Advantages of CAD/CAM/CAE systems on design exploration and development by kostas poulopoulos

Konstantinos Poulopoulos 2009

Konstantinos Poulopoulos

37-076133

Professional Diploma on Architectural Engineering , N.T.U.Athens 2004

â&#x20AC;&#x153;Advantages of CAD/ CAM/ CAE computational systems on design exploration and developmentâ&#x20AC;?

Graduation Thesis for Master Degree in Architectural Engineering University of Tokyo, Department of Architecture, February 2009 Chiba Manabu Laboratory

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Konstantinos Poulopoulos 2009

CONTENTS

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PROLOGUE INTRODUCTION.........................................................................7 Hypothesis.... ...............................................................................7 Motivation...................................................................................7 Definitions.....................................................................................9 METHODOLOGY.......................................................................11

PART I : ANALYSIS I : Computing the generic problem I_1. Systems of purpose................................................................13 I_1.1. Passive / Reactive adaptation..............................................13 I_1.2. Assertive Adaptation...........................................................14 I_1.3. Human Culture as a problem-solving algorithm................15 I_2. Computing the generic problem...........................................15 I_2.1. Input data..........................................................................16 I_2.2. Processing.........................................................................17 I_2.2.a. Analysis-Genesis-Synthesis............................................17 I_2.2.b. The role of Knowledge: the library..............................19 I_2.2.c. Experience vs Innovation................................................21 I_2.2.d. Output.............................................................................22 I_2.2.e. The role of skill: Image of the solution.............................22 I_2.3. The generic computational algorithm.................................23 I_2.4. Description.........................................................................23 I_2.5. Formal vs Informal Problem..............................................24

II: Anatomy of the design problem II_1. Design as informal problem-solving activity.......................25 II_1.1. Anatomy of the design problem........................................25 II_1.1.a. Constraint evaluation and deployment............................25 II_1.1.b. The solution image: conflict or compromise...................26 II_1.1.c. The factor of time in infogenesis....................................27

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II_1.1.d. The iterative nature of the design problem.....................28 II_1.2. Computing the design problem: description.....................30 II_2. Risk assesment....................................................................31 II_3. Skill-and-Experience Based Design....................................31 II_4. Critical evaluation of SEBD................................................32

III. From Experience-Based to Exploration-Based Design III_1. Review of chapter I............................................................33 III_2. Value transfer......................................................................34 III_3. Design for exploration........................................................35

PART II : GENESIS IV: Digital Computation in Design IV_1. The Third Revolution.......................................................37 IV_1.1. The Digital Computer......................................................37 IV_1.2. Computer Hardware: brain and nervous system..............37 IV_1.3. Operating System: language............................................38 IV_1.4. Software: performance....................................................39 IV_2. Man vs Computer / Man + Computer?..............................39 IV_3. Augmented Designer / Integrated Design Team................41

V: Digital Computation in Architectural Design V_1. History.................................................................................42 V_1.1. From representation to generation....................................42 V_1.2. Sydney Opera House: father of CAAD/CAM/CAE.........43 V_1.3. CAAD/CAM/CAE systems today....................................44 V_2. Thesis...................................................................................45

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PART III : SYNTHESIS

VI: CAAD/CAM/CAE through a case study VI_1. Traditional CAAD vs Advanced CAAD............................47 VI_2. Procedural Design..............................................................48 VI_2.1. Example: the design of a spoon ......................................48 VI_3. CASE STUDY: The design of a roof structure..................49 VI_4. Parametric Design..............................................................52 VI_4.1. Example: a staircase........................................................52 VI_5. Parametrization of the CASE STUDY ROOF....................53 VI_6. Explicit design....................................................................55 VI_7. Computer Aided Engineering (CAE)................................56 VI_7.1. Up-front CAE in concept phase: CASE STUDY.............57 VI_7.2. Evaluation of Case Studyâ&#x20AC;&#x2122;s CAE analysis.......................58 VI_8. Computer Aided Manufacturing (CAM).................................59 VI_8.1. Rapid Prototyping............................................................59 VI_8.2. Rapid Prototyping in the CASE STUDY.........................60

EPILOGUE VII: Conclusion VII_1. Review...............................................................................65 VII_2. Conclusions on the Case Study.........................................66 VII_2.2. Parametric Design...........................................................66 VII_2.3. Explicit Design...............................................................67 VII_2.4. Up-front Computer Aided Engineering (CAE)...............67 VII_2.5. Rapid Prototyping...........................................................67 VII_3. Thesis Conclusion.............................................................68 VII_4. Vision.................................................................................69

Bibliography..................................................................70

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PROLOGUE

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INTRODUCTION

Hypothesis The scope of this thesis is to demonstrate that the integration of Digital Systems with traditional design techniques that are based on skill and experience significantly empower designers at the concept, development and building process of a project.The effect is quantitative and qualitative. Digital tools quantitatively increase the pace of information production. Qualitatively, they help designers create better models to simulate a designâ&#x20AC;&#x2122;s behavior. Such an integration of human cognition with the computational and operational skills of CAD/CAM/CAE Systems, brings to life the augmented designer, a symbiotic man-machine organism that is equipped with Skill and Knowledge to adress complex design problems, that demand the manipulation of unstable social, economical, structural, functional and expressional constraints.

Motivation Architecture as Art? This investigation was primarily triggered by the realization that commonly, Architectural Design is deceitfully understood as a primarily artistic, highly intuitive process, excused of rigorous proof. Such a standpoint has proved to systematically compromise other aspects of a successful agenda, such as resources management, quality control, innovation in design and building or Return Of Knowledge (ROK) to the design organization.

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This veil of mystery that covers architectural design is further cultivated by architectural education worldwide, that fails to depart from the traditional notion of the architect as a creator of aesthetically interesting forms , leaving the problem-solving process to other design engineers, as if the architect weren’t, in 1: bibliography note no. 19

fact, a design engineer as well. As A.Shalama(2005)1 puts it : “Current architectural education still socializes its members into a predominantly artistic paradigm that emphsizes personal feelings, intuition, imagination and subjective judgements at the expense of other paradigms capable of fostering the creation of humane environments”.

Extracting and Formalizing Design Knowledge. A series of disfunctional design experiments, student or professional, during the past years lead the author to a series of questions on the nature of design knowledge. If design, as earlier stated, is “more than intuition” , more than an esoteric process of inspration, what more is there to it ? Is there knowledge involved in design? Where does design knowledge come from ? Where does it reside ? How is it retrieved and used? How do master architects think and work ? Where does such an expertise stem from? Is it only experience-based? Is it only talentbased ? Can such an expertise be learned ? Up to what extent ? And with what tools ? Talent being a matter of the past (genes) and experience a matter of the future (age), it has been the utmost motivation for the author to discover the areas of the present, where design expertise can be achieved by systematic effort and investment in knowledge.

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Definitions

Skill-and-Experience-Based Design (SEBD) Architectural Design has traditionally been perceived as a somewhat heroic act pertained by skillful masters; it thrives under the wise supervision and expert leadership. This centralized approach is necessary so that the less experienced members of an organization may benefit from the implicit knowledge that resides within the Master Architect, and that is expressed in the form of Skill and Experience. Here, projects stem form the educated guesses that Master Architects are in the position to cast; the design process therafter seeks to grasp and prove the original design vision. Architectural representative techniques pursue the designerâ&#x20AC;&#x2122;s vision, and try to describe it. Such an approach does not exclude the use of digital media. CAD systems may support the process, but commonly at a low level of integration, mainly for purposes of representation.

Digital Media (CAAD/CAM/CAE) Advanced CAAD/CAM/CAE systems present an alltoghether inverted way of design, that is not vision (end) based, but processbased. It is research during the process, transformations of ideas and curiosity, concerning the new tools and their potential, that lead the way and open paths into unknown territory. It is rigorous simulation and explicit description of each designâ&#x20AC;&#x2122;s behaviour that scaffold innovative ideas and prevent their collapse. This process does not have a leader : leadership rotates according to which agent is responsible for producing the critical information at each stage of the project.

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Main Objective

This investigation will seek to identify the advantages of systematically integrating CAD/CAM/CAE systems in the traditional Skill-and-Experience Based Design (SEBD) practicum, locate them within the overall design value-adding process, and evaluate these advantages qualitatively and where possible, quantitatively. Thereafter, a critical appreciation on the change that CAD/CAM/ CAE systems bring to the traditional design methodology will be attempted.

