摘要

參數式設計可提供建築師自由的參數調整，並藉此運算產生符合不同目的造型，擴充了建築設計的新創意。在國內外有許多應用於建築立面的參數化設計案例，我國亦有許多著重於遮陽板造型的設計案例，而運算參數大多為日射透過率等單一目的參數。近年來，參數化設計亦開始出現在國內外的建築設計教育中。在許多學生設計或學生競圖中，常出現將自然環境的分析作為目的參數的作品。然而，在台灣的大學中，常因師資缺乏、操作時間，以及學生技術能力的不足，造成在設計教育中導入參數化設計的案例仍相當少。
本研究著眼於如何將參數式設計導入大學建築設計的教育方案，並藉由介面整合以及加入多目的設計方法，讓大學生能夠更容易運用於設計方案的操作上。然而當今多目的最佳化的軟體只能記錄模擬結果的數值，無法記錄每個方案的分析圖，造成空間資訊的判讀困難，且缺乏圖像表現性。因此本研究首先在Rhino Grasshopper平台上運用Human UI建立自動生成立面的使用者介面，接著串聯ladybug、honeybee、diva、decoding space等複數性能模擬引擎，並運用Wallacei進行自然採光、建築耗能、視覺穿透性等參數的多目的最佳化設計。藉由UI介面處理最佳化，不但建立明確操作流程，化繁為簡，更能記錄每個方案的模擬分析圖，在圖像後處理上有不錯的表現性。最後，本研究擬於國立成功大學的大學部建築設計課中進行操作，並以問卷調查及個別訪談的方式，驗證多目的遺傳演算法結合模擬軟體的技術應用於建築設計教育的成效。
調查結果發現，雖然多目的最佳化的使用技術操作門檻較高，但問卷調查仍顯示受試者操作上並無太大困難。可知多目的最佳化技術導入教育訓練並不會造成受試者太大的負擔與挫折。但即使如此，多目的最佳化的使用仍面臨諸多限制，本研究原先預期多目的最佳化的操作方式將取代過去傳統操作方式，但經個別訪談後了解，發現與預期有所落差。因此，以現階段而言，多目的最佳化僅是一種操作性能模擬軟體的其中一種方式，而無法全然取代舊有的性能模擬手動方式。歸咎其原因可被分為幾個面向探討，將於本研究結論仔細說明。

關鍵字：介面整合、遺傳演算法、多目的最佳化、建築設計教育、參數式設計

SUMMARY

Parametric design can provide architects with free parameter adjustment. Through these calculations, architects generate shapes that meet different purposes, which expands the new ideas of architectural design. There are many parametric design cases applied to building facades abroad. Many design cases in my country focus on the shape of sun visors, and most of the calculation parameters are single-purpose parameters such as solar transmittance. In recent years, parametric design has also begun to appear in architectural design education at home and abroad. In many student designs or student competitions, there are often works that take the analysis of the natural environment as the objective parameter. However, in universities in Taiwan, there are still very few cases of introducing parametric design into design education due to lack of teachers, operating time, and insufficient technical ability of students.
This research focuses on how to introduce parametric design into the educational program of university architectural design, and through interface integration and the addition of multi-purpose design methods, it is easier for university students to apply the design program to the operation. However, today's multi-purpose optimization software can only record the numerical value of the simulation results, but not record the analysis diagram of each scheme, which causes difficulty in the interpretation of spatial information and lack of image expressiveness. Therefore, this research first uses Human UI on the Rhino Grasshopper platform to create a user interface and then connects multiple performance simulation engines such as Ladybug, Honeybee, Diva, and Decoding Spaces for multi-purpose optimization design.
UI interface not only establishes a clear operation process to perform optimization but also simplifies the complexity and records the simulation analysis diagram of each case, which makes a good performance in image post-processing. Finally, this research will be conducted in the architectural design course of the National Cheng Kung University and verify the effectiveness of multi-purpose optimization in architectural design education through questionnaire surveys and individual interviews.

