Intro computational design_mega2016_1_with_recommendedplugins

A Very Short Introduction to
Computational Design
An outline of essential topics
Ir. Pirouz Nourian
PhD Candidate, Researcher & Instructor
@TU Delft/A+BE/AE+T/Design Informatics
Email: p.nourian@tudelft.nl
Mega (High-rise workshop) 2016

About Me
 PhD Candidate, Researcher & Instructor:
@ AE+T/Design Informatics
@ Urbanism/Urban Design
@ Urbanism/3D Geoinfo
 MSc in Architecture & Urban Planning 2009
 BSc in Control Engineering (EE) 2005
languages
 C#.NET (reader, writer & speaker)
 VB.NET (reader, writer & speaker)
 Python (reader)

areas of expertise and/or interest
 Urban/Architectural Design and Modelling Algorithms
 Computational Design Support Systems
 Spatial Analysis & Spatial Planning
 Computational Geometry, Topology, and Graph Theory
 Computational Analysis, Simulation and Optimization
CONFIGURBANISTSYNTACTIC
Space Syntax For
Architectural Configuration
Network Analysis For
Urban Configuration
For 3D Reconstruction
from Point Clouds
rasterworks.dll
Library of Raster3D &
Voxel Tools
My R&D projects:

(Space Syntax for Generative Design)
 real-time Space Syntax analyses for parametric design
 interactive bubble diagram
 automated graph drawing algorithms
 enumeration of plan configuration topologies
 measuring the socio-spatial performance
www.grasshopper3d.com/group/space-syntax
https://sites.google.com/site/pirouznourian/syntactic-design
SYNTACTIC

 real-time accessibility analysis for walking and cycling modes,
considering topography
 aggregate accessibility analysis of geographic attractions
 polycentric distributions
 metric between-ness analysis
 parametric zoning and cycling network design
www.grasshopper3d.com/group/cheetah
https://sites.google.com/site/pirouznourian/configurbanist
CONFIGURBANIST
(Cheetah)

TOIDAR:
Computational Tools for Interactive 3D
reconstruction of buildings from LIDAR point clouds
 made for (and further developed by) MSc Geomatics students
 point cloud classifications: height, aspect, slope
 point cloud segmentation
 surface and solid reconstruction out of point clouds
Supervisor: Dr. Sisi Zlatanova, Contributors (in alphabetic order)
: Tom Broersen, Jiale (Carl) Chen, Martin Dennemark, Florian Fichtner, Martijn Koopman, Ivo de Liefde, Marco Lam, Maarten
Pronk, Stella Psomadaki, Rusne Sileryte, Dimitris Zervakis, Kaixuan Zhou
https://sites.google.com/site/pirouznourian/toidar

RASTERWORKS_TUDELFT.DLL
 Vector3D to Raster3D Operations & Raster specific queries
 Raster3D to Vector3D Operations (Level-Sets & Iso-Surfaces)
MonetDB:
a geospatial
database to
support 3D
GIS operations
Rhino-GH:
a parametric CAD
environment working as a
computational geometry lab
& visualization environment
ODBC connection
Interface between
MonetDB
&
Grasshopper
RASTERWORKS.DLL
an analytic engine for
voxel/raster 3D operations
NLeSC Geospatial Big Data Analytics

This lecture:
Part 1: terminology, design process & methods
Part 2: course requirements and workshops

This lecture:
Part 1: terminology, design process & methods
• Intellectual Design Process
• What is computational design?
• Computation
• Parametric Modelling
• Computational Analysis
• Performance Simulation
• Performance Evaluation
• Design Space Exploration
Part 2: course requirements and workshops
• What is expected
• Study materials
• Agenda

Common Misconceptions!
• we can automate the design process!
• parametric design is another architectural style!
• parametric design= grasshopper!
• computational design is a magic art!
• computational design is for geek guys!

Design Process
• Design is about making things
• Science is about knowing things
• Design is about working on vague problems that have no definitive solution!
• A design is a concrete proposition for an abstract demand.
• Philosophically, there can never be a proof that a design is the best it could ever be!
(Rittel, 1973)
• Formulation is as important as problem-solving. (Simon, 1999)
• Design is a process of co-evolution of problems and solutions (Cross & Dorst 2007),
through analysis, synthesis and evaluation (Lawson, 2005).