Sub-Objectives In order to examine the above stated sentence, its analysis to workable sub-objectives is necessary. Sufficiently adressing the following will be the target of this investigation hereafter; the attempt will be to show that the implementiation of advanced CAAD/CAM/CAE systems significantly contributes in:

1......................... acceleration in production of design alternatives 2.................................................... acceleration of communication 3.................................................................. integrated design team 4.................................... integration of client in the design process 5....................................... automation against tedious calculations 6...................... data extraction and fast documentation of projects 7..................................................................... elimination of errors 8............................. management of uncertainty thoughsimulation 9...................................................... design for further added value 10.................................................................. design for innovation

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METHODOLOGY

PART I......................................................... ANALYSIS Chapters I,II and III. Anatomy of the traditional Skill-andExperience Based Design (SEBD) Process; identification of its built-in difficulties; risk assesment.

PART II............................................................ GENESIS Chapters IV and V. Computational theory; general advantages of digital computation; CAD/CAM/CAE systems in Architecture; examples of buildings.

PART III...........................................................GENESIS Chapter VI. Design case study: a roof. Procedural design for concept definition; parametrization of model for family of solutions; CAE for geometry editing and concept design guidance; Use of master model for documentation; design evaluation with CAM: fabrication of a physical rapid prototype .

PART IV..................................................CONCLUSION Chapter VII. Case study conclusions. Thesis Conclusions. Vision.

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PART I : ANALYSIS

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CHAPTER I Computing the generic problem

I_1.

2: bibliography note no. 4

Systems of purpose

According to L.Bertalanffy2, a system is a complex of items that stand in interaction. In order to understand and explain the behaviour of any system, it is necessary to understand its constitutive parts, the way they interact and the purpose of this interaction. Moreover, in the cases of open systems, it is crucial to examine the input from the systemâ&#x20AC;&#x2122;s environment. All living things, either units or societies, are open systems. As such , they are in constant exchange of energy and matter with their environment. The purpose of this dynamic interaction is generally survival and continuity. The kind of interaction, however, is varying from organism to organism. Different mechanisms of dynamic interaction have developped over time, spanning from passive to assertive adaptation of the hosting environment . I_1.1.

Passive / Reactive adaptation

Many organisms demonstrate self-regulatory behaviour in order to adapt to external fluctuations and uncertainty. Trees, for example have developped a structural system that spreads equally to space, so that it can withstand strong winds. The treesâ&#x20AC;&#x2122; response in external input is passive; the system has sufficiently evolved over time to anticipate and respond to such circumstances. We will name such self-regulatory behaviour, passive adaptation. Other living creatures, in the case of a strong wind, will react on external influence and search for shelter, running away from danger. Such an adaptive behaviour, we shall call reactive adaptation.

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I_1.2.

Assertive Adaptation

On the other hand, there are organisms that demonstrate pro-active manipulative behavior against external uncertainty. Ants, bees, birds, monkeys and humans find, or construct tools to achieve specific local goals, that are integral parts of the ultimate goal: the specieâ&#x20AC;&#x2122;s survival, contuinuity and, in some cases, dominance. The ultimate goal is achieved as a summative result of all local goals. Assertive adaptation is the intelligent transformation of existing external conditions for the achievement of goals. The goals are generally local, meaning that they serve only partial purposes of the systemsâ&#x20AC;&#x2122;s behaviour. Local goals are small, yet essential parts of the systemâ&#x20AC;&#x2122;s ultimate goal, which is survival over the competition. We can write the above sentence schematically : Existing Conditions--> Process of Transformation---> Local Goal Input I--->Processing P--->output O (1)

I ---> P ---> O

and (2)

Ooverall = O1 + O2 + ... + Ov - 14 -

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I_1.3.

Human Culture as a problem-solving algorithm

Human culture is a product of constant transformations of the host system, which is the world that surrounds us. The purpose of this transformation has originally been survival, but, that being assured, it gradually shifted towards comfort of dominance.

Problem: from Greek πρό-βλημα

The issues that humans perceive as candidates for transformation,

from (v) προ-βάλλω : issue that is

are presented as problems.

prominent, protruding, projected.

- crossing a river Examples of problems

- travelling faster

問題 (もんだい) “mondai”

- making a company profitable

subject under question

- deciding what to cook for dinner

The process that humans follow to answer problems is called computation.

I_2.

Computing the generic problem

計算する: keisan suru

To compute means to obtain desired results by processing given

compute, to”count with arithmetics”

information. Computation is any problem-solving algorithm. As

compute: to determine through calculation.

such, it escorts humanity from the depths of its existence, from early hunting up to creating satellite telecommunications. The generic computational model is : Computation = Input-->Processing-->Output

COMPUTATION

(3)

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I_2.1.

Input data

The set of useful input data fed to the generic computational machine are called constraints. Constraints are agents that sufficiently define a problem. The solution to a problem is the locus of all its constraints. The answer to a problem is a locus that satisfices its most important constraints. 3: bibliography note no. 3

According to Herbert Simon3,constraints are of three inclusive kinds: the conditions of the external external environment, the system’s internal restrictions and the network of the desired goals, that pose the actual problem. For example, at an architectural problem, the external environment’s conditions namely include the building legislation, the site’s soil condition, etc. The internal system’s restrictions might include the client’s budgetary limit, the architect’s design skills, etc. Desired goals might be functional integrity, simplicity of form, integration of energy efficient systems etc. We can hence write: Input = ∫Constraints (EΧT, IΝT, GΟAL)

(4)

or:

EX: external conditions IN: internal limitations

GO: desired goals

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I_2.2.

Processing

I_2.2.a.

Analysis-Genesis-Synthesis

4: “Heuristic, or heuretic, or ars

According to Pappus, an important Greek mathematician who

inveniendi, was an area of a certain

lived around 300 b.C., problem-solving , or heuristics, is a two-

branch of study , not very clearly circumscribed, belonging to logic,

stepped process. The first part of it, analysis, is a process where “...

or to philosophy, or to psychology....

we start from what is required, we take it for granted, and we draw

The aim of heuristic is to study the

consequences from it , and consequences from the consequences,

methods and rules of discovery and

until we reach a point that is well known and can serve as starting

invention. Polya G., “How to Solve it.” (1945) Princeton University Press, primitive: non-reducible bit of knowledge

point.” The second part, synthesis, “...reversing the process we start from the point we reached last of all in the analysis....we move upwards, retracing our steps, until we succeed in arriving at what is required”4.

compound: bit of knowledge made of primitives that are explicitly

Analysis is the top-down decomposition of the ultimate goal into

connected.

smaller and easier sub-goals. It yields known and almost-known

structure: the interconnectivity network of primitives or compounds.

components (primitives or compounds) and their interconnectivity (structure).

ANALYSIS

EX IN GO

Synthesis is the inverse bottom-up recomposition of

known

and produced components , according to their interconnectivity network (usually a hierarchical tree), in order to reach the goal.

EX IN GO

SYNTHESIS

EX IN GO

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EX IN

Konstantinos Poulopoulos 2009

EX IN

5 In-between, J.Doblin traces genesis. This is the core stage of IN I EX

5: bibliography note no.17GO

production of new information, by use of intelligence. Most I

common systems of information generation are : direct application,

EX IN

transformation, mutation of information coming from analysis,

and sometimes invention of all-together new information.

INFORMATION GENESIS = INFOGENESIS

Processing = Analysis---> Genesis---> Synthesis. P = A ---> G ---> S

(5)

Hence the whole process can be drawn as follows: ANALYSIS

EX IN GO

SYNTHESIS

INFOGENESIS

EX IN

or simpler:

COMPUTATION

= I

EX IN GO

A G

G O

EX IN

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I_2.2.b.

The role of Knowledge: the library

How is analysis possible? Where do components and their interconnectivity networks come from? Does analysis mean the generation every single time, of all this information ? Analysis is possible thanks to knowledge that resides within and around the problem-solver. Knowledge can be explicit, meaning well understood to its components and their interconnectivity, (such as the body of mathematics) or implicit, meaning used and applied in a tacit fashion, not rigorously grasped (such as my

MEMORY

grandmotherâ&#x20AC;&#x2122;s knitting skills). COGNITION + MEMORY

Knowledge bits are stored in human cognitive skills, tools and MEMORY

MEMORY

techniques. Example of human cognitive knowledge is e.g. a NETWORK

NETWORK designerâ&#x20AC;&#x2122;s decision-making expertise; knowledge in the form of a

tool might be the pencil , or more abstractly, the grid; knowledge HARDWARE

SOFTWARE

COGNITION

within a HARDWARE

technique might be r the anti-seismic regulation code or TECHNIQUES TOOLS SOFTWARE

how to design a steel structure.

