaab N. From Smart-Eco Building to High-Performance Architecture: Optimization of Energy Consumption in Architecture of Developing Countries. E&ES. 2017 Aug;83(1): 012020. https://doi.org/10.1088/1755-1315/83/1/012020 [Article] [DOI]
43. Rahbar M, Mahdavinejad, M, Bemanian, M, Davaie-Markazi, A. Artificial neural network for outlining and predicting environmental sustainable parameters. Journal of Sustainable Architecture and Urban Design. 2020;7(2):169-182. https://doi.org/10.22061/jsaud.2019.4501.1333 [Article] [DOI]
44. Hsu YH, Juan YK. ANN-based decision model for the reuse of vacant buildings in urban areas. International Journal of Strategic Property Management. 2016 Jan 2;20(1):31-43. Available at: https://www.tandfonline.com/doi/abs/10.3846/1648715X.2015.1101626 [Article]
45. Desai M, Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth. 2021 Jan 1;4:1-1. https://doi.org/10.1016/j.ceh.2020.11.002 [Article] [DOI]
46. Zhang J, Li C, Yin Y, Zhang J, Grzegorzek M. Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artificial Intelligence Review. 2023 Feb;56(2):1013-70. https://doi.org/10.1007/s10462-022-10192-7 [Article] [DOI]
47. Diba D. L'Iran et l'architecture contemporaine. Mimar (Singapore). 1991;38:20-25. [French] Available at: francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19648743 [Article]
48. Diba D, Dehbashi M. Trends in modern Iranian architecture. J Iran Archit Chang Soc. 2004:31-41. Available at: https://b2n.ir/a12379 [Article]
49. Pourzargar M. Posto-Corona Visioning for Sustainable Adaptive Reuse of Kahrzak Sugar Factory. Naqshejahan-Basic studies and New Technologies of Architecture and Planning. 2022 Jan 10;11(4):79-95. [Persian] https://dorl.net/dor/20.1001.1.23224991.1400.11.4.7.6 [Article]
50. Car Z, Baressi Šegota S, Anđelić N, Lorencin I, Mrzljak V. Modeling the spread of COVID-19 infection using a multilayer perceptron. Computational and mathematical methods in medicine. 2020 May 29;2020. https://doi.org/10.1155/2020/5714714 [Article] [DOI]
51. Shaeri J, Mahdavinejad M. Prediction Indoor Thermal Comfort in Traditional Houses of Shiraz with PMV/PPD model. International Journal of Ambient Energy. 2022 Dec 31;43(1):8316-34. https://doi.org/10.1080/01430750.2022.2092774 [Article] [DOI]
52. [52]. Javanroodi K, Mahdavinejad M, Nik VM. Impacts of urban morphology on reducing cooling load and increasing ventilation potential in hot-arid climate. Applied Energy. 2018; 231: 714-46. https://doi.org/10.1016/j.apenergy.2018.09.116
53. Javanroodi K, Nik VM, Mahdavinejad M. A novel design-based optimization framework for enhancing the energy efficiency of high-rise office buildings in urban areas. Sustainable Cities and Society. 2019; 49:101597. https://doi.org/10.1016/j.scs.2019.101597 [Article] [DOI]
54. Heidari AA, Faris H, Mirjalili S, Aljarah I, Mafarja M. Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. Nature-Inspired Optimizers: Theories, Literature Reviews and Applications. 2020:23-46. https://doi.org/10.1007/978-3-030-12127-3_3 [Article] [DOI]
55. Askari A, Mahdavinejad M, Ansari M. Investigation of displacement ventilation performance under various room configurations using computational fluid dynamics simulation. Building Services Engineering Research and Technology. 2022 May 7;43(5):627–643. https://doi.org/10.1177/01436244221097312 [Article] [DOI]