Design Process
The process of “thinking” for “making” something based on needs,
intentions, requirements and constraints.
 Cross & Dorst 2007
Lawson 2005 

Why Computational Design?
What is it that we could not do without computation?
How do we design methodically?
1. Create (synthesize) objects systematically: parametric
formulation for analytic optimization, systematic
generation of design alternatives and cataloguing ranges
of alternatives.
2. Measure (analyze/simulate) qualities quantitatively
3. Compare (evaluate) designs objectively based on numeric
descriptions of our comparative quality criteria
How do we design for better (more sustainable if you like)?
But what is good??? And how do we compare actual design alternatives?!
What is it that we could do better with computation?

Computational Design is about:
How to reach at good solutions out of many alternatives
(optimization/control techniques)
• definition of design goals in terms of performance
criteria; and defining a phase-model for the parametric
design process: from schematic design to detailing;
• formulation of ‘design problems’ (parameterization);
• parametric generation of design alternatives (in
collaboration with Architect, Structural Designer,
Designer Building Services, Façade Designer and Project
Manager);
• performance measurements (again in collaboration with
the other team members);
• design optimization (maximization of desired performance
measures)

Hierarchical Decision Making
Synthesis SynthesisSynthesis
Evaluation Evaluation Evaluation
Configuration
Shape
Structure Details
Materials
Construction
Analysis Analysis Analysis
Phase 0:
Design Intent
Phase 1:
Design Development
Phase 2:
Detail Design

What is not computational design!
Using sophisticated software applications does NOT
necessarily mean doing computational design!
• If we start with wrong assumptions at the beginning, a simulation tool cannot
tell us what to do to improve our design!
• Even if we optimize minor things at a late stage of design, the whole
configuration might be extremely ineffective and inefficient due to initial
decisions!
• Most important decisions pertained to configuration and shape are made at
early stages of design process!
Design
(CAD)
Simulate
(FEA)
Label!
(LEED)
Certified—45
points
Silver—60 points
Gold—75 points
Platinum—90 points

What is it? Who is she?

Computation
Computers and computer are causal systems; meaning, programs and computers
DO NOT THINK in the sense that human beings do; they do not have intentions,
motives, anticipation or creativity: they just act as programmed!
Information processing
by means of algorithms
An algorithm is
a technical recipe for doing something
Image courtesy of http://iheartapple.com

Flowcharts
Algorithms are conventionally illustrated as flowcharts
IN OUT
A CAUSAL SYSTEM
Nourian, Rezvani, Sariylidiz, 2013, Space Syntax for Generative Design

Parametric Modeling & Design
• Thinking of parameters instead of numbers!
• Same rationales, many alternatives!
 We could model an actual circle as a particular instance of a generic circle, which is
the locus of points equidistant from a given point as C (center), at a given distance R
(Radius), on a plane p.
 Parametric modeling is essential for formulating design problems
 The same role algebra has had in the progress of mathematics, parametric modeling
will have in systematic (research-oriented) design.
Parametric
Circle
Plane
Radius
Circle

𝑥 = 𝑟𝑐𝑜𝑠(𝑡)
𝑦 = 𝑟𝑠𝑖𝑛 𝑡
𝑡 ∈ [0,2𝜋]
𝑡 =
2𝜋𝑖
𝑛
|𝑖 ∈[1,n]⊂ ℕ
Plane
Radius
Circle

Computational Analysis
Measuring qualities and features of [geometric] objects
• Continuous Models:
Analytic measurements using
mathematical models of objects, e.g.
curvature analysis
• Discrete Models:
Algorithmic analysis of discrete models,
e.g. finding distances on a network
using shortest path algorithms

Performance Simulation
Mimicking physical behavior of objects in real world
How does a ‘system’ behave (affects or gets affected by) in a particular ‘environment’?
For example, how much a certain building will be lit throughout winter in Amsterdam?
Agent Simulation, image courtesy of Space Syntax LtdSolar Gain Estimation