+ HARDWARE

+ SOFTWARE

We shall call a library L the network of cognitive memory skills, tools and people techniques that reside within and around the problem-solver. network

Library = Cognition + Tools + Techniques TOOLS

TECHNIQUES

product

process

hardware

or: software

L= COGN + TOOL + TECH

primitive: non-reducible bit of knowledge

(5)

We can further analyze each one of them to components (COMP)

compound: bit of knowledge made

and structures(STR) that hold them interrelated. Components

of primitives that are explicitly

are either primitives, e.g. a wooden beam, or compounds, e.g. a

connected.

floor. Structures are the laws responsible of holding components

structure: the interconnectivity network of primitives or compounds.

together to form higher-level entities. Example of a structure is the hierarchical connection of wooden beams of a roof.

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Therefore we can write: COGN = COMPCOGN + STRCOGN TOOL = COMPTOOL + STRTOOL

(7)

TECH = COMPTECH + STRTECH By combining (5) and (6) follows that :

(

EX L= COMP COGN + COMPTOOL + COMPTECH

A ( STR

G+

COGN

STRTOOL + STRTECH

or equivalently: COM

STR L=COMP (COGN,TOOL,TECH) + STR (COGN,TOOL,TECH)

(8)

Clause (8) means that a Library can be perceived as the sum of components and structures of cognition, tools and techniques. The following scheme is then derived:

L COM: components STR: structures

The library constantly feeds the analysis process with known components and structures. Simultaneously, it is fed back with interesting findings that are products and by-products of the analysis/genesis/synthesis (AGS) algorithm. Such a feedback is what actually increases the skills of the problem-solver. The library is the workshop of the problem-solver.

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Embedding the concept of the library into the computational diagram will update it as follows:

EX IN

COM

STR

It is clearly shown that computation is a process of identifying and combining input constraints under the influence of library-based analysis, generating information and recomposing them towards

a desired output.

COM: components STR: structures

I_2.2.c.

Experience vs Innovation

It is understood, then, that problem-solving is largely depending on the problem-solverâ&#x20AC;&#x2122;s knowledge library to perform effective analysis. Problems that have common parts with known components that are stored at the problem solverâ&#x20AC;&#x2122;s library L, are probably easier to approach and solve. The larger the library, the more the possibility for computing a solution to the problem at hand. On the other hand, experience-lead research might inhibit innovation; if innovation itself is an important constraint of the problem, experience is important, but serious play (M.Schrage 6: bibliography note no. 8

1999)6 is more likely to bring about innovative solutions.

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I_2.2.d.

Output

The output of a problem is the result, and it should be or approach the desired goal. It is essential that the output is fed back to the problemâ&#x20AC;&#x2122;s constraints to ensure that it indeed is the answer to the problem. Then , it must be stored to the Library as a known item, for future use. I

EX IN

O O

COM

STR

EX IN

I_2.2.e. L

EX IN GO

IMG COM

STR

The role of skill: Image of the solution

COM STR

The problem-solving process often asks of a vision. We shall call EX I IN the starting point , image of the solution. The imageAof theGsolution S IMG

L analytical process; starting from an imaginary is what drives the COM: components

STR: structures solved state, the problem-solver tries to trace backwards to already

COM

L STR similar problems with known solutions known components,i.e.

! for knew knowledge to be or establish new?research directions IMG

OUT

applied to the process. The Output, which is the answer to the problem, is the proven and established original theorem that Image proposes. L The Image is the imagined Output of the problem, before the COM: components problem is even solved.

STR: structures

IMG

OUT

Setting up the most likely image of a solution is an essential skill of a good problem-solver. - 22 -

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EX IN

I_2.3.

The generic computational algorithm

COM

The generic computational algorithm for solving problems can

STR

then be updated as follows:

EX IN IMG

O O

COM STR

I_2.4.

COM: components The

Description

generic copmutational algorithm can be described as :

STR: structures

- original input of constraints I (external, internal, goals).

IMG

-perception through skill and experience of the image IMG of the solution. OUT

- analysis A of the image, with the help of the library L, to known components (primitives or clusters). - generation G of information through translation, mutation, invention / feedback to library L. - synthetical recomposition S of findings according to structure proposed by analysis / feedback to library L. - feedback of output O to the constraintsâ&#x20AC;&#x2122; space, for confirmation. - feedback of output O to library L.

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I_2.5.

FORMAL -well defined and constraints -limited number of constraints

Formal vs Informal Problem

CONSTR

INP

There are problems that have specific and rigorously defined area of investigation; concrete and sufficient number of constraints; and

have a proven and unquestionable answer as output, e.g. how

-clear image of the solution

many bones does the human body have ? what is the distance

-rigorously proven solution

between the earth and the moon ? Moreover, this type of problems

LIB

has a clearly perceived image of the solution; for example we can imagine that the distance between earth and moon must be a big COMPONENTS

number, e.g. calculated by counting the time that moonlight needs STRUCTURES

to arrive to earth.

We will call such problems formal. There are two kinds of formal problems : problems-to-find and

problem

informal

problems-to-prove. The former have a certain unknown quantity formal

x to be found by the combination according to laws, of the known quantities a,b,c,...v. The latter have a certain sentence to prove

problemto-find

problemto-prove

INFORMAL -ill-defined constraints -large number of constraints

right or wrong, through logical steps. In both cases, the approach is largely that of the generic problem; the result is a solution, .

On the contrary, there is another type of problems that suffer from uncertainty and complexity in the area of the constraints; due to

this the image of the solution cannot be easily perceived; therefore,

-unclear image of solution

the solution cannot be easily expected, assumed, or even guessed.

-open answer, not solution

Such problems are : What is the proper way to dress for a wedding? Who is the right person to start a business with ? How does one decide what house to build? We will call such problems, informal. Informal problems cannot be solved; they can be adressed to and answered.

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CHAPTER II Anatomy of the design problem II_1.

design:

Design as informal problem-solving activity

Design is the computational process that produces desired

1.to prepare the preliminary sketch or the plans for (a work to be

artifacts. It does so, by transforming existing data, by means of intelligence.

executed), esp. to plan the form and structure

of: to design a new

bridge.

IMG

2. to plan and fashion artistically or skillfully.

3. to intend for a definite purpose 4. to form or conceive in

The existing data in a real-world design problem are numerous, their nature is complex and the process of evaluating their importance, uncertain. Therefore, design problems are informal problems.

the mind; contrive; plan 5. to assign in thought or intention; purpose

We will define and further discuss design as the computation of informal problems for the production of desired artifacts.

TRANSLATION

fast / rare

TRANSFORMATION

slow / very common

CREATION

very slow / common

II_1.1.

Anatomy of the design problem

An anatomy of the design problem will be attempted hereafter. Also, inherent process risks will be discussed. II_1.1.a.

The most important problem a design engineer faces is the large

number of constraints, their varied and Îżscillating nature. For

Constraint evaluation and deployment

example, an architectural project is a polyparametric problem that has to deal with unstable economic environment, unstable aesthetics of all stakeholders, weather, site, country specifications, various legislation directives, environmental issues etc.

INP

CONSTR

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Konstantinos Poulopoulos 2009

CONSTR

INFOGENESIS

INP

TRANSLATION

fast / rare

TRANSFORMATION

slow / very common

CREATION

very slow / common

IMG

C to investigate the constraint Therefore, the first task of a designer is space and discover the constraints that will be decisive for a

succesful project. Such an evaluation decides the starting point of LIB

a project and gives an idea for its development, while secondary,

COMPONENTS

tertiary, etc constraints will be deployed during the process.

STRUCTURES

INP

CONSTR problem

informal

formal

problemto-find

problemto-prove

LIB COMPONENTS STRUCTURES

O Risk 1

It is an essential task to evaluate and deploy constraints in an

RISK #1

optimal way, that will be productive along the process. Due to high complexity, the risk is higher where experience is not sufficient. II_1.1.b.

