Algorithmic Generation of Building Typology for Office Building Design
2. Primary Data
- The building typology to be developed should be as large as a European common public office building with a useful area between 5000–10,000 m2. The number of stories should be between 4–10 levels.
- To produce the office building typology requires basic cubes with an average and usable size of min. 4 × 4 × 3 m and max. 6 × 6 × 3 m. From such a basic unit (BU), it is possible to create both a small cell office and a co-working space by using several units. In this research, a size of 5 × 5 × 3 m was used, but other sizes can be implemented. The presented mathematical method is independent of this scaling.
- The BUs need to be grouped into so-called basic groups (BGs) in order to plan the spatial organization of a large-scale office building efficiently. The research worked with 4 × 4 units to create square groups (for design efficiency), but this can be achieved with other numbers of units. The presented mathematical method is independent of this.
- All levels are the same.
- Offices should have the same usable floor area as the atrium space on each level. The atrium is a multi-purpose zone where, in addition to the transport function, temporary meetings, events, reception, and project work can take place. On the upper floors, a gallery should be provided in 60% of the floor area of the atrium space, where a so-called semi-office can be established. The semi-office space performs the functions described above. The size of the semi-office should be 50% of the floor area of the gallery and can be provided on all levels.
- Inefficient office and corridor designs should be avoided. For example, amoeba-shaped, excessively long and narrow corridors and relief-type office contours.
Exemplary Modelling of an Office Building Space Arrangement Typology
3. Generating Possible Building Configurations
- the possible BG structure;
- the percentage of office space (as a whole number, e.g., 50);
- the percentage of the atrium section (also as a whole number, e.g., 50).
4. The Main Functions
4.1. Check the Number of Connected Spaces
4.2. Determination of Office Block Sizes
4.3. Checking Corner Connections
4.4. Space Organization of Atrium Corridors
4.5. Examining the Space Organization of Office Blocks: Staircase Rule
- left and right coordinates of the top row;
- left and right coordinates of the bottom row;
- top and bottom coordinates of the left-most column;
- top and bottom coordinates of the right-most column.
4.6. Examining the Space Organization of Office Blocks: The Bubble Rule
5. Trivial Tests and Calculations
5.1. Number of BUs Per Office Block with Indoor Connection
5.2. Number of BUs Per Office Block with Outdoor Connection
5.3. Number of Atrium Bus on the Building Wall
5.4. Atot/Stot–Ratio-Based Model Sorting
6. Results and Discussion
- Based on the Atot/Stot value for the building configuration;
- Based on internal office mass ratios.
6.1. Galleries in Atrium Spaces on Each Floor
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
- IEA. Global Status Report for Buildings and Construction 2019; IEA: Paris, France, 2019; Available online: https://www.iea.org/reports/global-status-report-for-buildings-and-construction-2019 (accessed on 7 July 2021).
- Norouzi, M.; Chàfer, M.; Cabeza, L.F.; Jiménez, L.; Boer, D. Circular economy in the building and construction sector: A scientific evolution analysis. J. Build. Eng. 2021, 44, 102704. [Google Scholar] [CrossRef]
- Pérez-Lombard, L.; Ortiz, J.; Pout, C. A review on buildings energy consumption information. Energy Build. 2008, 40, 394–398. [Google Scholar] [CrossRef]
- Allouhi, A.; El Fouih, Y.; Kousksou, T.; Jamil, A.; Zeraouli, Y.; Mourad, Y. Energy consumption and efficiency in buildings: Current status and future trends. J. Clean. Prod. 2015, 109, 118–130. [Google Scholar] [CrossRef]
- Cao, X.; Dai, X.; Liu, J. Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy Build. 2016, 128, 198–213. [Google Scholar] [CrossRef]
- Energy Information Administration (EIA). About the Commercial Buildings Energy Consumption Survey (CBECS); U.S. Department of Energy: Washington, DC, USA, 2018. Available online: https://www.eia.gov/consumption/commercial/data/2018/bc/html/b11.php (accessed on 7 July 2021).