Key words：User interface integration, Genetic algorithm, Multi-objective optimization, Architectural design education, Parametric Design

(一) 中文文獻
1. 陳柏睿（2019），建築性能多目的最佳化之參數式建築設計教育方案
2. 林權萱（2018），以建築師事務所觀點探究建築性能模擬軟體在設計實務中之採用因素
3. 國家教育研究院，學術名詞暨辭書資訊網，http：//terms.naer.edu.tw/
4. 內政部建築研究所（2020），綠建築評估手冊–基本型

(二) 英文文獻

多目的最佳化

1. Mahmoud Gadelhak (2019). A Computational Framework for the Optimization of the Environmental Performance of Facades in Early Design Stages
2. Vincent J.L. Gan, Irene M.C. Lo, Jun Ma, K.T. Tse, Jack C.P. Cheng, C.M. Chan (2020). Simulation optimisation towards energy efficient green buildings: Current status and future trends
3. Farshad Kheiri (2018). A review on optimization methods applied in energy-efficient building geometry and envelope design.
4. H Sghiouri, M Charai, A Mezrhab (2020). Optimisation in Building Performance Simulation and Obstacles Facing Its Widespread Use
5. Joshua Knowles, Hirotaka Nakayama (2008). Meta-modeling in multiobjective optimization
6. Touloupaki E., Theodosiou T. (2017). Performance simulation integrated in parametric 3D modeling as a method for early stage design optimization—A review
7. Wortmann T, Nannicini G (2017). Introduction to Architectural Design Optimization. In: Karakitsiou A, Migdalas A, Pardalos PM, Rassia S (eds) City Networks - Planning for Health and Sustainability. Springer International Publishing, Cham, CH pp 259–278
8. Wortmann T, Nannicini G (2016). Black-box optimization for architectural design: An overview and quantitative comparison of metaheuristic, direct search, and model-based optimization methods. In: Chien S-F, Choo S, Schnabel MA, et al. (eds) Proceedings of the 21th CAADRIA Conference. CAADRIA, Hong Kong, CN pp 177–186
9. Miles J (2010). Genetic Algorithms for Design. In: Waszczyszyn Z (ed) Advances of Soft Computing in Engineering. Springer, Vienna pp 1–56
10. T Wortmann (2018). Efficient, visual, and interactive architectural design optimization with model-based methods
11. Binghui Si, Jianguo Wang, Xinyue Yao, Xing Shi, Xing Jin, Xin Zhou (2019). Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms
12. Deb K, Pratap A, Agarwal S, Meyarivan T (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE T Evolut Comput 6(2):182–197.
13. Voutchkov I., A. J. Keane. (2006). Multi-Objective Optimization Using Surrogates.
14. P Pilechiha, M Mahdavinejad, FP Rahimian (2020). Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency
15. Mahmoud Islam Abdelhay Gadelhak (2019). A Computational Framework for the Optimization of the Environmental Performance of Facades in Early Design Stages

BPST教育訓練

1. Delbin S., Vanessa G.D. Silva., Doris C.C. K.Kowaltowski., Lucila C. Labaki. (2006). Implementing building energy simulation into the design process： A teaching experience in Brazil. PLEA2006 - The 23rd Conference on Passive and Low Energy Architecture, Geneva, Switzerland, 6-8 September.
2. Guzowski M. (2013). Time and Adaptive Comfort Studies： Luminous and Thermal Design for Zero-Energy Architectural Education. International Conference on Adaptation and Movement in Architecture, Toronto, Canada, 10‐12 October.
3. Göçer Ö., Sokol D. (2015). The Use of Building Performance Simulation Tools in Undergraduate Program Course Training. Proceedings of BS2015： 14th Conference of International Building Performance Simulation Association, Hyderabad, India, Dec. 7-9.
4. He S., Passe U. (2015). Architectural Student's Attitude towards Building Energy Modeling: A Pilot Study to Improve Integrated Design Education
5. Bernier M., Michaël K., Simon S., Denis B., Didier T. (2016). Teaching a Building Simulation Course at the Graduate Level.
6. MORRISON I.B., HOPFE C.J. (2015). Teaching Building Performance Simulation through a continuous learning cycle. 14th International Conference of the International Building Performance Simulation Association, Hyderabad, India, Dec. 7-9th.
7. Ian Beausoleil-Morrison (2019). Learning the fundamentals of building performance simulation through an experiential teaching approach
8. Kamal Eldin Mohamed, Soofia Tahira Elias-Ozkan (2019). Incorporating sustainability principles into architectural design education: Results of an experimental design studio
9. Tracey J.B., Hinkin T.R., Tannenbaum S., Mathieu J.E. (2001). The Influence of Individual Characteristics and the Work Environment on Varying Levels of Training Outcomes. Cornell University.
10. Alainati S., Sarmad N.A., Wafi A.K. (2009). European and Mediterranean Conference on Information Systems 2010, April 12-13 2009, Abu Dhabi, UAE.