The solution image: conflict or compromise

The solution image of a design problem depends heavily on the evaluation and decision making of original constraints. Inversely, the choice of the original constraints depends heavily on the solution image that is being born in the designerâ&#x20AC;&#x2122;s mind. Therefore, the decision of the original Input Constraints is decided as a result of compromise between costraint analysis and the insight of the solution image. CONSTR

INP

IMG

LIB COMPONENTS STRUCTURES

INFOGENESIS

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fast / rare

TRANSFORMATION

slow / very com

Konstantinos Poulopoulos 2009

The conflict beween constraint analysis and solution image is

Risk 2

inevitable. Decision-making is itself compromise transgressing a situation of conflict. Often, especially when experience is short, conflict prevails compromise and the design project is at risk.

CONSTR

II_1.1.c.

The factor of time in infogenesis IMG

INP

The ?scheme Analysis-->Genesis-->Synthesis of the generic

problem is valid in the design problem. The information generation I

EX IN

G G

is possible through: translation of known components to the new

LIB O

COMPONENTS

context; transformation of old concepts to updated and useful ones;

STRUCTURES

creation of alltogether new concepts.

INFOGENESIS

CONSTR

TRANSLATION

fast / rare

TRANSFORMATION

slow / very common

CREATION

very slow / common

RarelyINP is it possible to IMGsimply use ready-made components. In C most cases, a design problem demands transformations of a known ? concept or alltogether new concepts to deal with exhaustively ?

uncertain project conditions. Infogenesis demands time. For LIB COMPONENTS STRUCTURES

example, in an architectural design, typically a few days are needed for the production of comprehensive material (drawings, models , etc) for evaluation.

CONSTR

INP

problem

informal

Risk 3 formal

problemto-find

problemto-prove

? Time is always an enemy to project development. Therefore, an inherent risk of the design process is the exhaustion of time LIB

resources and the consequent compromise of the projectâ&#x20AC;&#x2122;s course, COMPONENTS

STRUCTURES due to slow infogenesis.

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RISK #1

Konstantinos Poulopoulos 2009

II_1.1.d.

The iterative nature of the design problem

The design problem is an iterative process, where a series of temporary outputs are re-fertilized with new constraints and reprocessed in the Analysis-Genesis-Synthesis engine. Each step should yield higher complexity and refinenment outputs, until the final design occurs. The concise diagram can be described as follows:

ITERATIONS ITERATIONS INP

CONSTR

? LIB

CONSTR

INP

IMG

PROCESS

IMG

? S

OUTPUT

PROCESS

OUTPUT

LIB

COMPONENTS STRUCTURES

COMPONENTS

STRUCTURES

O3 V

satisficing of constraints (value)

? ? ?

? ? ? ?

T Due to the increased complexity of the design? problem , the process iterations (time)

deadline

is essentially an iterative filtering of temporary outputs O, until a

iterations is (time) satisfactoryV answer is reached. Satisfaction defined here as the

deadline

V filtering occurs in the form of a question: satisficing of constraints (value)

intersection : satsificing

satisficing of constraints (value)

commonplace of the solutions of local costraints. At each iteration,

- Does output Om satisfice its constraints? - If no, stop and restart from previous output Om-1 . t1 iterations (time) t2=deadline - If yes, enrich Om with more constraints and proceed for Om+1.

iterations (time)

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O1 O2

Konstantinos Poulopoulos 2009

satisficing of constraints (value)

? ? ? ? ?

T iterations (time)

Ideal iterations diagram

deadline

The loop contiues until a solution Ov is found that satisfices the V

majority of constraints, and certainly, the more important ones.

INP

CONSTR

value

the

IMG

building-up

PROCESS

? knowledge and the successful STRUCTURES

embedding of information on the design over time.

Value V in a design problem is added when each phase is including

S A theGconstraints of O1 the previous and can move forward with new,

more refined constraints. Therefore, the more inclusive is the O2

original solution image IMG, the further can a design problem be advanced and fertilized with constraints, i.e. value V. O3

Risk 4

t1 iterations (time) It is an inherrent risk within the design process that the iterationst2=deadline satisficing of constraints (value)

COMPONENTS

OUTPUT

(time) are consumed while designers still seek for the original ?

solution image IMG. Most? of the times, that means that project ?

time will either? be developped beyond the deadline (t3), or that ?

project value will be compromised (t2). In some cases it might lead T

mean that the designer be fired, as uncapable of dealing with the iterations (time)

deadline

specfic design problem (t1). V satisficing of constraints (value)

LIB

satisficing of constraints (value)

ITERATIONS

iterations (time)

t2=deadline

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Konstantinos Poulopoulos 2009

II_1.2.

Computing the design problem: description

Design is an iterative process, where the original image of the solution IMG is enriched at each step with more constraints, producing temporary outputs O1,O2,.... The process of embedding value in an output Om is occuring at a tripartite analytical-geneticalsynthetical engine AGS that manipulates given constraints, generates new information (infogenesis) and re-composes them to a higher level output Om+1. If not, the output Om returns to the stage of infogenesis for re-evaluation and reprocessing, before it is checked again for consistency. The process ends when all constraints, are eventually integrated in the final output Ov. - 30 -

Konstantinos Poulopoulos 2009

II_2.

Risk assesment

Anatomy of the Design Problem Computation method under the scope of locating and estimating risks, yields the following: Risk 1

Evaluation and incremental deployment of design constraints is difficult due to high complexity and uncertainty of the definition space of a design problem.

Risk 2

The design process can start only if the conflict between the solution image IMG and the original constraints gives way to compromise. If not, the project is at stake.

Risk 3

The progress of a design project depends on the speed of infogenesis. Slow infogenesis impedes progress.

Risk 4

The progress of a design project depends on constructive iterative evaluation, that increases project value with time. Slow iterations result to few iterations. Few iterations result to less refined designs. II_3.

Skill-and-Experience Based Design

The aforementioned risks are generally mitigated through the wise and skillfull leadership of experienced designers that steer the design process. Because of this dependency on Skill and Experience, the design problem answering process can be also called Skill-and-Experience Based Design (SEBD). SEBD is decision-driven, or vision-driven; the artifact is well imagined and the process is aimed at reaching it. SEBD is not process-driven and exploration-driven.

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Konstantinos Poulopoulos 2009

II_4.

Critical evaluation of SEBD

According to all the above, the following conclusions are derived: conclusion 1

The traditional Skill-and-Experience Based computation of a design problem (SEBD) depends gravely on the decision-making at early stages of the process, where design knowledge is at its mininum.

conclusion 2

Dependency on experience is vital for mitigating the start-up conundrum.

conclusion 3

Dependency on experience is also vital for accelerating infogenesis, which is generally slow.

conclusion 4

The experience-based design often compromises research for innovation; designers often solve problems according to the contents of their library L, which can only yield known solutions.

conclusion 5

The exploration space of the solution is being limited in the existing library; therefore, chances of collateral knowledge beyond the specific design problem are lost. SEBD does not promote the idea of design as research.

conclusion 6

From a philosphical viewpoint, Skill-and-Experience Based Design (SEBD) is deterministic; it implies that there is a mastermind that designs the world and that the world should be made to fit within these answers. Such a cosmology is de facto proven obsolete; nature is blind in producing endless alternatives of itself, sustaining successful designs and aborting unsucessful ones. Such a probablistic view of the world is free of guilt in producing significantly different, but equally valid functional designs.

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iterations (time)

t2=deadline

Konstantinos Poulopoulos 2009

CHAPTER III From Experience-Based to Exploration-Based Design

III_1.

Review of chapter I

So far we have seen that Skill-and-Experience Based Design, or SEBD, is a master-mind driven process, where a more or less clear vision of a solution is pursued. This is happening through an iterative process, where the original image of the solution IMG is enriched at each step with more constraints, producing temporary outputs O1,O2,.... The process of embedding value in an output Om is occuring at a tripartite analytical-genetical-synthetical engine AGS that manipulates given constraints, generates new

ITERATIONS

information (infogenesis) and re-composes them to a higher level output Om+1. The process is ended when all the constraints that are perceived as important, are integrated in the final output Ov. Afterwards, SEBD’s innate weaknesses were examined: too much experience-based; not promoting innovative designs; not CONSTRAINTS

supporting “design as research”; incorporating original assumptions of potential high risk, because of poor knowledge at early stages. It has been shown that the reason for dependence on experience is the slow infogenetic process.

