COMPUTATIONAL ANALYSIS FOR DESIGN DEVELOPMENT EVALUATION IN SPATIAL PLANNING

Abstract

The influence of new technological software on architectural design is increasing with every passing day. This led to new horizons discovery in spatial analysis and design interpretation and extended by engaging different techniques based on computational design and human-computer interaction. Throughout the architectural design process, decision-making on spatial performance parameters such as visibility, density, and building typology is frequently taken by examining a limited number of materials. They are conventionally optimized by employing repetitive experimentations without systematically evaluating the complete range of potential designs and their efficient outcomes. A computational design analysis approach of spatial morphological structure based on several indicators is presented in response to this challenge. This research compares contextual spatial analysis with computational methods and determines the consistency of Eskisehir technical university master plan expansion mechanisms through the relationship between layout and spatial arrangement, connectivity and accessibility, and built area and open space of the university map in two different periods (2005/2020). For density measurements, Ground Space Index (GSI), Floor Area Ratio (FAR), and Open Space Ratio (OSR) calculations in urban spatial planning are analyzed. Furthermore, the Isovist analysis (Attractiveness, Extent of observation, line of orientation, and arrangement) and their visual quality was examined using the logical interpretation approach. The collected visual and numerical data show that the visual quality of the observer's full view, as seen from the center of the university campus master plan, is directly related to the open space and built environment. The visibility and density characteristics of the university campus master plan showed that these analytical techniques are very responsive to the design limitation and context requirements. The presented application has evaluated the visual aspects of each of the university campus maps to deliver a technique to the designers so that they may implement their requested visual characteristics in future design expansion.

Keywords

• Computational Analysis
• Spatial Planning
• Architectural Design
• Development Mechanisms
• Campus Master Plan

MEKANSAL PLANLAMADA TASARIM GELİŞİMİ DEĞERLENDİRMESİ İÇİN HESAPLAMALI ANALİZ

Abstract

Yeni teknolojik yazılımların mimari tasarım üzerindeki etkisi her geçen gün artmaktadır. Bu durum, mekansal analiz ve tasarım yorumunda yeni ufuklar keşfetmeye yol açmakta ve hesaplamalı tasarım ve insan-bilgisayar etkileşimine dayalı farklı tekniklerin kullanılmasıyla genişlemektedir. Mimari tasarım süreci boyunca görünürlük (visibility), yoğunluk (density) ve bina tipolojisi (building typology) gibi mekânsal performans parametrelerinin kararlaştırılması, sıklıkla sınırlı sayıda malzeme incelenerek alınmaktadır. Bu parametreler potansiyel tasarımların tamamını ve bunların verimli sonuçlarını sistematik olarak değerlendirmeden tekrarlayan deneyler kullanılarak geleneksel olarak optimize edilmektedirler. Mekansal morfolojik yapının çeşitli göstergelere dayalı bir hesaplamalı tasarım analizi yaklaşımı, bu zorluğa yanıt olarak sunulmaktadır, Bu araştırma, bağlamsal mekansal analizi hesaplama yöntemleriyle karşılaştırmakta ve iki farklı dönemde (2005 /2020) üniversite haritasının yerleşim ve mekansal düzenleme, bağlanabilirlik, erişilebilirlik ve yapılı alan ile açık alan arasındaki ilişki aracılığıyla Eskişehir teknik üniversitesi master planı genişleme mekanizmalarının tutarlılığını belirlemektedir. Yoğunluk ölçümleri için, kentsel mekansal planlamada ‘’Ground Space Index’’ (GSI), ‘’ Floor Area Ratio’’ (FAR) ve ‘’Open Space Ratio’’(OSR) hesaplamaları analiz edilmektedir. Ayrıca, İsovist analiz (Attractiveness, Extent of observation, line of orientation, and arrangement) ve görsel kaliteleri mantıksal yorumlama yaklaşımı kullanılarak incelenmiştir. Mevcut araştırmalarla karşılaştırıldığında, bulgular mimari süreçleri tanımada gelişmiş hesaplama doğruluğunu ortaya çıkarmaktadır. Toplanan görsel ve sayısal veriler, üniversite kampüs master planının merkezinden görüldüğü gibi, gözlemcinin tam görüşünün görsel kalitesinin açık alan ve yapılı çevre ile doğrudan ilişkili olduğunu göstermektedir. Üniversite kampüs master planının görünürlük ve yoğunluk özellikleri, bu analitik tekniklerin tasarım sınırlamalarına ve bağlam gereksinimlerine çok duyarlı olduğunu göstermiştir. Sunulan uygulama, tasarımcılara gelecekteki tasarım genişletmelerinde istenen görsel özellikleri uygulayabilmeleri amacıyla bir teknik sunmak için üniversite kampüs haritalarının her birinin görsel yönlerini değerlendirmiştir.

