Groningen Twister - PowerPoint PPT Presentation

About This Presentation
Title:

Groningen Twister

Description:

semi-underground, for parking 3000 bicycles. public square on top, ... Areas without cellar: No columns necessary. 6/22/09. fabian.scheurer - caad:arch:ethz ... – PowerPoint PPT presentation

Number of Views:91
Avg rating:3.0/5.0
Slides: 16
Provided by: fabians6
Category:

less

Transcript and Presenter's Notes

Title: Groningen Twister


1
Groningen Twister
  • Fabian ScheurerFederal Institute of Technology
    Zurich

2
The project
  • Stadsbalkon Groningen
  • Public square in Groningen by the train station
  • Two layers
  • semi-underground, for parking 3000 bicycles
  • public square on top, only accessible by
    pedestrians
  • No level surfaces in the design
  • Miscellaneous incisions and holes
  • Architecture KCAP (Kees Christianse), Rotterdam
  • Engineering Arup, Amsterdam

3
Demands for supporting structure
  • The engineers
  • Regular grid of columns!
  • 30 cm column diameter!
  • 6 m column spacing!
  • The architects
  • A bit of freedom!
  • Varying column profiles!
  • Varying column spacing!
  • The design
  • Holes and incisions in the pedestria level limit
    column placement!
  • No level surfaces all columns are somehow
    tilted!
  • Bicycle- and pedestrian paths not to be
    obstructed!

4
Brain-twisting task
  • Place an adequate number of columns...
  • with 3 different profiles, and therefore...
  • with 3 different load bearing capacities, and...
  • with a maximum tilt of 10 from the vertical
  • So that...
  • the number of columns in minimized
  • the bearing load of the pedestrian level is
    evenly distributed
  • the bicycle- and walkways are not obstructed

5
Idea Make the columns mobile!
  • Physical model of the mobile column
  • Two masses at the top and bottom end
  • Moveable within the respective planes defined by
    floor and ceiling
  • Linked by an elastic column
  • Top mass surrounded by a bumper to keep
    appropriate distance to other columns
  • Let them get organised by themselves!

6
Programming Java3D
  • Java 3D API
  • On MS Windows, SGI and Sun
  • Test simple gravity-simulation
  • 100 columns
  • Attracting each other
  • Good performance
  • OK, we can handle it.

7
First implementation
  • Three different column types
  • 9 parameters for each type
  • Bicycle stands to attract columns

8
Additional constraints
  • Outline of pedestrian level
  • boundary for column tops
  • Holes in pedestrian level
  • not allowed for column tops
  • Bikestands and roundabout
  • attractors for colum-bases
  • Areas without cellar
  • No columns necessary

9
Current version
  • 3 column types
  • 9 parameters each
  • 3 attractor types
  • Columns (among themselves)
  • Bike stands
  • Slab (with holes and walls)
  • 7-8 parameters each
  • Preset viewpoints
  • Export functions
  • Graphic (SVG)
  • Text (table of column pos.)

10
Up to now rapid prototyping
  • Straight forward programming
  • Not very elaborate class model
  • Extension (new constraints) is difficult
  • Brute force computing
  • No optimization in collision detection
  • Necessary computing time grows quadratic with
    number of columns
  • Limits are reached quickly

11
New constraints
  • Translucent panels
  • Linear bearings
  • Expansion joints

12
To do optimization
  • Introduce lean and consistent class model
  • Easy extension of existing software
  • construction kit for similar problems
  • Optimize algorithms
  • Fast update of neighborhood-relations
  • Faster Collision detection
  • Reach min. 5 frames per second on an average
    workstation

13
To do new concepts
  • Columns grow (change type 1-2-3)
  • if there is enough space
  • Columns split
  • If they cant grow any further (type 3)
  • Columns shrink
  • If they lack space
  • Columns die
  • If they cant shrink any further (type 1)

14
A colony of columns
  • Growth within habitat
  • Define different areas in the habitat which
    enable/restrict growth
  • Columns grow to appropriate size
  • Genetic programming
  • Encode all column parameters into virtual DNA
  • Define fitness criteria
  • Plant 100 genetically different columns
  • Select fittest DNA of this generation
  • Mate with 100 random columns
  • Start next generation

15
Thank you for your attention!
  • Fabian Scheurer
  • T 41 (0)1 633 40 25
  • F 41 (0)1 633 10 50
  • scheurer_at_arch.ethz.ch
  • Federal Institute of Technology Zurich
  • Professorship for CAAD
  • Prof. Ludger Hovestadt
  • ETH Hönggerberg HIL E.15
  • CH-8093 Zürich
Write a Comment
User Comments (0)
About PowerShow.com