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Volume 40, Issue 3 (2025)
GeoRes 2025, 40(3): 253-265 |
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Received: 2025/06/9 | Accepted: 2025/08/16 | Published: 2025/09/1
Received: 2025/06/9 | Accepted: 2025/08/16 | Published: 2025/09/1
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Hosseini Z, Mofidi Shemirani S, Labib Zadeh R. Water Performance Optimization of Typical Residential Apartments in Tehran. GeoRes 2025; 40 (3) :253-265
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1- Department of Architecture, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Department of Architecture and Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Department of Architecture and Urban Development, Science and Research Branch, Islamic Azad University, Tehran, Iran
Abstract (341 Views)
Aims: The exponential increase in water consumption during the construction and operation phases of residential apartments in Tehran has led to critical water use conditions in the city. Accordingly, this study aims to optimize the geometrical characteristics of a typical residential apartment based on building water performance to inform urban-scale planning strategies.
Methodology: The research method combines building water performance modeling and simulation with regression modeling and optimization analysis. The simulation tools include Python programming and the Ladybug Tools plugin within the Grasshopper visual programming environment. Two optimization processes were conducted, each with 9,000 models generated across 300 generations, resulting in a total of 18,000 simulations.
Findings: Based on the dual optimization tests, the study achieved reductions of up to 53.98% in embodied water of construction materials, 56.76% in construction-phase water consumption 54.29% in operational water use, and 46.51% in mechanical systems water demand by altering spatial layout parameters of the apartment. The results indicate that east-west elongation of the residential unit on an equal building footprint, reduction of interior space area and height, placement of the vertical circulation core in the southeast, and positioning the living room on the west side with a north-south elongation significantly improve building water performance.
Conclusion: The spatial layout characteristics of residential apartments, in a complex and multidimensional model, have a significant impact on building water performance and, consequently, national water consumption. The proposed generative design model can reduce more than 50% of residential apartment water consumption from material production to the operation phase, thereby contributing to environmental, economic, and social sustainability, as well as promoting sustainable urban development.
Methodology: The research method combines building water performance modeling and simulation with regression modeling and optimization analysis. The simulation tools include Python programming and the Ladybug Tools plugin within the Grasshopper visual programming environment. Two optimization processes were conducted, each with 9,000 models generated across 300 generations, resulting in a total of 18,000 simulations.
Findings: Based on the dual optimization tests, the study achieved reductions of up to 53.98% in embodied water of construction materials, 56.76% in construction-phase water consumption 54.29% in operational water use, and 46.51% in mechanical systems water demand by altering spatial layout parameters of the apartment. The results indicate that east-west elongation of the residential unit on an equal building footprint, reduction of interior space area and height, placement of the vertical circulation core in the southeast, and positioning the living room on the west side with a north-south elongation significantly improve building water performance.
Conclusion: The spatial layout characteristics of residential apartments, in a complex and multidimensional model, have a significant impact on building water performance and, consequently, national water consumption. The proposed generative design model can reduce more than 50% of residential apartment water consumption from material production to the operation phase, thereby contributing to environmental, economic, and social sustainability, as well as promoting sustainable urban development.
References
1. Abbasi M, Tabesh M, Shahangian SA, Safarpour H (2025). Enviro-technical assessment of social responses to water demand management policies facing water scarcity. Journal of Water Resources Planning and Management. 151(8):04025030. [Link] [DOI:10.1061/JWRMD5.WRENG-6500]
2. Ahmadi J, Maddahi SM, Mirzaei R (2023). Generative design of housing spatial layout based on rectangular spaces. Advances in Civil Engineering. 2023(1):1142371. [Link] [DOI:10.1155/2023/1142371]
3. Antão-Geraldes AM, Pinto M, Afonso MJ, Albuquerque A, Calheiros CSC, Silva F (2023). Promoting water efficiency in a municipal market building: A case study. Hydrology. 10(3):69. [Link] [DOI:10.3390/hydrology10030069]
4. Ataman C, Dino İG (2022). Performative design processes in architectural practices in Turkey: Architects' perception. Architectural Engineering and Design Management. 18(5):690-704. [Link] [DOI:10.1080/17452007.2021.1995315]
5. Borah G (2025). Urban water stress: Climate change implications for water supply in cities. Water Conservation Science and Engineering. 10(1):20. [Link] [DOI:10.1007/s41101-025-00344-5]
6. Deb K, Pratap A, Agarwal S, Meyarivan TAMT (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation. 6(2):182-197. [Link] [DOI:10.1109/4235.996017]
7. Deslatte A, Koebele EA, Wiechman A (2025). Embracing the ambiguity: Tracing climate response diversity in urban water management. Public Administration. 103(1):250-272. [Link] [DOI:10.1111/padm.13017]
