Al-Azhar University Engineering Journal, JAUES

Vol. 9, No. 6, Dec. 2014 1

1.

عشرلثالثامؤتمر األزھر الھندسي الدولي

AL-AZHAR ENGINEERING

THIRTEENTH INTERNATIONAL CONFERENCE

December 23-25, 2014

Code: A 30

USING PARAMETRIC OPTIMIZATION TOOLS IN

ARCHITECTURAL DESIGN PROCESS

Randa Medhat H. Khalil 1 and

Sahar Abdalwahab Mohammad

2

1 Future University in Egypt, Cairo, Egypt

2 AL-Azhar University, Cairo, Egypt

الملخصتستخدم أدوات التصميم التوليدية في اكتشاف تصميمات متعددة ومختلفة وبالتالي الوصول إلي التكوين المعماري

ة انتقاء الحل األمثل ويمكن الحصول علي أفضل حل ممكن من تلك التصميمات المتعددة من خالل استخدام أدا. المناسبالبارامترية األوتوماتيكية وربطها بأدوات التصميم التوليدية، وتلك العلمية قيمة للغاية بسبب إمكانية الحصول علي أكثر

حيث يناقش هذا ). اليدوية(الحلول التصميمية فعالية بسهولة مقارنة بتقييم تلك التصميمات المتعددة بالطرق التقليدية نتقاء الحل األمثل البارامترية وإمكانية ارتباطها بأدوات التصميم التوليدية في مجال الهندسة المعمارية بهدف البحث أداة ا

أدوات : و يشرح البحث التقنيات واألدوات المختلفة المستخدمة في تلك العملية مثل. الوصول إلي االداء األمثل للمبانيRhinoceros و Grasshopperاألمثل وأداة انتقاء الحل optimizationبهدف تحقيق المنهجية التصميمية ، .

باإلضافة إلي ذلك سيتطرق البحث لعرض بعض االمثلة المعمارية و الطريقة المتبعة في كل مثال لتحقيق افضل أداء ية إلمكانية كما سيقدم البحث دارسة تجريب. مطلوب من خالل استخدام عملية انتقاء الحل األمثل البارمترية األوتوماتيكية

دمج أداة انتقاء الحل األمثل البارامترية األوتوماتيكية مع أدوات التصميم التوليدية للوصول إلي الحل األمثل من خالل ويهدف البحث إلي توفير التعريف بأداة انتقاء الحل األمثل البارامترية للمصممين المعماريين . تصميم واجهة افتراضيةتلك األداة أثناء عملية التصميم، كما يقدم البحث طريقة بسيطة لتقييم وانتقاء الحل األمثل ألداء وبالتالي يمكن استخدام

.الواجهات باستخدام تلك االدوات© 2014 Faculty of Engineering, Al-Azhar University, Cairo, Egypt. All rights reserved.

ABSTRACT: Generative design tools have been used to explore design variations and to generate form. In

order to adjust different variables to get the best possible solution; parametric optimization

process links to these generative tools; this automatic optimization process is a very precise

method that finds the most efficient solution instead of evaluating the results manually. This

paper is going to explore parametric optimization process and its possibility of being

integrated with generative tools in the design process, in order to reach the optimum

building’s performance. The research will explain different techniques and tools that used in

this process; Rhinoceros, grasshopper and parametric optimization in order to investigate this

design methodology. In addition, the research is going to track some architectural examples

that experience optimization process in order to reach the optimum performance needed and

to explain the workflow of each process, also the research will present a pilot study of

integrating genetic optimization tools during a typical design process for building facade.

Research goal is to make parametric optimization tools a well-known tool for architects to be

used during the design process, and to offer a simple method of evaluating and optimizing

faced design and its performance by using these tools.

Keywords: Architectural Design Process, Parametric, Optimization, Grasshopper.

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1. INTRODUCTION

Parametric modeling is now well established within the computational design community.

Software applications such as Grasshopper, Generative Components (GC) and more recently

Design Script by Autodesk allow complex ideas to be explored at the early stage of design

that go beyond what is possible using the traditional methods of hand sketching and model

making alone. In addition to the generic software platforms, in recent years many third-party

analysis plug-ins have also been developed that provide real-time performance feedback to

assist at the early stage of design. (Shea, 2003)

2. TRADITIONAL METHOD

In usual traditional design, the role of the designer is to explore a solution space. The key

relationship between designer and product is a direct one. There is a direct relationship

between the designer’s intentions and that of the designed product (Figure 1). (El Iraqi, 2008)

Figure 1 Left; tradition design approach. Right; generative design approach.

