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Abstract

Designing architectural and urban spaces based on continuous human spatial experience throughout a sequence may lead to architectural and urban forms not bound to conventional concepts. These forms would be made possible through the combination of a quantitative sequential landscape evaluation and morphological optimization utilizing algorithmic processes. In this study, we introduce a method for evaluating sequential landscapes, in which the configuration of buildings, trees, etc., in view changes as pedestrians move along a particular path, using a genetic algorithm optimization of architectural forms to bring them closer to the designer’s ideals. The method’s validity was tested using two case studies, assuming the design of a sequential landscape in an actual city and park. The results present a more efficient, objective, and reproducible method for designing sequences in buildings, cities, and landscapes, compared to traditional methods.

Keywords

Sequencing landscape evaluation algorithmic design optimization architectural design design methods

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References

Lynch

. The image of the city. Cambridge, MA: The MIT Press, 1959.

Thiel

. A sequence-experience notation: for architectural and urban spaces. Town Plan Rev 1961; 32(1): 33–52.

Miyauji

. A study on visual sequence with human movement (Part 1): an analysis of sequential characteristics and verification of the sequential notation. J Archit Plan (Transactions of AIJ) 1992; 440: 101–109.

Miyauji

. A study on visual sequence with human movement (Part 2): an analysis of sequential characteristics and verification of the sequential notation. J Archit Plan (Transactions of AIJ) 1994; 455: 97–108.

Tadayoshi

. Kankō dōro no kenkyū, Beppu Aso dōro no riyō jittai to dōro keikan kōgaku-teki kenkyū [Studies of tourist roads, usage of roads in beppu aso and studies of road landscape engineering]. Tokyo: Japan Travel and Tourism Association, 1996. [in Japanese].

Fei

Liu

Guo

. Visual integration relationship between buildings and the natural environment based on eye movement. Buildings 2022; 12(7): 930.

Lee

Kim

Park

. A machine learning and computer vision study of the environmental characteristics of streetscapes that affect pedestrian satisfaction. Sustainability 2022; 14(9): 5730.

Benedikt

. To take hold of space: isovists and isovist fields. Environ Plann Plann Des 1979; 6(1): 47–65. DOI: 10.1068/b060047.

Batty

. Exploring isovist fields: space and shape in architectural and urban morphology. Environ Plan B Urban Anal City Sci 2001; 28(1).

10.

Dalton

. (2001). An application for isovist field and path analysis. In: (Proceedings) 3rd International Space Syntax Symposium, Atlanta, Georgia, US, 2001.

11.

Yasufuku

Deki

Abe

. Development and application of visual space analysis tool along moving paths using walk-through system. J Archit Plan (Transactions of AIJ) 2013; 78(684): 365–372. DOI: 10.3130/aija.78.365.

12.

Betti

Aziz

Ron

. HENN workplace analytics. In: Gengnagel

Baverel

Burry

, et al. (eds) Impact: Design with all senses: Proceedings of the Design Modelling Symposium. Berlin: Springer International Publishing, 2020, pp. 636–647. DOI: 10.1007/978-3-030-29829-6_49.

13.

Schneider

Koenig

. Exploring the generative potential of isovist fields—the evolutionary generation of urban layouts based on isovist field properties. Brussels: eCAADe, 2012. DOI: 10.13140/RG.2.1.2569.9042.

14.

Hatada

. Artificial reality and visual space perception. Jpn J Ergon 1993; 29(3): 129–134. DOI: 10.5100/jje.29.129.

15.

Deb

Agrawal

Pratap

, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: nsga-II. In: International conference on parallel problem solving from nature, Paris, France, 2000, pp. 849–858.

16.

Mitchell

. An introduction to genetic algorithms. Cambridge, London: MIT Press, 1998.

17.

Guan

Keith

Hong

. Designing walkable cities and neighborhoods in the era of urban big data. Urban Plan Int 2019; 34: 9–15. DOI: 10.22217/upi.2019.389.

18.

Kasai

Okaze

Yamamoto

, et al. Summer heatstroke risk prediction for Tokyo in the 2030s based on mesoscale simulations by WRF. J Heat Island Inst Int 2017; 12(2): 61–67. https://heat-island.jp/web_journal/JGM8SpecialIssue/P-8_kasai.pdf

19.

Tuholske

Caylor

Funk

, et al. Global urban population exposure to extreme heat. Proc Natl Acad Sci USA 2021; 118(41): e2024792118. DOI: 10.1073/pnas.2024792118.

20.

Ervin

. Turing landscape: computational and algorithmic design approaches and futures in landscape architecture. In: Cantrell

Mekies

(eds) Codify: Parametric and computational design in landscape architecture. London: Routledge, 2018, pp. 89–115.

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Algorithm optimization of architectural form through sequential landscape evaluation

Abstract

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