PROBLEM

Slow infogenesis compromises the speed, and therefore the number, of design iterations, which in turn impedes the refinement of a design.

SOLUTION ?

What if we could speed up infogenesis ? How would that transform the design process?

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Konstantinos Poulopoulos 2009

III_2.

Value transfer

too much uncertainty...

no time!

V more value !

early knowledge...

ASSUMPTION 1

Could accelerating infogenesis help handle early complexity more efficiently, so that early decision-making be done with the minimum possible risk and the maximum possible knowledge? Could we transfer project value early, where exploration is easier and mistakes cheap to correct? Could we imagine a design process where the dependency on experience is less, thanks to more knowledge that can be applied successfully? Is there an opportunity for more knowledge-based decisions in the early stages of the design, whose influence on the course of a project is critical? How can designers be aided to confirm that a design direction is indeed interesting and within constraints, while at the same time, abort underachieving potential solutions?

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Konstantinos Poulopoulos 2009

Design for exploration

ITERATIONS

III_3.

CONSTRAINTS

ASSUMPTION 2

Could faster infogenesis transform design from being a deterministic process, to a probablistic one, where an exhaustively explored design space could yield the best possible answer, along with knowledge of the design space itself?

Wouldnâ&#x20AC;&#x2122;t such an open, procedure-based, rather than end-based process find more than it originally set out to? Wouldnâ&#x20AC;&#x2122;t such a practice benefit a designer or a design organization in the building of new knowledge?

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Konstantinos Poulopoulos 2009

PART II : GENESIS

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Konstantinos Poulopoulos 2009

CHAPTER IV Digital Computation in Design IV_1.

The Third Revolution

Culture advances with technology. Technology comes in waves, that shake the established ways of life and reshape our understanding of the world, our society and ourselves. The first such wave was the Agricultural revolution, somewhere near 8000 b.C.; it involved the invention of the wheel, irrigation and cultivation of land, and it was the step that decided manâ&#x20AC;&#x2122;s transcedence from active adaptation to assertive adaptation. The second revolution, which occured at the end of the 18th century, the Industrial, concerned the replacement of human muscle with the power of machines that consume energy. The third Revolution, the Digital Revolution, that started after WW2, and is mature in our days, concerns the support of human intelligence by information processing machines. (W.J. Mitchel and M.McCullough 7).

7: bibliography note no. 14

IV_1.1.

The Digital Computer

The product of the Digital Revolution is the Digital Computer. It is fundamentally an information processing machine that transforms

existing data to desired data. Digital Computer Computes Calculates towards desired situations Design The primary goal of the digital computer is to assist to the design problem-solving activity.

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Konstantinos Poulopoulos 2009

IV_1.2.

Computer Hardware: brain and nervous system

Artificial Intelligence is designed at the image of human intelligence. The computer is the mirrored image of the human brain. Like the human brain, it posesses two fundamental capabilities: memory storing, (long-term RAM, or short term ROM) and reasoning, performed by the Central Processsing Unit). These two functions are possible thanks to input devices (such as the keyboard, scanner, etc) and can be perceived and evaluated by humans through output devices (such as the screen, printer, 3D printer, speakers, etc). Input and output devices represent the central nervous system of the computer.

IV_1.3.

Operating System: language

The Operating System is the program that controls the interface between the mechanical parts of the digital computer and the higher level processes that they perform. For example, it controls how and where information is saved, how it is introduced into the system, where it is processed, with what order, and how it is displayed to the user.Most of all , the Operating System is the platform where Software runs on. Considering again the simile of the digital computer and the human brain, the Operating System is for the computer what language is for human brain. Language is the primary system that enables communication of internal human conditions, such as feelings and ideas, and higher level operations, such as remembering, observing, etc. It is impossible to articulate any meaning at all without having to resort to language; a wheel is a meaning as it is a word.

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Konstantinos Poulopoulos 2009

IV_1.4.

Software: performance

Software is the reason why computers are built: to perform. Software is the higher-level organization of the digital system to compute data of a certain kind. For the digital computer, it does not make a difference how data are perceived by the human user; it will patiently and persistently compute lists of 0’s and 1’s. For the user however, it is extremely important to be able to input information in a form that is suitable with their cognitive system of semantics, and respectively obtain results that are easy and quick to grasp. Software is for the human brain a set of simpler actions put together for specific purpose, e.g “making a snowman”. IV_2.

8: bibliography note no. 27

Man vs Computer / Man + Computer?

J.M. Boyle8 (1989), on his article “Interactive Engineering Systems Design” attempted to draw a comparative chart of man vs machine information process capabilities. Man

Machine

short-term STM

limited in size

large

long term LTM

large but unreliable

unlimited and reliable

Memory retrieval

Variable

Fast from both LTM STM

Numerical Data Input

Slow

Fast

Graphical Data Input

Fast but little detail

Slower but High Detail

Uncertainty of Data

Capacity to handle limited

Complex ill-defined methods

amounts of uncertainty

(current research area)

Can handle:

-unexpected events

-expected events

-slow variations

-fast on-line data

-variations in few

-simultaneous parameters varia-

parameters

tions

Memory Capacity

Data handling

Time Varying Data

continues to next page - 39 -

Konstantinos Poulopoulos 2009

Man

Machine

Limited capacity of

Fast parallel input of

numerical information

Simple calculations

Fast

Complex calculations

Slow

Fast

Fatigue

Problem

No problem

Distraction

Problem

No problem

Repeatability

Unpredictable

Excellent

Emotional Factors

Problem

No forseeable problems

Ability to abstract

Good

Poor

Acquired with age

Machine Learning under research

Parallel Data Input

Performance

and conceptualize Experience

Poor or Nil Commonsense

Good

Social Skills

Good

Poor

The above chart clearly demonstrates the need for a complementary existence of man and machine to a higher performance entity, that shares the abilities of both domains. On the one hand, human cognition provides abstract thought, conceptualization and inspiration, while the digital computer contributes with

unlimited and well accessed memory , fast

calulation skills, a toolbox of software operations, precise representations of concepts and

invariant persistency in

calculations of any kind, that human nature cannot compete.

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MEMORY

COGNITION + MEMORY

Konstantinos Poulopoulos 2009

MEMORY

MEMORY HARDWARE

HARDWARE

SOFTWARE

S ARE

SOFTWARE

IV_3.

MEMORY COGNITION

TECHNIQUES TOOLS + + SOFTWARE HARDWARE COGNITION + MEMORY

Augmented Designer / Integrated Design Team MEMORY memory

NETWORK people

NETWORK

network

HARDWARE TOOLS

As it was discussed earlier, resides in the designer’s TECHNIQUES TOOLS knowledge SOFTWARE

HARDWARE

SOFTWARE TECHNIQUES

SOFTWARE hardware HARDWARE software tools library L, in the forms of cognition, and techniques. process

product

memory

COGNITION

people network

Respectively, knowledge in a computer system resides in its

WARE

NETWORK

TOOLS

TECHNIQUES

hardware and software. software memory, processits hardware

product

COGNITION + MEMORY MEMORY

MEMORY

NETWORK

Moreover, it resides to the network that is created by their SOFTWARE

HARDWARE

+ interconnection.

COGNITION + MEMORY COGNITION MEMORY

TECHNIQUES SOFTWARE

The augmented designer is the high-performance symbiotic

network

NETWORK

organism that occurs by integrating the two cognitive realms, the

hardware process TECHNIQUES

product TOOLS + HARDWARE

SOFTWARE

memory

people

NETWORK

TECHNIQUES

TOOLS + HARDWARE

SOFTWARE

physical and the digital. Here, integration means the selection of

software

each domain’s inherent advantages, that are able to cover for the

+ SOFTWARE

other domain’s weaknesses.

memory

The integrated design team is a network of digitally literate

people network

process

hardware

experts of various disciplines, that systematically produce and communicate information. The integrated design team does not

software

have a leader; leadership rotates and is only occasional; each time, is the engineer that is responsible for the production of critical information at every stage of the design process. Integrated design teams are like jazz bands, not classical orchestras.

INTEGRATED DESIGN TEAM MEMORY

COGNITION + MEMORY MEMORY

MEMORY

NETWORK

HARDWARE

SOFTWARE

HARDWARE

SOFTWARE

TOOLS + HARDWARE

memory

COGNITION

people

TECHNIQUES + SOFTWARE

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Konstantinos Poulopoulos 2009

CHAPTER V Digital Computation in Architectural Design

V_1.