Performance Evaluation
How good a system is behaving/performing?
• Quality criteria
• A quantitative interpretation of performance simulations/estimations
• How to tell if design A is performing better than design B?
• Defining an “objective function”
Solar Gain Estimation and Evaluation: comparing a set of different design alternatives for a courtyard housing block

Design Space Exploration
A) Continuous Changes:
• Feed-Forward using mathematical analysis
• Feed-Back  using meta-heuristic methods such as evolutionary algorithms,
simulated annealing, swarm intelligence, etc.
Parametric
Circle
Radius𝑟 = 𝐴
𝜋
A 100 𝑚2
big circle
Parametric
Circle
Radius circle
Manipulate R
to minimize Δ
Compute
Area

A) Continuous Changes:
• Feed-Forward using mathematical analysis
• Feed-Back  using meta-heuristic methods such as evolutionary
algorithms, simulated annealing, swarm intelligence, etc.
example how to make a circle that is as big
100 𝑚2?

B) Discrete Changes:
Catalogue/Enumerate and Rank  listing all(most important) possibilities

Plugins that we recommend:
Structural Design Computations:
• Kangaroo
• Millipede
• Karamba
• Geometry Gym (export to GSA)

Climatic Design Computations:
• Ladybug
• DIVA

BIM Plugins Syntactic
• Geometry Gym
• GH>>Revit
• Visual ARQ
• Chameleon

Façade Design Computations:
• Mesh Edit (UTO)
• Weaver Bird
• Mesh(+)

Architectural Design Computations:
• Spider Web
• Syntactic

Part 2: Course Requirements
Deliverables and Matters to Consult
• definition of design goals in terms of performance
criteria; and defining a phase-model for the parametric
design process: from schematic design to detailing;
• formulation of ‘design problems’ (parameterization);
• parametric generation of design alternatives (in
collaboration with Architect, Structural Designer,
Designer Building Services, Façade Designer and Project
Manager);
• performance measurements (again in collaboration with
the other team members);
• design optimization (maximization of desired performance
measures)

Formulation Examples
Think about the followings:
• How would you define a concept such as “compactness”?
• How would you measure the aptitude of a building in
terms of solar gain?
• How would you define structural goodness of a building?
• How would you define functional performance of a
building?

Agenda of Workshops
0. An introduction to Computational Design
1. Geometry and Topology
2. Data Structure and Algorithms
3. Performance Analysis, Evaluation & Optimization

Study Materials
Please follow the tutorials before our first workshop!
• http://wiki.bk.tudelft.nl/toi-pedia/Rhino_Introduction
• webinar about Grasshopper:http://vimeo.com/28175502
• I suggest reading this book: (Essential Mathematics for
Computational Design, written by Rajaa Issa):
http://blog.rhino3d.com/2010/03/essential-mathematics-
for-computational.html
Check the list of preparation materials on the Black Board!

Bibliography
Cited in this presentation, on general topics
• Dorst, K., & Cross, N. (2007). 'Co-evolution of Problem and
Solution Spaces in Creative Design'. In J. M. Gero,
Computational Models of Creative Design. Sydney: Key Centre of
Design Computing and Cognition.
• Lawson, B. (1980). How designers think (4th, 2005 ed.).
Burlington: Architectural Press, Elsevier.
• Nourian, P. Rezvani, S., Sariyildiz, S, Designing with Space
Syntax, eCAADe 2013 proceedings, ID-131, Delft, the
Netherlands.
• Rittel, Horst W.j. & Webber, Melvin M. (1973). Dilemmas in a
General Theory of Planning. Policy Sciences, pp 155-169.
• Simon, H. A. (1999). The Sciences of the Artificial. London:
MIT Press.

Food for Thought
Try to guess how a line or a circle is represented in a computer
“If it looks like a duck, swims like a duck, and quacks like a duck, then
it probably is a duck.”
Image: DUCK: GETTY Images; ILLUSTRATION: MARTIN O'NEILL, from
http://www.nature.com/nature/journal/v484/n7395/full/484451a.html?message-global=remove

Food for Thought
The image of a geometry is not the same as its representation
What you see is not what you get
Image: René Magritte, ceci n'est pas une pipe (this is not a pipe)

THANKS
FOR
YOUR
ATTENTION

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