Source; (El Iraqi, 2008, P 194)

3. COMPUTATIONAL DESIGN METHOD Computational systems have emerged as a fundamental keystone in architectural design during the past decades, marking the rise of a new area of study that engages with design cognition, computation and Obstetric principles in contemporary design practice (Gero, 1994). It enlists two main areas in the development of computer aided design: “the representation and production of the geometry and topology of designed objects” and “the representation and use of knowledge to support or carry the collections of designs”. The first area relates to the general use off-the-shelf CAD tools that aim to increase the efficiency or to automate drafting and design activities; the second has given birth to novel generative approaches that regard computation as an aid to the design process and to explore design ideas. Computational systems can systematically explore sets of possible architectures and are not fixed, but increases the likelihood that they will find architectures giving good performance (Wyatt, 2009). Due to this; the design process is converted from a one-direct liner of manual try and error method to a non-direct computational operation (Figure 2) that generates unlimited alternatives to be evaluated numerically. A combination of parametric modeling, performance analysis tools and heuristics allow for a variety of design options to be explored both quantitatively and qualitatively by adjusting numeric parameters. As the most impactful decisions in the design process are made at the start of any project, tools which assist good decision making at this early stage are of great help to the design team (Harding, 2013). Parametric design is one of computational methods that can act as both a generative and analytical method during design process, and has recently gained great acceptance from theoretical and practical architectural fields (Dino, 2012).

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Figure 2 Form generation operation.

Source ;( Oxman, 2010)

3.1. PARAMETRIC DESIGN TOOLS Grasshopper (GH) is a data-flow programming plug-in for the CAD-software Rhinoceros (McNeel) written by David Rutten. It gives user the ability of creating directed acyclic graph out of pre-defined or custom made components. This is done by pulling wires from the output of one component into the input of another component in a visual way, allowing for a very intuitive approach to scripting procedures. A feature which sets the Grasshopper approach to programming apart from conventional scripting (e.g., Rhinoscript) is the constant real time visualization. Changes to referenced geometry or to the history tree itself are reflected in a preview window with no delay, and it enables the user in turn to react to those changes, with no need to reset or restart the script (Braumann, 2010). 3.2. Parametric optimization Parametric systems are principally based on algorithmic principles. An algorithm is a finite set of instructions that aim to fulfil a clearly defined purpose in a finite number of steps. An algorithm takes one value or a set of values as input, executes a series of computational steps that transform the input, and finally produces one value or a set of values as output. The power of algorithms lay in the ability to solve a wide range of computational problems including but not limited to sorting and searching, data structure operations, combinatorial problems, numerical problems (including random number generation), and computational geometry (Cormen, 2001). Optimization tools used to extensively search the design alternatives looking for high performance solutions in terms of specified goals. In order to combine parametric modeling with an optimization technique to support design explorations and form finding, Genetic algorithms (GAs) have been considered. GAs can perform a series of simulations in a multi-dimensional search space, increasing the relevance of the cases simulated. They are used to find the configuration that best matches desired performance goals (Rakha, 2011). 4. COMBINING PARAMETRIC MODELLING WITH OPTIMIZATION TOOLS Adjusting the parameters in any parametric model enables exploration of different design options, each of this options can be evaluated by quantitative and qualitative criteria. When used in combination with a multi-objective optimization algorithm (Deb, 2001), multiple designs can be generated and evaluated automatically within the set parameter constraints, with high scoring designs stored and identified. Current parametric modeling tools are beginning to include generic solvers as standard allowing bi-directional graph associations (Rutten, 2010), (Coenders, 2011), hence their importance for architectural design problems is already growing (Evins, 2012), and is likely to increase in the future. Initializing such a process assumes that the performance metrics can be expressed quantitatively, however the important addition of subjective judgments during the search is often required in order to include qualitative aspects such as social impact, aesthetics, etc...

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5. CASE STUDIES 5.1. Nate Holland residential tower This tower is located in downtown Seattle, which the upper floors are residential and the middle floors are office spaces, the ground floor contains retails and leisure facilities (Figure 3). In order to reach maximum profit for this project; there are some requirements: Ground floor requirements: 1- To increase access. 2- Proximity of public transportation. 3- Proximity to existed retails. 4- Access to sunny outdoor space. 5- Proximity of existing public plaza. Typical floor requirements: 1- To maximize the length of the perimeter that has a direct line of sight to the waterfront.

Figure 3 Tower’s location and constrains.

Source; (Holland, 2011)

Optimization process The designers addressed computer as one of design team work, in order to make the tower not only a visual addition to the skyline; but also to perform. By using grasshopper software, a computer optimization process was run to reach the best performance which maximizes the profits. The primary communication between the designer and grasshopper is a 3d digital model of the immediate site and its conditions, giving the system knowledge about heights, views, proportions and zoning restrictions (Holland, 2011). Galapagos, the evolutionary GA solver within grasshopper, optimizes through an evolutionary process which creates and tests one generation of possibilities. The optimizing process was run twice; firstly to adjust ground floor profile according to site distractions, secondly to adjust upper floors profile to reach maximum length of the perimeter that has a direct line of sight to the waterfront which also takes into account zoning requirements. Each time Galapagos runs it becomes an opportunity for the designer to work with the computer in determining the best solutions.