56. [56]. Shaeri J, Mahdavinejad M, Pourghasemian MH. A new design to create natural ventilation in buildings: Wind chimney. Journal of Building Engineering. 2022 Aug 22:105041. https://doi.org/10.1016/j.jobe.2022.105041
57. Rasoolzadeh M, Moshari M. Prioritizing for Healthy Urban Planning: Interaction of Modern Chemistry and Green Material-based Computation. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2021 May 10;11(1):94-105. [Persian] https://dorl.net/dor/20.1001.1.23224991.1400.11.1.7.0 [Article]
58. Saadatjoo P, Mahdavinejad M, Zhang G, Vali K. Influence of permeability ratio on wind-driven ventilation and cooling load of mid-rise buildings. Sustainable Cities and Society. 2021 Jul 1;70:102894. https://doi.org/10.1016/j.scs.2021.102894 [Article] [DOI]
59. Behnava B, Pourzargar M. Impact of New Materials on Dynamics of Four Recent Decades in Iranian Architecture 1980-2020. Naqshejahan - Basic studies and New Technologies of Architecture and Planning. 2021 Nov 10;11(3):49-66. [Persian] https://dorl.net/dor/20.1001.1.23224991.1400.11.3.5.2 [Article]
60. Xu Y, Li F, Asgari A. Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms. Energy. 2022 Feb 1;240:122692. https://doi.org/10.1016/j.energy.2021.122692 [Article] [DOI]
61. Mansourimajoumerd P, Mahdavinejad M, Niknia S, Shirvani M. Comprehensive Strategies for Optimization e_Energy System in Different Climate Zone. InThe 4th International Conference on Architecture, Arts and Applications www.iconfaaa.com 2020 Oct 12. Available at SSRN: https://ssrn.com/abstract=3709733 [Article]
62. Mansourimajoumerd P, Bazazzadeh H, Mahdavinejad M, Nia SN. Energy Efficiency and Building's Envelope: An Integrated Approach to High-Performance Architecture. Urban Planning and Architectural Design for Sustainable Development (UPADSD 2021). Florence, Italy, 14, Sep / 16, Sep 2021; Pp. 122-123. Available at: https://flore.unifi.it/bitstream/2158/1259071/6/UPADSD%202021_ATTI_Firenze.pdf#page=133 [Article]
63. Sarmadi H, Mahdavinejad M. A designerly approach to Algae-based large open office curtain wall Façades to integrated visual comfort and daylight efficiency. Solar Energy. 2023 Feb 1;251:350-65. https://doi.org/10.1016/j.solener.2023.01.021 [Article] [DOI]
64. Shaeri J, Mahdavinejad M. Prediction Indoor Thermal Comfort in Traditional Houses of Shiraz with PMV/PPD model. International Journal of Ambient Energy. 2022 Dec 31;43(1):8316-34. https://doi.org/10.1080/01430750.2022.2092774 [Article] [DOI]
65. Shams G, Rasoolzadeh M. Bauchemie: Environmental Perspective to Well-Building and Occupant Health. Naqshejahan - Basic Studies and New Technologies of Architecture and Planning. 2023 Jan 10; 12(4):51-69. https://dorl.net/dor/20.1001.1.23224991.1401.12.4.2.8 [Article]
66. Talaei M, Mahdavinejad M, Azari R, Haghighi HM, Atashdast A. Thermal and energy performance of a user-responsive microalgae bioreactive façade for climate adaptability. Sustainable Energy Technologies and Assessments. 2022 Aug 1;52:101894. https://doi.org/10.1016/j.seta.2021.101894 [Article] [DOI]
67. Xiangli Y, Xu L, Pan X, Zhao N, Rao A, Theobalt C, Dai B, Lin D. Citynerf: Building nerf at city scale. arXiv preprint arXiv:2112.05504. 2021 Dec 10. https://doi.org/10.48550/arXiv.2112.05504 [Article] [DOI]