Keywords

• Hesaplamalı Analiz
• Mekansal Planlama
• Mimari Tasarım
• Gelişme Mekanizmaları
• Kampüs Master Planı

References

1. [1] He Y, Dai L & Zhang H. Multi-branch deep residual learning for clustering and beamforming in user-centric network. IEEE Communications Letters, 2020; 24(10), 2221–2225.
2. [2] Huang Z, Wang T, Liu W, Valencia-Cabrera L, Pe´rez-Jime´nez MJ & Li P. A fault analysis method for three-phase induction motors based on spiking neural p systems. Complexity, 2021, 1–19.
3. [3] Manogaran G, Shakeel PM, Fouad H, Nam Y, Baskar S, Chilamkurti N & Sundarasekar R. Wearable IoT smart-log patch: An edge computing-based Bayesian deep learning network system for multi-access physical monitoring system. Sensors, 2019; 19(13), 3030.
4. [4] Preeth SSL, Dhanalakshmi R, Kumar R & Shakeel PM. An adaptive fuzzy rule-based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. Journal of Ambient Intelligence and Humanized Computing, 2018; 1-13.
5. [5] Tiboni M, Rossetti S, Vetturi D, Torrisi V, Botticini F & Schaefer MD. Urban Policies and Planning Approaches for a Safer and Climate Friendlier Mobility in Cities: Strategies, Initiatives and Some Analysis. Sustainability, 2021; 13(4), 1778.
6. [6] Moraci F, Errigo MF, Fazia C, Campisi T & Castelli F. Cities under pressure: Strategies and tools to face climate change and pandemic. Sustainability, 2020; 12(18), 7743.
7. [7] Campisi T, Basbas S, Skoufas A, Akgün N., Ticali D & Tesoriere G. The Impact of COVID-19 Pandemic on the Resilience of Sustainable Mobility in Sicily. Sustainability, 2020; 12(21), 8829.
8. [8] Herman K & Rodgers M. From tactical urbanism action to institutionalized urban planning and educational tool: the evolution of Park(ing) Day. land, 2020; 9(217).

9. [9] Feng W. Virtual display system of geological body based on key algorithms of three-dimensional modelling. Earth Sciences Research Journal, 2019; 23, 185–189.
10. [10] Kuller M, Bach PM, Roberts S, Browne D & Deletic A. A planning-support tool for spatial suitability assessment of green urban stormwater infrastructure. Science of the total environment, 2019; 686, 856-868.
11. [11] Berghauser-Pont MY & Haupt P. Spacematrix: space, density and urban form. NAi Publishers, 2010;177-196.
12. [12] Davis LS & Benedikt ML. Computational models of space: Isovists and isovist fields. Computer graphics and image processing, 1979; 11(1), 49-72.
13. [13] Estu. Sustainable Eco-Campus Development Plan, Eskisehir Technical University (ESTU). https://www.eskisehir.edu.tr/uploads/anadolu/ckfinder/web/files/ecocampus_15_10_19.pdf.2019; (Accessed: 25/03/2022)
14. [14] Mueller J, Lu H, Chirkin A, Klein B & Schmitt G. Citizen Design Science: A strategy for crowd-creative urban design. Cities, 2018; 72, 181-188.
15. [15] Van der Voordt TJM, Vrielink D and Van Wegen H. Comparative floorplan-analysis in programming and architectural design, Design Studies, 1997; 18 (1), 67-88.
16. [16] Stahle A. Compact sprawl: Exploring public open space and contradictions in urban density. Doctoral dissertation, KTH, 2008; 125-143.
17. [17] Berghauser Pont M & Haupt P. The Space mate: Density and the typo morphology of the urban fabric. Urbanism laboratory for cities and regions: progress of research issues in urbanism. IOS Press, 2007; 11-26.
18. [18] Hillier B and Hanson J. The social logic of space (Cambridge, Cambridge University press), 1984.
19. [19] Esfandiari A and Tarkashvand A. Application of Isovist analysis and sightlines in measuring visual quality of residential complexes. Quarterly Journal of Motaleate Shahri, 2020; 9 (35), 19-32.
20. [20] Benedikt ML & Mcelhinney S. Isovists and the metrics of architectural space. Proceedings 107th ACSA Annual Meeting; Ficca J, Kulper A, Eds, 2019; 1-10.
21. [21] Xiang L, Papastefanou G & Ng E. Isovist indicators as a means to relieve pedestrian psycho-physiological stress in Hong Kong. Environment and Planning B: Urban Analytics and City Science, 2021; 48(4), 964-978.
22. [22] Huang KS. Vision & Mechanism of Campus Planning: An Urban Design Prototype. J.Architect. 2007; 59, 189–202.
23. [23] Li Y, Gu YF, Liu C. Prioritizing performance indicators for sustainable construction and development of university campuses using an integrated assessment approach. Journal of cleaner production, (202), 2018; 959–968.
24. [24] Koenig R. CPlan: An Open Source Library for Computational Analysis and Synthesis: An Open Source Library for Computational Analysis and Synthesis. eCAADe real time-extending the reach of computation. Proceedings of the 33rd International Conference on Education and Research in Computer Aided Architectural Design in Europe, 2015; (1), 245-250.
25. [25] Henriques R, Bação F, Lobo V. GeoSOM Suite: A Tool for Spatial Clustering, Computational Science and Its Applications. ICCSA, 2009; 257.
26. [26] Hajrasouliha A. Campus score: Measuring university campus qualities. Landscape and Urban Planning, 2017; 158, 166–176.