- Cappelletti, F.; Prada, A.; Romagnoni, P.; Gasparella, A. Passive performance of glazed components in heating and cooling of an open-space office under controlled indoor thermal comfort. Build. Environ. 2014, 72, 131–144. [Google Scholar] [CrossRef]
- Giouri, E.D.; Tenpierik, M.; Turrin, M. Zero energy potential of a high-rise office building in a Mediterranean climate: Using multi-objective optimization to understand the impact of design decisions towards zero-energy high-rise buildings. Energy Build. 2020, 209, 109666. [Google Scholar] [CrossRef]
- Méndez Echenagucia, T.; Capozzoli, A.; Cascone, Y.; Sassone, M. The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis. Appl. Energy 2015, 154, 577–591. [Google Scholar] [CrossRef]
- Amaral, A.R.; Rodrigues, E.; Gaspar, A.R.; Gomes, Á. A thermal performance parametric study of window type, orientation, size and shadowing effect. Sustain. Cities Soc. 2016, 26, 456–465. [Google Scholar] [CrossRef][Green Version]
- Feng, F.; Kunwar, N.; Cetin, K.; O’Neill, Z. A critical review of fenestration/window system design methods for high performance buildings. Energy Build. 2021, 248, 111184. [Google Scholar] [CrossRef]
- Zhao, J.; Du, Y. Multi-objective optimization design for windows and shading configuration considering energy consumption and thermal comfort: A case study for office building in different climatic regions of China. Sol. Energy 2020, 206, 997–1017. [Google Scholar] [CrossRef]
- David, M.; Donn, M.; Garde, F.; Lenoir, A. Assessment of the thermal and visual efficiency of solar shades. Build. Environ. 2011, 46, 1489–1496. [Google Scholar] [CrossRef]
- Palmero-Marrero, A.I.; Oliveira, A.C. Effect of louver shading devices on building energy requirements. Appl. Energy 2010, 87, 2040–2049. [Google Scholar] [CrossRef]
- Magri Elouadjeri, S.; Boussoualim, A.; Ait Haddou, H. Evaluating the Effect of External Horizontal Fixed Shading Devices’ Geometry on Internal Air Temperature, Daylighting and Energy Demand in Hot Dry Climate. Case Study of Ghardaïa, Algeria. Buildings 2021, 11, 348. [Google Scholar] [CrossRef]
- González, J.; Fiorito, F. Daylight design of office buildings: Optimisation of external solar shadings by using combined simulation methods. Buildings 2015, 5, 560–580. [Google Scholar] [CrossRef][Green Version]
- Mangkuto, R.A.; Koerniawan, M.D.; Apriliyanthi, S.R.; Lubis, I.H.; Atthaillah; Hensen, J.L.M.; Paramita, B. Design Optimisation of Fixed and Adaptive Shading Devices on Four Façade Orientations of a High-Rise Office Building in the Tropics. Buildings 2022, 12, 25. [Google Scholar] [CrossRef]
- Wang, Y.; Wei, C. Design optimization of office building envelope based on quantum genetic algorithm for energy conservation. J. Build. Eng. 2021, 35, 102048. [Google Scholar] [CrossRef]
- Rapone, G.; Saro, O. Optimisation of curtain wall faades for office buildings by means of PSO algorithm. Energy Build. 2012, 45, 189–196. [Google Scholar] [CrossRef]
- Goia, F.; Haase, M.; Perino, M. Optimizing the configuration of a façade module for office buildings by means of integrated thermal and lighting simulations in a total energy perspective. Appl. Energy 2013, 108, 515–527. [Google Scholar] [CrossRef][Green Version]
- Silva, T.; Vicente, R.; Rodrigues, F. Literature review on the use of phase change materials in glazing and shading solutions. Renew. Sustain. Energy Rev. 2016, 53, 515–535. [Google Scholar] [CrossRef]
- Rabani, M.; Bayera Madessa, H.; Nord, N. Achieving zero-energy building performance with thermal and visual comfort enhancement through optimization of fenestration, envelope, shading device, and energy supply system. Sustain. Energy Technol. Assess. 2021, 44, 101020. [Google Scholar] [CrossRef]
- Bui, D.K.; Nguyen, T.N.; Ghazlan, A.; Ngo, N.T.; Ngo, T.D. Enhancing building energy efficiency by adaptive façade: A computational optimization approach. Appl. Energy 2020, 265, 114797. [Google Scholar] [CrossRef]
- Lin, Y.-H.; Tsai, K.-T.; Lin, M.-D.; Yang, M.-D. Design optimization of office building envelope configurations for energy conservation. Appl. Energy 2016, 171, 336–346. [Google Scholar] [CrossRef]