8. Dino IG (2016). An evolutionary approach for 3D architectural space layout design exploration. Automation in Construction. 69:131-150. [Link] [DOI:10.1016/j.autcon.2016.05.020]
9. Dino IG, Üçoluk G (2017). Multiobjective design optimization of building space layout, energy, and daylighting performance. Journal of Computing in Civil Engineering. 31(5):04017025. [Link] [DOI:10.1061/(ASCE)CP.1943-5487.0000669]
10. Ekici B, Cubukcuoglu C, Turrin M, Sariyildiz IS (2019). Performative computational architecture using swarm and evolutionary optimisation: A review. Building and Environment. 147:356-371. [Link] [DOI:10.1016/j.buildenv.2018.10.023]
11. Fattahi Tabasi S, Rafizadeh HR, Andaji Garmaroudi A, Banihashemi S (2023). Optimizing urban layouts through computational generative design: Density distribution and shape optimization. Architectural Engineering and Design Management. 20(5):1164-1184. [Link] [DOI:10.1080/17452007.2023.2243272]
12. Guo ZJ, Yu J, Zhang YZ, Zhang J, Chen Y, Wu Y, et al (2017). Water-stable in (III)-based metal-organic frameworks with rod-shaped secondary building units: Single-crystal to single-crystal transformation and selective sorption of C2H2 over CO2 and CH4. Inorganic Chemistry. 56(4):2188-2197. [Link] [DOI:10.1021/acs.inorgchem.6b02840]
13. He C, Liu Z, Wu J, Pan X, Fang Z, Li J, et al (2021). Future global urban water scarcity and potential solutions. Nature Communications. 12(1):4667. [Link] [DOI:10.1038/s41467-021-25026-3]
14. Hosseinian SM, Ghahari SM (2021). The relationship between structural parameters and water footprint of residential buildings. Journal of Cleaner Production. 279:123562. [Link] [DOI:10.1016/j.jclepro.2020.123562]
15. Hosseinian SM, Sabouri AG, Carmichael DG (2023). Sustainable production of buildings based on Iranian vernacular patterns: A water footprint analysis. Building and Environment. 242:110605. [Link] [DOI:10.1016/j.buildenv.2023.110605]
16. Kamazani MA, Dixit M, Shanbhag SS (2024). Interdependencies and tradeoffs in embodied and operational energy use, carbon emissions, and embodied water use of buildings. IOP Conference Series: Earth and Environmental Science. 1363(1):012024. [Link] [DOI:10.1088/1755-1315/1363/1/012024]
17. Keshavarzi M, Rahmani-Asl M (2021). Genfloor: Interactive generative space layout system via encoded tree graphs. Frontiers of Architectural Research. 10(4):771-786. [Link] [DOI:10.1016/j.foar.2021.07.003]
18. Li Z, Li S, Hinchcliffe G, Maitless N, Birbilis N (2024). Automated architectural space layout planning using a physics-inspired generative design framework. arXiv:2406.14840 [Preprint]. 2024 [cited 2025 Nov 20]. Available from: https://arxiv.org/abs/2406.14840 [Link]
19. Liu S, Zhou L (2025). LayoutGAN for automated layout design in graphic design: An application of generative adversarial networks. Informatica. 49(10). [Link] [DOI:10.31449/inf.v49i10.7099]
20. Liu Z, Zhang C, Guo Y, Osmani M, Demian P (2019). A building information modelling (BIM) based water efficiency (BWe) framework for sustainable building design and construction management. Electronics. 8(6):599. [Link] [DOI:10.3390/electronics8060599]
21. Pradeep T, Tam MK, Serrano JF (2024). Managing water, the mother of resources: Thoughts on world water day 2024. ACS Sustainable Resource Management. 1(3):368-369. [Link] [DOI:10.1021/acssusresmgt.4c00096]
22. Rahbar M, Mahdavinejad M, Markazi AH, Bemanian M (2022). Architectural layout design through deep learning and agent-based modeling: A hybrid approach. Journal of Building Engineering. 47:103822. [Link] [DOI:10.1016/j.jobe.2021.103822]
23. Saemian P, Tourian MJ, AghaKouchak A, Madani K, Sneeuw N (2022). How much water did Iran lose over the last two decades?. Journal of Hydrology: Regional Studies. 41:101095. [Link] [DOI:10.1016/j.ejrh.2022.101095]
24. Sharma AK, Chani PS (2024). Embodied water-energy nexus towards sustainable building construction: Case of conventional Indian houses. Journal of Building Engineering. 83:108453. [Link] [DOI:10.1016/j.jobe.2024.108453]
25. Su P, Lu W, Chen J, Hong S (2024). Floor plan graph learning for generative design of residential buildings: A discrete denoising diffusion model. Building Research & Information. 52(6):627-643. [Link] [DOI:10.1080/09613218.2023.2288097]
26. Thebuwena ACHJ, Samarakoon SSM, Ratnayake RC (2024). On the necessity for improving water efficiency in commercial buildings: A green design approach in hot humid climates. Water. 16(17):2396. [Link] [DOI:10.3390/w16172396]
27. Wang B, Lu W, Zhang Y (2025). A graph-enabled parametric modeling approach for façade layout generative design. Journal of Building Engineering. 105:112481. [Link] [DOI:10.1016/j.jobe.2025.112481]
28. Xia T, Ledbetter A, Bobe A, Hofland J, Krouwels B, Wang T, et al (2024). Interactive AI for generative housing design based on graph neural networks and deep generative models. Proceedings of the 2024 European Conference on Computing in Construction. Brussels: European Council on Computing in Construction (EC3). p. 469-477. [Link] [DOI:10.35490/EC3.2024.188]
29. Yan S, Wu C, Zhang Y (2025). Generative design for architectural spatial layouts: A review of technical approaches. Journal of Asian Architecture and Building Engineering. Latest Articles:1-21. [Link] [DOI:10.1080/13467581.2025.2512235]
30. Yi H (2016). User-driven automation for optimal thermal-zone layout during space programming phases. Architectural Science Review. 59(4):279-306. [Link] [DOI:10.1080/00038628.2015.1021747]
31. Yong L, Chibiao H (2022). A generative design method of building layout generated by path. Applied Mathematics and Nonlinear Sciences. 7(2):825-848. [Link] [DOI:10.2478/amns.2021.2.00168]
32. Zhang J, Liu N, Wang S (2021). Generative design and performance optimization of residential buildings based on parametric algorithm. Energy and Buildings. 244:111033. [Link] [DOI:10.1016/j.enbuild.2021.111033]