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Architectural design optimization

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Architectural design optimization (ADO) is a subfield of engineering that uses optimization methods to study, aid, and solve architectural design problems, such as optimal floorplan layout design, optimal circulation paths between rooms, sustainability and the like. ADO can be achieved through retrofitting, or it can be incorporated within the initial construction a building. Methods of ADO might include the use of metaheuristic, direct search or model-based optimisation.[1] It could also be a more rudimentary process involving identification of a perceived or existing problem with a buildings design in the concept design phase.[2]

Evolution of digital ADO

The origins of digital based methods of ADO can be attributed to the early days of Computer-Aided Design (CAD), a type of software which enabled architects to create, modify and optimise their drafts freely within a digital environment.[3] Although CAD was invented in the early 1960s, with Ivan Sutherland’s Sketchpad, its applications predominated the aerospace and automotive industries.[4] It was only until the 1970s that it became of novel use to architects, and only in the 90s did it become widespread within the industry.[4] Programs such as AutoCAD, Rhinoceros and Revit have since assisted architects in the creation of more accurate, more extensively optimised designs by relying on computational power to determine efficient variables in areas of daylighting, energy consumption, circulation and the like.[5] This process has been significantly aided by the integration of black box simulations such as genetic algorithms, which greatly increase the efficacy of ADO when used in conjunction with CAD software.[2]

Methods of ADO

Genetic algorithms

Genetic algorithms (GA) are the most popular form of metaheuristic, black box simulation utilised in the fulfilment of complex ADO.[6] GA emulate the process of biological evolution by engaging in a recursive process of selection or deletion based on a criterion of ‘fitness’.[7] Fitness is determined by how effective or ineffective a solution is at solving a given design problem, such as the optimum angle of windows to achieve daylighting, circulation etc.[8] What differentiates GA from more rudimentary, gradient method simulations is its ability to search for a solution from a population of potential solutions.[9] This multi-directional approach accounts for the often-non-linear nature of architectural design problems by allowing for complex variables from multiple different areas to be incorporated into the optimisation process.[10] The randomised, non-linear characteristics of GA mean they are capable offering solutions to design problems which are, at times, more inventive and unconventional than their search-based counterparts.[11] Due to the complexity of GA simulations, they take a comparatively longer time to perform than other methods.[12] This can be a significant implication to projects operating under time constraints.[13][14]

Direct search methods of optimization operate by selecting parameters in a deterministic sequence, from one point to the next successively until a global optimum is achieved.[15] It is not as ubiquitous a method as genetic algorithms in ADO, but research suggests it outperforms metaheuristic simulations such as GA when improvement attained through each evaluation is measured.[16]

Concept design

This method does not rely on computational optimisation, but instead requires the architect to locate areas of optimisation through creative problem solving.[17] This method is limited in its reliance on individual performance and is not likely to yield the most effective optimisation on its own.[18] It could be used in conjunction with optimisation simulations when simulation results are at odds with aesthetic requirements and compromise is necessary.[19]

Applications of ADO

Sustainability

One potential application of ADO is in the reduction of a building’s energy consumption and environmental impact. This might be achieved through the optimisation of the envelope, or façade of a building to ensure ideal thermal properties, which could subsequently reduce the necessity of cooling and heating systems.[20] Other aspects of a buildings form, such as roofing, might be optimised for renewable energy sources.[21] ADO could also assist in the selection of materials that maintain aesthetic and structural qualities, while also being sustainable and of low environmental impact to the surrounding area.[22]

Daylighting

ADO can also be applied to ensure sufficient daylighting within a building. Black box simulations might assist in determining the optimum placement of windows, as well their size, in relation to the building’s situation to maximise daylighting.[23] They can similarly determine a floor plan that maximises daylighting from the building’s exterior, while concurrently minimising the obstruction of light from interior rooms.[24]

Ventilation

ADO can help to promote natural as well as man-made ventilation in a buildings design. This might involve establishing wind properties on a building’s exterior to ascertain the most efficient method of natural ventilation.[25] In areas where natural ventilation cannot be sufficiently optimised, such as in a buildings substructure, ADO can assist in developing an internal ventilation system that efficiently distributes air.[26]

Travel distance/Circulation

ADO could be employed to reduce travel time between internal areas of a building through the optimisation of its floor plan layout.[27] Ideal circulation paths within a building might also be attained through the considered placement of stairwells, elevators, and escalators in relation to frequently used amenities.