- Granadeiro, V.; Correia, J.R.; Leal, V.M.S.; Duarte, J.P. Envelope-related energy demand: A design indicator of energy performance for residential buildings in early design stages. Energy Build. 2013, 61, 215–223. [Google Scholar] [CrossRef]
- Chen, X.; Yang, H.; Peng, J. Energy optimization of high-rise commercial buildings integrated with photovoltaic facades in urban context. Energy 2019, 172, 1–17. [Google Scholar] [CrossRef]
- Vasileva, I.L.; Nemova, D.V.; Vatin, N.I.; Fediuk, R.S.; Karelina, M.I. Climate-Adaptive Façades with an Air Chamber. Buildings 2022, 12, 366. [Google Scholar] [CrossRef]
- Andjelković, A.S.; Cvjetković, T.B.; Djaković, D.D.; Stojanović, I.H. Development of simple calculation model for energy performance of double skin façades. Therm. Sci. 2012, 16, 251–267. [Google Scholar] [CrossRef]
- Szekeres, S.; Kostyák, A.; Szodrai, F.; Csáky, I. Investigation of Ventilation Systems to Improve Air Quality in the Occupied Zone in Office Buildings. Buildings 2022, 12, 493. [Google Scholar] [CrossRef]
- Guo, R.; Heiselberg, P.; Hu, Y.; Zhang, C.; Vasilevskis, S. Optimization of night ventilation performance in office buildings in a cold climate. Energy Build. 2020, 225, 110319. [Google Scholar] [CrossRef]
- Guo, R.; Gao, Y.; Zhuang, C.; Heiselberg, P.; Levinson, R.; Zhao, X.; Shi, D. Optimization of cool roof and night ventilation in office buildings: A case study in Xiamen, China. Renew. Energy 2020, 147, 2279–2294. [Google Scholar] [CrossRef][Green Version]
- Lim, T.; Yim, W.S.; Kim, D.D. Analysis of the Thermal and Cooling Energy Performance of the Perimeter Zones in an Office Building. Buildings 2022, 12, 141. [Google Scholar] [CrossRef]
- Espejel-Blanco, D.F.; Hoyo-Montaño, J.A.; Arau, J.; Valencia-Palomo, G.; García-Barrientos, A.; Hernández-De-león, H.R.; Camas-Anzueto, J.L. HVAC Control System Using Predicted Mean Vote Index for Energy Savings in Buildings. Buildings 2022, 12, 38. [Google Scholar] [CrossRef]
- Shan, X.; Luo, N.; Sun, K.; Hong, T.; Lee, Y.K.; Lu, W.Z. Coupling CFD and building energy modelling to optimize the operation of a large open office space for occupant comfort. Sustain. Cities Soc. 2020, 60, 102257. [Google Scholar] [CrossRef]
- Kang, J.; Weng, S.; Li, Y.; Ma, T. Study of Building Demand Response Method Based on Indoor Temperature Setpoint Control of VRV Air Conditioning. Buildings 2022, 12, 415. [Google Scholar] [CrossRef]
- Zhang, Y.; Vand, B.; Baldi, S. A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems. Buildings 2022, 12, 238. [Google Scholar] [CrossRef]
- Salimi, S.; Hammad, A. Optimizing energy consumption and occupants comfort in open-plan offices using local control based on occupancy dynamic data. Build. Environ. 2020, 176, 106818. [Google Scholar] [CrossRef]
- Wang, Y.; Quan, Z.; Jing, H.; Wang, L.; Zhao, Y. Performance and operation strategy optimization of a new dual-source building energy supply system with heat pumps and energy storage. Energy Convers. Manag. 2021, 239, 114204. [Google Scholar] [CrossRef]
- Sung, W.-P.; Chen, T.-Y.; Liu, C.-H. Strategy for Improving the Indoor Environment of Office Spaces in Subtropical Cities. Buildings 2022, 12, 412. [Google Scholar] [CrossRef]
- Clements-Croome, D.; Lei, Q.; Liu, S.; Yuan, C.; Qi, Y. Post-Occupancy Evaluation of the Biophilic Design in the Workplace for Health and Wellbeing. Buildings 2022, 12, 417. [Google Scholar]
- Wang, M.; Li, L.; Hou, C.; Guo, X.; Fu, H. Building and Health: Mapping the Knowledge Development of Sick Building Syndrome. Buildings 2022, 12, 287. [Google Scholar] [CrossRef]
- Maučec, D.; Premrov, M.; Leskovar, V.Ž. Use of sensitivity analysis for a determination of dominant design parameters affecting energy efficiency of timber buildings in different climates. Energy Sustain. Dev. 2021, 63, 86–102. [Google Scholar] [CrossRef]
- Alghamdi, S.; Tang, W.; Kanjanabootra, S.; Alterman, D. Effect of Architectural Building Design Parameters on Thermal Comfort and Energy Consumption in Higher Education Buildings. Buildings 2022, 12, 329. [Google Scholar] [CrossRef]