History

Digital support on design can be traced back to the early 1960’s. Its original form, Computer Aided Design or CAD, emerged as a research field launched by aviation and automotive industries (eg. Lockheed-Martin, Citroen,etc). CAD aimed at providing engineers with the comprehensive software that would serve at sufficiently describing a design; in other words, CAD software was originally introduced for the production of precise 2D drawings that would be used for a project’s construction and management. The developments in technology that transformed computers from large corporate machinery to personal tools (Personal Computers or PC’s), helped the transfusion of CAD into architectural engineering. The emerging field, Computer Aided Architectural Design, or CAAD dedicated in producing software especially for engineers related to the AEC (Architecture, Engineering and Construction) industry.

V_1.1.

From representation to generation

CAAD immediately served architects in producing accurate representations of ideas, decisevly connecting design with the manufacturing processes of construction industry. Very quickly, though, the playfull nature of the profession unveiled certain fascinating possibilities in the concept design of new buildings, foretelling the future of architecural design and production.

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Konstantinos Poulopoulos 2009

V_1.2.

Sydney Opera House: father of CAAD/CAM/CAE

Sydney Opera House by J. Utzon (1956-1973) is the earliest and most telling example of the design revolution that CAAD systems were about to bear. On the one hand, CAAD systems made the conception and the actual drawing of the project’s shell structure possible. Until that time, the drawing of circles of very large radius was impossible; circles could only be drawn as big as the biggest compass around. CAAD systems helped the architects and engineers describe the exceptional forms of the building as parts of spheres of very large radius. In the virtual design space, such spheres could easily be drawn. On the other hand, CAAD systems made engineering and construction of such a complex structure, possible. Arup & Partners researched this direction exhaustively for 14 years (1957-1954). The result of this extensive research became the heritage of today’s CAAD advances; they developped the first parametric modelling software; they connected the designer’s virtual desk with the industry, proliferating Computer Aided Architectural Manufacturing (CAAM) ; they developped engineering simulation methodology in order to compute difficult engineering issues without having to resort to physical models , thus opening the path for Computer Aided Architectural Engineering (CAAE).

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Konstantinos Poulopoulos 2009

V_1.3.

CAAD/CAM/CAE systems today

Today CAAD/CAM/CAE systems in architectural design and construction are an integral part of the AEC industry. Major projects, such as F.O.Gehryâ&#x20AC;&#x2122;s Guggenheim Museum in Bilbao(1997) , or Renzo Piano â&#x20AC;&#x2DC;s Kansai Airport (1994), were realized through the extensive use of digital computational systems that help architects and engineers grasp, resolve and execute such complex building projects. Other examples: Mercedes Museum, from UN Studio (2006), the Hongluo Clubhouse (2006) and the Absolute Towers (under construction) by Mad Architects. Increasingly, emerging architectural practices inpcorporate advanced CAAD/CAM/ CAE techniques for concept, development and construction of significantly smaller projects, such as KBAS in the Pentagon Memorial for 9/11 (2008).

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Konstantinos Poulopoulos 2009

V_2.

Thesis

It is now possible to articulate the central thesis that this research proposes. The investigation from this point on will focus on proving the following propositions. PROPOSITION 1

CAAD/CAM/CAE systems accelerate infogenesis and intensify design exploration and development.

PROPOSITION 2

More intensive exploration through CAAD/CAM/CAE systems in the early stages increases the possibility for : - better and well accepted solutions of the design problem - innovative solutions - collateral knowledge

PROPOSITION 3

Exhaustive early research ignites value adding early in the process, thus leaving time for more refined designs.

PROPOSITION 4

CAAD/CAM/CAE systems ultimately help a design organisation to survive in a competitive biusiness environment which demands that designers deliver “Faster, Cheaper, Better” and “New”.

PROPOSITION 4

Extensive CAAD/CAM/CAE-based design research transforms the design process from end-based to procedure-based (procedural design), or from deterministic to probablistic.

The above propositions will be examined through the case study of a roof structure in CAAD, CAM and CAE practices, followed by the related theory. Subsequently, local conclusions from each experiment will be assembled to global conclusions at the end.

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Konstantinos Poulopoulos 2009

PART III : SYNTHESIS

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Konstantinos Poulopoulos 2009

CHAPTER VI CAAD/CAM/CAE through a case study

VI_1.

Traditional CAAD vs Advanced CAAD

Traditional CAAD software started out to help architects in accurately designing an original idea, by use fo 2D, 3D and 4D representations. Architects that use CAAD for representational and draughting purposes only generally work at a deterministic way, that contains a risk of erroneous decisions and can only prosper through experienced leadership. Skill-and Experience-Based Design (SEBD) uses traditional CAAD for representation of visions, not for their exploration. Advanced CAAD that emerges through technological and software developments, eventually transforms architectural design from vision-driven to process-driven. This is possible through rapid and numerous operation-driven transformations, or mutations, of original input entities. In these cases, innovative design emerges naturally, since there is no original vision to be reached or promise to be fulfilled. Instead the design process becomes serious play.

Rhinoceros 4 toolbox

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Konstantinos Poulopoulos 2009

VI_2.

Procedural Design

The concept of serious play sheds light in the modern CAAD smooth

design process. Design tools transform into “toys” and design becomes a playful procedure of exploration and discovery. The “toys” that allow such a curiosity-based design process, are

bend

called operations. Operations are applied upon existing objects and transform them parametrically, according to the designer’s commands. The speed of operations that can be applied on an

taper

object is real-time, thanks to the computational power of the digital computer and thanks to the real-time representation on the computer screen. The number of operations can be unlimited,

twist examples of operational transformations

transforming a given object into a desired functional object. VI_2.1.

Example: the design of a spoon

This fast design is produced through a create-->modify algorithm. There is no vision of the design. The starting point is nothing like a spoon. The mutations of the primitive object ( a box) is what brings about the final result. In this case, the procedural transformations are : pulling of control points and smoothing of final geometry. Procedures being so fast, the designer is allowed to quickly evaluate and explore alternative directions.

design time= 10 minutes

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EX IN IMG

O O

COM

Konstantinos Poulopoulos 2009

STR

L COM: components

Procedural design diminishes the importance of the image of

STR: structures

solution IMG. In SEBD, the starting point is an attempt to predict IMG

OUT

the desired design. In Procedural Design, the original input is simply a departure point; it is the vehicle that will help the design emerge through multiple mutations, until a satisfactory result is reached.

VI_3.

CASE STUDY: The design of a roof structure

Playfulness is a platform for innovation. Procedural design is serious play. Therfore, Procedural Design is a platform for innovation. The design criteria of this experiment is the design of a roof structure 60 m x 20 m that has no interior columns and provides a well lit multi-purpose covered space.

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Konstantinos Poulopoulos 2009

At this experiment, the ulitmate goal is to find the form of a complex grid structure that adresses simultaneously both archtectural and structural issues, providing an integrated solution. Here, the design occurs as a manipulation of a plane surface of 60 x 20 m. The operations that bring this result about are similar to the spoon experiment; they only have different departure point (the plane), and they are enriched for extracting the structural grid off the surface.

abstract concept the support of a waved surface on only 4 areas.

operations 1.introduction of plane 2.control point editing 3.tesselation of surface 4.extraction of structure mesh 5. extrusion desgin time : 40 minutes

- 50 -

Konstantinos Poulopoulos 2009

geometry check curvature evaluation (E-Map and Zebra commands)

form evaluation production of 3D perspectives for form studying and communication rendering time: approx.1 hour

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Konstantinos Poulopoulos 2009

VI_4.

Parametric Design

Early design stages yield a concept design, upon which stakeholders concept

agree and decide to develop. The concept design is never the final design; it has to pass through a large number of of micro-

transformations that will corect mistakes, exploit oportunities and finilize the product for manufacturing. Optimization of a concept

final product

is the most painstaiking and long process within the design algorithm. The design iterations might be hundreds, and , in cases of complex geometries, they might intimidate the designers away from a desired solution and lead them towards a more conventional design, which is easier to grasp and develop. Parametric design adresses the issue of project development and small-scale to medium-sacle fine tuning of a design project. The parametric 3D model does not represent a state. It simulates a process: it is constructed in a hierarchical fashion, from basic components to their dependents. The parts of a hierarchical model have a child-parent relationship, where the child cannot exist if the parent is not preceding. VI_4.1.