Disadvantages of black box simulations

Due to the complex, time-consuming, computationally demanding and at times restrictive nature of black box simulations, there has been some debate over whether these methods are prohibitive in their practical, everyday use to architects.[28] In a survey conducted in 2015, 93% of architects indicated that they would like to better understand the computational principles that underpin optimisation simulations.[29] Other research aimed at addressing this very problem concluded that architects should be educated on the nature of black box simulations and should be able to readily engage with them through an intuitive program that obviates the need for any programming ability.[30]

A majority of architects in the survey also indicated a preference for global multi-objective simulations over local, single objective simulations.[31] Multi-objective simulations, such as those that employ GA, solve this problem, but demand significant computational power and time.[12] Research has been conducted to find a viable, multi-objective alternative to GA that exhausts less resources and will be more accessible to architects.[12][24]

References

  1. ^ Wortmann, Thomas (2019-07-01). "Genetic evolution vs. function approximation: Benchmarking algorithms for architectural design optimization". Journal of Computational Design and Engineering. 6 (3): 414–428. doi:10.1016/j.jcde.2018.09.001. ISSN 2288-5048.
  2. ^ a b Renner, Gábor; Ekárt, Anikó (2003). "Genetic algorithms in computer aided design". Computer-Aided Design. 35 (8): 709–726. doi:10.1016/S0010-4485(03)00003-4.
  3. ^ "The Evolution of CAD for Engineering and Architectural Technicians". Digital School Technical Design College.{{cite web}}: CS1 maint: url-status (link)
  4. ^ a b "CAD software - history of CAD". Cadazz.{{cite web}}: CS1 maint: url-status (link)
  5. ^ Protocols, flows and glitches : proceedings of the 22nd International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2017). Patrick Janssen, Xi'an Jiaotong-Liverpool University. Hong Kong. 2017. ISBN 978-988-19026-8-9. OCLC 1035835606.{{cite book}}: CS1 maint: location missing publisher (link) CS1 maint: others (link)
  6. ^ Wortmann, “Genetic Evolution vs. Function Approximation,” 414.
  7. ^ Renner and Ekárt, “Genetic Algorithms in Computer Aided Design,” 710.
  8. ^ Renner and Ekárt, 710.
  9. ^ Renner and Ekárt, 711.
  10. ^ Renner and Ekárt, 709.
  11. ^ Wortmann, Thomas; Nannicini, Giacomo (2017), Karakitsiou, Athanasia; Migdalas, Athanasios; Rassia, Stamatina Th.; Pardalos, Panos M. (eds.), "Introduction to Architectural Design Optimization", City Networks, vol. 128, Cham: Springer International Publishing, pp. 259–278, doi:10.1007/978-3-319-65338-9_14, ISBN 978-3-319-65336-5, retrieved 2022-05-13
  12. ^ a b c Su, Zhouzhou; Yan, Wei (2015). "A fast genetic algorithm for solving architectural design optimization problems". AI EDAM. 29 (4): 457–469. doi:10.1017/S089006041500044X. ISSN 0890-0604.
  13. ^ Su and Yan, 457.
  14. ^ Thomas Wortmann and Giacomo Nannicini, “Black-Box Optimisation Methods for Architectural Design,” in Proceedings of the 21st International Conference of the Association of Computer-Aided Design Research in Asia (Living Systems and Micro-Utopias: Towards Continuous Designing, Hong Kong: The Association for Computed-Aided Architectural Design Research in Asia, 2016), 179.
  15. ^ Wortmann, “Genetic Evolution vs. Function Approximation,” 415.
  16. ^ Wortmann and Nannicini, “Black-Box Optimisation Methods for Architectural Design,” 179–80.
  17. ^ Renner and Ekárt, “Genetic Algorithms in Computer Aided Design,” 717.
  18. ^ Renner and Ekárt, 717.
  19. ^ Li, Shaoxiong; Liu, Le; Peng, Changhai (2020). "A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges". Sustainability. 12 (4): 1427. doi:10.3390/su12041427. ISSN 2071-1050.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  20. ^ Li, Liu, and Peng, 5.
  21. ^ Li, Liu, and Peng, 10.
  22. ^ Li, Liu, and Peng, 3.
  23. ^ Su and Yan, “A Fast Genetic Algorithm for Solving Architectural Design Optimization Problems,” 464.
  24. ^ a b Shi, Xing; Yang, Wenjie (2013-07-01). "Performance-driven architectural design and optimization technique from a perspective of architects". Automation in Construction. 32: 125–135. doi:10.1016/j.autcon.2013.01.015. ISSN 0926-5805.
  25. ^ Yue, Peng; Liu, Jia Ping; Zhou, Bin; Lu, Yi Xuan; Zhang, Ashley Xin (2012). "Ecological Architectural Technology Optimization Design — Longgang High School's Cafeteria Design as an Example". Advanced Materials Research. 368–373: 3619–3623. doi:10.4028/www.scientific.net/AMR.368-373.3619. ISSN 1662-8985.
  26. ^ Yue et al., 3621.
  27. ^ Su and Yan, “A Fast Genetic Algorithm for Solving Architectural Design Optimization Problems,” 461.
  28. ^ Shi and Yang, “Performance-Driven Architectural Design and Optimization Technique from a Perspective of Architects,” 125.
  29. ^ Judyta Cichocka, Will Browne, and Edgar Rodriguez, “Optimization in the Architectural Practice,” in Proceedings of the 21st International Conference of the Association of Computer-Aided Design Research in Asia (Hong Kong: The Association for Computed-Aided Architectural Design Research in Asia, 2017), 395.
  30. ^ Shi and Yang, “Performance-Driven Architectural Design and Optimization Technique from a Perspective of Architects,” 126.
  31. ^ Cichocka, Browne, and Rodriguez, “Optimization in the Architectural Practice,” 387.
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