68. Shaeri J, Mahdavinejad M, Zalooli A. Physico-mechanical and Chemical Properties of Coquina Stone Used as Heritage Building Stone in Bushehr, Iran. Geoheritage. 2022 Sep;14(3):1-11. https://doi.org/10.1007/s12371-022-00738-0 [Article] [DOI]
69. Moayedi H, Mosavi A. Synthesizing multi-layer perceptron network with ant lion biogeography-based dragonfly algorithm evolutionary strategy invasive weed and league champion optimization hybrid algorithms in predicting heating load in residential buildings. Sustainability. 2021 Mar 15;13(6):3198. https://doi.org/10.3390/su13063198 [Article] [DOI]
70. Shaeri J, Mahdavinejad M, Vakilinejad R, Bazazzadeh H, Monfared M. Effects of sea-breeze natural ventilation on thermal comfort in low-rise buildings with diverse atrium roof shapes in BWh regions. Case Studies in Thermal Engineering. 2023 Jan 1;41:102638. https://doi.org/10.1016/j.csite.2022.102638 [Article] [DOI]
71. Botero-Valencia JS, Valencia-Aguirre J, Durmus D, Davis W. Multi-channel low-cost light spectrum measurement using a multilayer perceptron. Energy and Buildings. 2019 Sep 15;199:579-87. https://doi.org/10.1016/j.enbuild.2019.07.026 [Article] [DOI]
72. Wang J, Li S, Chen H, Yuan Y, Huang Y. Data-driven model predictive control for building climate control: Three case studies on different buildings. Building and Environment. 2019 Aug 1;160:106204. https://doi.org/10.1016/j.buildenv.2019.106204 [Article] [DOI]
73. Finzi M, Welling M, Wilson AG. A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups. InInternational Conference on Machine Learning 2021 Jul 1 (pp. 3318-3328). PMLR. Available at: https://proceedings.mlr.press/v139/finzi21a.html [Article]
74. Almutairi K, Algarni S, Alqahtani T, Moayedi H, Mosavi A. A TLBO-Tuned neural processor for predicting heating load in residential buildings. Sustainability. 2022 May 13;14(10):5924. https://doi.org/10.3390/su14105924 [Article] [DOI]
75. Gharaati F, Mahdavinejad M, Nadolny A, Bazazzadeh H. Sustainable Assessment of Built Heritage Adaptive Reuse Practice: Iranian Industrial Heritage in the Light of International Charters. The Historic Environment: Policy & Practice. 2023 Oct 4:1-35. https://doi.org/10.1080/17567505.2023.2261328 [Article] [DOI]
76. Foroughian S, Zandieh M, Medi H, Karimi F, Mahdavinejad M. The Space Opening Number and Volume Effect in the Form of High Buildings Under the Wind Force. Tuijin Jishu/Journal of Propulsion Technology. 2023 Oct 16;44(4):6549-68. Available at: https://propulsiontechjournal.com/index.php/journal/article/view/2290/1546 [Article]
77. Ahmadi J, Mahdavinejad M, Larsen OK, Zhang C, Asadi S. Naturally ventilated folded double-skin façade (DSF) for PV integration-Geometry evaluation via thermal performance investigation. Thermal Science and Engineering Progress. 2023 Oct 1;45:102136. https://doi.org/10.1016/j.tsep.2023.102136 [Article] [DOI]
78. Dezfuli RR, Bazazzadeh H, Taban M, Mahdavinejad M. Optimizing stack ventilation in low and medium-rise residential buildings in hot and semi-humid climate. Case Studies in Thermal Engineering. 2023 Oct 28:103555. https://doi.org/10.1016/j.csite.2023.103555 [Article] [DOI]
79. Heidarzadeh S, Mahdavinejad M, Habib F. External shading and its effect on the energy efficiency of Tehran's office buildings. Environmental Progress & Sustainable Energy. 2023 May 17:e14185. https://doi.org/10.1002/ep.14185 [Article] [DOI]

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