Example : a staircase

The design of a staircase can be proved a challenging task; even though the principles of this design are simple, optimization may present difficulties: the few parameters integrate towards a polyparamteric problem that can be time consuming to resolve. Here, a parametric NURBS curve is the spine of the staircase. The stepsâ&#x20AC;&#x2122; topology depends on the position of the curve. Their number and integers are parametrized and externally controlled. Similarly, the handrail is dependent on the topology of the steps. The thickness of the tubes is also parametrically defined. A change in the curveâ&#x20AC;&#x2122;s topology affects the topology of the entire model. Multiple alternatives are easily tested. - 52 -

Konstantinos Poulopoulos 2009

VI_5.

Parametrization of the CASE STUDY ROOF

The use of procedural operations (e.g. the transformation of a simple 60x20 m plane) produced a temporary design output for the roof project. This is obviously a vision, that will have to be further studied and edited until sufficient design information is embedded in the proposed design. It is expected that this temporary output will have to be further optimized and subject to change: for example, so far, it is absolutely unknown wether the proposed geometry is actually good enough to be constructed, or how this could happen. Therfore, a parametric model of the proposed design would allow the designers to manipulate, fine-tune and regenerate information easily, for both faster and better results. The parametrization of a design is an essential moment between concept design and design development. It means that the central idea of the design has frozen and the task at hand is to investigate it thoroughly, in order to reach its best possible version. Once the parametric model is set up, exploration starts within the family of solutions that inhabit it.

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Konstantinos Poulopoulos 2009

In Grasshopper Parametric Modelling plug-in for Rhinoceros 4.0, the I-->P-->O (Input-Process-Output) definition is not only used as the fundamental idea for the software structure, but it is also visualized as such: input is shown as cables that “wire” data , procedural operations are symbolized as boxes; finally, cables also outsource data to the next suitable operation box. Some input is externally decided as a parameter, by use of the “slider” obect. In case of wrong kind of input, the user is notified: the process box becomes red.

In the case of the roof, the parent object is the surface, and the children objects are the structure that occurs. Change in the surface geometry will be followed by change in the structure’s topology. Parametrization of elements, e.g. the tubes’ density and thickness, enriches the control over uncertainty during design development. 1. Surface decides structure’s topology

2. Control points decide surface topology 3. Parameter controls structure’s density

4. Parameter controls tubes’ diameter as a function of span.

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Konstantinos Poulopoulos 2009

VI_6.

Explicit design

A very important property of prametric CAAD software is their ability to record the design process. In that way, the stages that brought the design to a certain stage are visible and accessible to the designer.

Real-time oportunity of revisiting the design process is new to architectural design; there is usually little knowledge amongst the design team on why a design has come to point A and not point B. It is often forgotten at which point of the process the crucial decisions were made. It is therefore difficult to trace backwards to the crucial moment and examine other alternatives . Moreover, due do the implicitness and the opacity of the design process, its is problematic for any designer to follow the process of another designer. Therefore, monitoring the process in history-sensitive software creates a document of the design process itself, which can be very useful to any designer to revisit, edit and enrich, or simply enter the design problem and continue someone elseâ&#x20AC;&#x2122;s work. Last but not least, recording design history formalizes and creates a Knowledge Base, that remains in the design organisation, rather than within the neurons of expert designersâ&#x20AC;&#x2122; minds.

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Konstantinos Poulopoulos 2009

VI_7. Computer Aided Engineering (CAE) Artificial Intelligence (AI) strives to support and enhance Human Intelligence at all scales of activity. The greatest challenge for AI designers is the building of systems that substitute expert human cognition. Such sustems are known as Expert Systems, or Knowledge-Based Systems. According to the Japanese Technology Evaluation Center (JTEC) , Expert Systems “...are computer programs that are derived from a branch of computer science research called Artificial Intelligence (AI). AI’s scientific goal is to understand intelligence by building computer programs that 9: bibliography note no. 2

exhibit intelligent behavior.” 11 Expert systems are comprised of two fundamental parts: the Knowledge Base and the Reasoning, or Inference, Engine. The former is equivalent to the experience that an expert has accumulated over the yars .The latter is equivalent to the reasoning processes of the human mind. Expert systems are more and more integrated in the AEC (Architecture, Engineering and Construction) Industry. The purpose for this integration is to predict a design’s behaviour from accurate simulation and consequently, to guide descision-making. Computer Aided Engineering is commonly performed for issues such as: dynamic structural performance, aerodynamic behaviour, thermal behaviour, structural members optimization, etc. Especially in structural engineering, Expert Systems, such as Pro/Engineer, SAP 2000, etc are able to simulate the dynamic behaviour of a structure, identify problems, and produce variable solutions according to different combinations of input data out of their Knowledge Base. The Knowledge Base of such an expert system, for example, includes all the industrial standard profiles of steel beams.

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Konstantinos Poulopoulos 2009

VI_7.1. Up-front

CAE in concept phase: CASE STUDY

The roof structure that is examined here as a case study, has been experience

designed through a playful procedure, with the intention of an innovative design that adresses architectural and structural issues

simulation evaluation decision-making

simultaneouly. In such a complex and unusual approach, it is !

obvious that experience cannot play a significant role at the design. Rather, it is through simulation that the designer can be helped to evaluate the feasibility of a design and to direct the following steps of the research process. In this case, Finite Elements Analyis (FEA) helps the design team evaluate the project at an early conceptual stage, and establish that

shear forces diagram

the concept design is, indeed, a good working scenario. Moreover, the preliminary Computer Aided Engineering Analysis indicates how the original geometry can be optimized; such early knowledge can be proved valuable, since it might mean significant savings in materials at the construction phase. Up-front engineering helps designers transfer a projectâ&#x20AC;&#x2122;s value

axial forces diagram

early in the process, increasing knowledge and guiding decisionmaking.

moment diagram

stiffness diagram

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Konstantinos Poulopoulos 2009

VI_7.2. Evaluation

of Case Studyâ&#x20AC;&#x2122;s CAE analysis

a. The diagrams above demonstrate that the original concept geometry is a good departure point for such a design; moreover, they propose directions that will optimize the master geometry: b. The axial forces diagram demonstrates an even flow of forces towards the foundation. c. The stiffness diagram is showing exaggerated distortions on the members, which migh mean that a denser grid is necessary. d. The 4D video animations of modal deformation demonstrate that the distortion during earthquake motions at the cantilevers is unacceptable; therefore, the circular support rings must be modified (probably enlarged and axially distorted towards the furthest cantilever corner).

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Konstantinos Poulopoulos 2009

VI_8.

Computer Aided Manufacturing (CAM)

Computer Aided Manufacturing was originally introduced in the aviation and automotive industries, in order to ensure precision in the fabrication process of complex parts that need to be assembled with very low tolerances. In AEC (Architecture, Engineering and Construction) Industry, CAM has been very helpful at the production of bulding parts of various sizes and for different purposes. Digital

Manufacturing

(CAM)

possible

through

the

interconnection of CAD software with Computer Numerically Controlled (CNC) hardware. CAM machinery widely vary at scale and method of manufacturing. Therefore, CAM can be used for both real size production of building parts, as well as for the production of simulations of building parts for evaluation. The latter, Rapid Prototyping, will be further examined here. VI_8.1. Rapid

Prototyping

The production of models during the development of a design is called Rapid Prototyping. The name directly refers to this techniqueâ&#x20AC;&#x2122;s major advantage; the speed of production. Rapid Prototyping is possible through various methods, such as, subtractive: subtracting material from an original volume; additive: adding layers of metrial incrementally, or 3d printing : 3D powderspraying, that is a technology similar to 2D inkjet printing. During concept and production design , it is important that information is quickly and easily extracted from the virtual environment into the physical world, for real-life evaluation. Simulations are commonly ran on physical models , e.g. in the wind tunnel, to evaluate the wind stress on a tall building, or to calculate the aerodunamic performance of an automobile.