- Bano, F.; Sehgal, V. Finding the gaps and methodology of passive features of building envelope optimization and its requirement for office buildings in India. Therm. Sci. Eng. Prog. 2019, 9, 66–93. [Google Scholar] [CrossRef]
- Tian, Z.; Zhang, X.; Jin, X.; Zhou, X.; Si, B.; Shi, X. Towards adoption of building energy simulation and optimization for passive building design: A survey and a review. Energy Build. 2018, 158, 1306–1316. [Google Scholar] [CrossRef]
- Stevanović, S. Optimization of passive solar design strategies: A review. Renew. Sustain. Energy Rev. 2013, 25, 177–196. [Google Scholar] [CrossRef]
- Chen, X.; Yang, H.; Zhang, W. Simulation-based approach to optimize passively designed buildings: A case study on a typical architectural form in hot and humid climates. Renew. Sustain. Energy Rev. 2018, 82, 1712–1725. [Google Scholar] [CrossRef]
- Du, T.; Jansen, S.; Turrin, M.; van den Dobbelsteen, A. Effect of space layouts on the energy performance of office buildings in three climates. J. Build. Eng. 2021, 39, 102198. [Google Scholar] [CrossRef]
- AlOmani, A.; El-Rayes, K. Automated generation of optimal thematic architectural layouts using image processing. Autom. Constr. 2020, 117, 103255. [Google Scholar] [CrossRef]
- Du, T.; Turrin, M.; Jansen, S.; van den Dobbelsteen, A.; Fang, J. Gaps and requirements for automatic generation of space layouts with optimised energy performance. Autom. Constr. 2020, 116, 103132. [Google Scholar] [CrossRef]
- Almashaqbeh, M.; El-Rayes, K. Optimizing the modularization of floor plans in modular construction projects. J. Build. Eng. 2021, 39, 102316. [Google Scholar] [CrossRef]
- Zhang, L.; Zhang, L.; Wang, Y. Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China. Sol. Energy 2016, 132, 38–50. [Google Scholar] [CrossRef]
- Kheiri, F. A review on optimization methods applied in energy-efficient building geometry and envelope design. Renew. Sustain. Energy Rev. 2018, 92, 897–920. [Google Scholar] [CrossRef]
- Ciardiello, A.; Rosso, F.; Dell’Olmo, J.; Ciancio, V.; Ferrero, M.; Salata, F. Multi-objective approach to the optimization of shape and envelope in building energy design. Appl. Energy 2020, 280, 115984. [Google Scholar] [CrossRef]
- Kistelegdi, I.; Horváth, K.R.; Storcz, T.; Ercsey, Z. Building Geometry as a Variable in Energy, Comfort, and Environmental Design Optimization—A Review from the Perspective of Architects. Buildings 2022, 12, 69. [Google Scholar] [CrossRef]
- Hausladen, G.; Saldanha, D.M.; Liedl, P.; Sager, C. Climate Design; Birkhäuser: Berlin, Germany, 14 October 2005; ISBN 9783764372446. [Google Scholar]
- Elshafei, G.; Vilčeková, S.; Zeleňáková, M.; Negm, A.M. An extensive study for a wide utilization of green architecture parameters in built environment based on genetic schemes. Buildings 2021, 11, 507. [Google Scholar] [CrossRef]
- Laignel, G.; Pozin, N.; Geffrier, X.; Delevaux, L.; Brun, F.; Dolla, B. Floor plan generation through a mixed constraint programming-genetic optimization approach. Autom. Constr. 2021, 123, 103491. [Google Scholar] [CrossRef]
- Javanroodi, K.; Nik, V.M.; Mahdavinejad, M. A novel design-based optimization framework for enhancing the energy efficiency of high-rise office buildings in urban areas. Sustain. Cities Soc. 2019, 49, 101597. [Google Scholar] [CrossRef]
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Androsics-Zetz, D.N.; Kistelegdi, I.; Ercsey, Z. Algorithmic Generation of Building Typology for Office Building Design. Buildings 2022, 12, 884. https://doi.org/10.3390/buildings12070884
Androsics-Zetz DN, Kistelegdi I, Ercsey Z. Algorithmic Generation of Building Typology for Office Building Design. Buildings. 2022; 12(7):884. https://doi.org/10.3390/buildings12070884Chicago/Turabian Style
Androsics-Zetz, Dóra Noémi, István Kistelegdi, and Zsolt Ercsey. 2022. "Algorithmic Generation of Building Typology for Office Building Design" Buildings 12, no. 7: 884. https://doi.org/10.3390/buildings12070884