BPST

1. AIA . (1995). Document D200：Project Checklist.
2. AIA. (2012). AIA-Energy-Guide.
3. Mahdavi, A. (2012). People in building performance simulation. In Building Performance Simulating for Design and Operation
4. Baba A., Mahjoubi L., Olomolaiye P., Booth C. (2013). Architects Requirements of Decision Support Tools to deliver Low Impact Housing Design in the UK: Insights and Recommendations
5. Maria-Mar Fernandez-Antolin, José-Manuel del-Río, Fernando del Ama Gonzalo, Roberto-Alonso Gonzalez-Lezcano (2020). The Relationship between the Use of Building Performance Simulation Tools by Recent Graduate Architects and the Deficiencies in Architectural Education.
6. Mostapha S.R., Michelle P. (2013). LADYBUG： A Parametric Environmental Plugin for Grasshopper to Help Designers Create an Environmentally-conscious Design. Proceedings of BS2013：13th Conference of International Building Performance Simulation Association, Chambéry, France, Aug. 26-28.
7. Attia S. (2012). Computational optimisation for Zero Energy Building Design Interview with Twenty Eight International Expats. International Energy Agency (IEA) Task 40： Net zero Energy Buildings Subtask B.
8. Attia S., De Herde A. (2010). Early Design Simulation Tools for Net Zero Energy Buildings： A Comparison of Ten Tools. IBPSA, Vol. 1, no. 1.
9. Attia S., Gratia E., De Herde A., Hensen J.L.M. (2013). Achieving informed decision-making for net zero energy buildings design using building performance simulation tools, Building Simulation, Vol 6-1, P 3-21.
10. Attia S., Jan L.M. Hensenb., Liliana B., De Herde A. (2012). Selection criteria for building performance simulation tools： contrasting architects’ and engineers’ needs. Journal of Building Performance Simulation. Vol. 5, No. 3, May 2012, 155–169.
11. Mojtaba Parsaee, Claude M.H. Demers, Jean-François Lalonde, André Potvin, Mehlika Inanici, Marc Hébert (2020). Human-centric lighting performance of shading panels in architecture: Abenchmarking study with lab scale physical models under real skies
12. Ramsden J., Keeling T., Shepherd P., Shea A., Sharma S. (2015). 'SmartBuildingAnalyser： A parametric earlystage analysis tool for multi-objective building design'. CIBSE Technical Symposium, London, UK United Kingdom, 16/04/15-17/04/15.
13. JLM Hensen, R Lamberts (2012). Building Performance Simulation for Design and Operation
14. Tanja Siems, Katharina Simon, Karsten Voss (2018). State-of-the-Art of Education on Solar Energy in Urban Planning Part II: Solar Irradiation Potential Tools in Education
15. Leather, Phil, Pyrgas, Mike, Di Beale, Lawrence, Claire (1998). Windows in the workplace: sunlight, view, and occupational stress. Environ. Behav. 30 (6), 739–762.
16. Aries, Myriam B.C., Veitch, Jennifer A., Newsham, Guy R. (2010). Windows, view, and office characteristics predict physical and psychological discomfort. J. Environ. Psychol. 30 (4), 533–541.
17. Damigos, Dimitris, Anyfantis, Fotis (2011). The value of view through the eyes of real estate experts: a fuzzy delphi approach. Landsc. Urban Plann. 101 (2), 171–178.
18. Kanters J., Horvat M., Dubois M.C. (2014). Tools and methods used by architects for solar design. Energy and Buildings 68 (2014) 721-731.

Sustainability

1. Vehmaa A., Karvinen M., Keskinen M. (2018). Building a More Sustainable Society? A Case Study on the Role of Sustainable Development in the Education and Early Career of Water and Environmental Engineers. Sustainability