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VI_8.2. Rapid

Prototyping in the CASE STUDY

In the case study of the roof, CAM assists in producing a quick and accurate model of the imagined design. a. The first step towards this direction is to decide the size, scale and material of the model, which will subsequently decide the manufacturing technique to be used. In this case, the model will be made from 3mm MDF flat sheet., Here, a simple laser-cutter is adequate in rapidly and accurately fabricating the flat parts that will then be manually assembled. b. Knowing the manufacturing technology and materials, the next step is the production of the suitable information , that will be embedded on the material to produce the building parts of the model. 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 A B C D E F G H I J K

A B C D E F G H I J K

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Data are extracted from the Master Model by parallel sections on the Master Geometry at directions x and y at the desired itervals. c. The data file (in .dxf format) is then sent over to the manufacturing workshop over the internet. In this case production time was approximately 8 hours. d. Fabrication is done overnight. The parts were prepared on Friday, 30.01.2009, and were on the designerâ&#x20AC;&#x2122;s desk by Saturday 31.01.2009 at 4.11 a.m. The cost of the model is 17,010 Yen. 16:11

e. Assembly started right away; by 18.00, the model was finished. 16:47

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17.02

17.34

18.05

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Konstantinos Poulopoulos 2009

EPILOGUE

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Konstantinos Poulopoulos 2009

CHAPTER VII Conclusion

VII_1. Review

This thesis started by proposing that Digital Design Tools transform profoundly our ways of understanding architectural design; not only do they help designers express their ideas better, faster, and easier: they constitute a new platform of thought, where simulation leads the way to innovation. Contrary to that, experience-driven design (SEBD) strives, under risk, to prove right a pre-determined design. To show that, first it was necessary to establish a theory of design as a problem-solving algorithm. It was shown that design is an iterative process that progresses by gradually embedding value on temporary outputs, until an inclusive, satisfactory solution is reached. Inherent process risks where then identified; it was discused how slow production of information (infogenesis) radically compresses the purely creative time: as a consequence, much of the problem solving has to be left to experienced leadership, which, in turn, compromises the possibilities of innovative designs(1). Moreover, design is understood widely as recycling of existing knowledge, that will generally fail at producing new knowledge(2). The central proposition of this investigation is to show that acceleration of infogenesis is capable of inverting these two consequences: it can promote innovation in design and seize opportunities for capturing and implementing new knowledge. To show that, this investigration resorted to the domain of digital computation, where cognition and calculations are performed at a speed that exceeds human capacity.

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A brief presentation of Digital Tools (CAAD/CAM/CAE) was then unfolded; procedural design, parametric design, up-front engineering, explicit design and rapid prototyping techniques were examined through a case study of a roof structure.

VII_2. Conclusions

on the Case Study

The case study of a 60x20 m. roof structure was performed in order to illustrate the various advantages that stem from the use of Digital Tools on Architectural Design. VII_2.1 Procedural

conclusion 1

Design

Design exploration that is performed through the use of software operations, is a playful, open process that is more likely to lead to innovative designs than traditional experience-based design.

conclusion 2

The importance of a start-up vision is reduced; the result depends mostly on the procedural mutations of a reasonably suitable departure point.

VII_2.2. Parametric

conclusion 3

Design

Parametrisation of a design allows for exploration within a locus of solutions that is dictated by the hierarchical structure designâ&#x20AC;&#x2122;s components. Therefore, design development and optimisation is easy: documentation for each alternative is extracted from the master model. Each parametric model contains a family of solutions, that can be made presentable quickly and efficiently.

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VII_2.3. Explicit

conclusion 4

Design

Computation not only represents states, but also assists in the description of processes. This description is recorded and kept as a Knowledge Data Base, that is easily accessed, shared, applied and updated. Such a library of Explicit Knowledge increases the effectiveness, hence the competitiveness, of a design organisation. VII_2.4. Up-front Computer

conclusion 5

Aided Engineering (CAE)

CAE simulates the performance of a design. Done proactively at the early stages, it may exclude erroneous scenarios and propose directions that have more chance to become functional.

conclusion 6

In case of innovative designs, knowledge cannot serve engineers; effective simulation is more likely to steer the process safely.

conclusion 7

Computer Aided Manufacturing of Rapid Prototypes during concept design produces evaluation material of quality with speed and precision. The quality of the models during the process ensure the quality of the outcome.

conclusion 8

Investment in CAM technologies for Rapid Prototyping seems reasonable; for the case study model, approximately 20 working hours and 17,000 Y were consumed for digital data preparation, manufacturing and assembly. The model was prepared within a day. If manually manufactured, it would need approximately 40 hours of work. Assuming that a student of architecture would perform the task at 1000Y/h, it would cost 40,000Y, materials not included.

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VII_3. Thesis

Conclusion

1. This thesis discussed that CAD/CAM/CAE systems, integrated with human design expertise, create the augmented designer and the interdisciplinary design network. 2. Within that framework, infogenesis is significantly accelerated in comparison with traditional Skill-and Experience Based Systems. 3. This is possible by use of procedural design, parametric design, up-front concept engineering and rapid prototyping techniques, that simulate the behavior of designs and assist in their effective evaluation. Successive iterations of simulation and evaluation increase knowledge on the design rapidly; safer conclusions can be reached earlier in the design process. CAD/CAM/CAE systems transfer value at early stages; that presents opportunities for more refined or innovative designs, since time is invested in exploration.

4. Digital tools produce design documentation as a consequence. they free designers form the burden of production; again, time is invested in exploration. 5. Exploration-driven design is more likely to yield innovation. Digital Media transform design to â&#x20AC;&#x153;serious playâ&#x20AC;?, which is open, and curious. 6. Curiosity is more likely to produce more knowledge on specific designs, but also on the design space itself. Therefore, Digital Computation in Design is allowing design to be viewed as a research activity, that has a dual purpose: production of artifacts and production of knowledge on producing artifacts.

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VII_4. Vision

Digital Tools in design proliferate an era of human creation that regresses to the paradigm of Mother Nature. Biodiversity teaches us that Nature has no preference as to what species should survive or not; She shows no preference to animals with wings over sea creatures; no tendency to promote insects over mammals. Nature does seem to follow any particular pattern for generating Life. Rather, the contrary seems to be true: Nature seems to play; She seems to try several things out and see how they will work. If we might say so, Nature seems to enjoy its morphogenetic power and gooes about exploring Her own boundaries. Nature’s Workshop is the World. Within it, life forms emerge as syntheses of local conditions; these “outputs” are then left to interact; it is the general system and the interactions within it that will eventually “decide” which ones are the most successful designs, and which ones will go redundant. In the case of Nature, there is no need for testing; the World is the playground where evaluation is practiced in-situ. On the contrary, Man needs simulation; simulation is the interpretation of a existing or desired situation by use of a model. The model is what stands between the vagueness of an idea and the sheer complexity of the Real World. Being deprived of strong tools for simulation, man designed so far in a goal-oriented, deterministic fashion; Digital Computation seems to change Design towards an explorative process of probabilities; designs and their variations are launched into the Virtual Space and their behaviour is observed; then, the most likely to survive, are being advanced. Hereafter, Man seems to return to Nature’s Way of generating designs as if they were “life forms”, through trial-and-error, playfulness and curiosity._

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BIBLIOGRAPHY

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22. Robinson, Herbert S., Carrillo, Patricia M., Anumba, Chimay J. and AlGhassani, Ahmed M. Knowledge Management for Continuous Improvement in Project Organisations, Knowledge Management Support. 1999 23. Austin, Simon, Newton, Andrew, Steele, John and Waskett, Paul. Modelling and managing project complexity. International Journal of Project Management 20 (2002) 191-198 24. Lawson, B., Bassanino, M., Phiri, M. and Worthington, J. Intentions, practices and aspirations: Understanding learning in design. Elsevier, Design Studies (2003) 327-339 25 . Purcell, P. Computing and Architecture: a fresh conspectus. ComputerAided Design, vol.10, nr. 4. July 1978 26. Reinhardt, Forest L., Yao, Dennis A. and Egawa, Masako. Toyota Motor Corporation: Launching Prius. Harvard Business School, 9-706-458, December 7, 2006 27. Boyle, J.M. , Interactive engineering systems design: a study for artificial intelligence applications. Artificial Intelligence in Engineering, Vol.4, No.2, 1989 28. Rosenthal , Charles W. Computer Aids for the System Design Process, Elsevier, Computers in Industry 14 (1990) 51-57

WEB-PAGES 29. http://www.toyota.com 30. Knowledge-Based Systems in Japan: http://www.wtec.org/loyola/kb/ toc.htm 31. Physics-Based Simulation Games : http://www.freewebarcade.com/ game/top-figures/ 32. Word Definitions from http://www.dictionary.com

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