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Figure 4 Alternatives that are generated.

Source ;( Holland, 2011)

After optimizing the lower and the upper floor plate profiles, Grasshopper creates the massing

of each building by blending the two profiles together. Each square from the base floor plate

is lofted, multi-lofted and extruded with its twin on the upper level (Figure 5) and then joined

together with the other pairs to create the generic form of the building (Holland, 2011).

Figure 5 Building’s masses.

Source; (Holland, 2011)

5.2. The Tulip gymnastics hall This project is a performance hall formed by sweeping a double curve wall profile over a rectangular perimeter, and the transparent part is surrounding the flat roof, which allows the natural light entering the space indirectly, because it uses the perimeter walls as reflectors (Figure 6); the concept employee this wall-cross section shape in order to enhance daylight qualities in the inner space, however, after evaluating the daylight of the current design solution, it was found that the supposed cross section causes glow and very high luminance ratio. The simulation results show the minimum illuminations value equal to 97 Lux and the maximum illuminations value equal to 46283Lux. These values are extreme in comparison with the recommend daylight illuminations values by IESNA (IESNA, 2000) which recommends for sports hall daylight illumination equal to 500Lux as minimum and 2000Lux as a maximum (Wagdy, 2013).

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Figure 6 reflector walls of Tulip hall.

Source; (http://inhabitat.com/tulip-like-gymnastics-hall-in-utretcht-provides-privacy-but-maintains-

natural-daylighting/tulip-building-nl-architects-14/)

Wagdy, A. in his research suggests an optimization process to improve these results in

conjunction with the perimeter outline parameters (Figure 7). The new wall shape which

generated based on the daylighting simulation distributes the daylight much better than the

original design and prevents the direct sunlight from entering the space. The new simulation

result shows the minimum illuminations value equal to 448 Lux and the maximum

illuminations value equal to 2784Lux. The luminance ration equal to 1:6 which is considered

better than standard luminance ration which is equal to 1:10 (IESNA, 2000), (Wagdy, 2013).

Figure 7 the optimization process.

Source; (Wagdy, 2013)

6. PILOT STUDY The previous case studies show that the optimization process was used for many design aspects; functional aspect (example 1) and environmental aspect (example 2). The proposed pilot study in this research will expert the ability of integrating optimization tools within the typical design process for one of environmental design aspects (Daylighting), daylighting

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directly affects the performance of many types of building activities (educational, hospital, office, performance..etc. ). The aim of this study is to offer a simple method that could be embedded within the typical design process of building façade, in order to evaluate and optimize façade daylighting performance during the design process. The proposed method could be applied to different oriented facade to offer a guide line for façade design. 6.1. Methodology: By using Rhinoceros and Grasshopper, a model was created for a hypothetical indoor office space of 16 m2 area (4 m (w) x 4 m (l) x 4 m (h)). The space is a sidelit space facing the south direction at the ground floor, located in Cairo, Egypt. The DIVA component (a plug in within the grasshopper) will be used for evaluating daylight performance. The process will follow two successive stages: Stage one: this stage will evaluate daylighting performance for the hypothetical space with a mere glass façade. Stage two: this stage will use optimization process to guide façade design and define the depth of shading device that is needed to optimize daylighting uniformity in the space. Optimization parameters: All model parameters were fixed except one variable (shading device depth) which is used for the optimization as show in (Table 1).

Table 1: The optimization parameters

Optimization Parameters

shading device depth 60 cm to 240 cm (step 60 cm)

6.2. Results:

The optimization objective was set to maximize the daylit area and to minimize the over lit

and partially lit areas in the space. The last generation of the optimization process reached the

optimum solution which eventually gives a better daylighting performance. Next table

illustrates the two different stages and its results (Table 2). Table 2: The two different stages

The optimum solution is using shading device with a depth of 240 cm that offer 59% daylit .

Stage shading device depth daylit % partially lit % over lit %

one None 25 0 75

Two 240 cm 59 0 41

7. CONCLUSION This paper presents computational and generative methods in architectural design that shows high ability in contemporary design practice compared with the one-way traditional design operations of try and error. Engaging parametric optimization within these methods contributes in raising building performance through offering unlimited alternatives that can be estimated numerically to reach the optimum performance needed and get the best possible solution. The research presents and analyzes two different case studies that use parametric optimization in the design process workflow and presents a pilot study of embedding these tools within the design process in order to test, evaluate and optimize one or multi- aspects of building performance; it shows a simple method of evaluating and optimizing façade design to enhance daylighting performance, this method could be practically engaged to the each stage in the design process for more qualified performance, it also explains to what extent this process has affected building’s performance and its requirements. 8. Recommendations The paper demonstrates that integrating computational design methods and parametric optimization contribute in reaching better building’s performance. However the important

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