Shake Table Data Visualization

AMIT CHOURASIAVISUALIZATION SCIENTIST

VISUALIZATION SERVICES

SAN DIEGO SUPERCOMPUTER CENTER

PRESENTED AT : NEES 5TH ANNUAL MEETING, JUN 20TH

SNOWBIRD UTAH

Shake Table Data Visualization

Data Source2

Observed: Sensors (600 channels)

Data AttributesTime varying MultivariateHeterogeneous

Original Footage

Study Goal3

• Integrate the observed data into visual domain

• Asses the requirements and applicability to Structural Engineering

• Demonstrate such a system to Structural Engineers

• Disseminate information about the state-of-the-art research in structural engineering

Motivation: Understanding Data4

Large amount of numbers don’t make much sense to humans

Visual information can encode large amount of numbers to gain insight

Low Bandwidth High Bandwidth

Quest for Visual Representation5

Desirable Attributes

Intuitive (trainable)

Highlights feature of interest

Color: Perceptual or Rainbow style?

Compact

Unambiguous

Dirty Data Issues6

Data comprised of 117 channels 5000+ timesteps @ 50 hz ~ 100 sec

Data Formating and Translation

3D Model7

Movie:

3d Model

Deformation Model8

Movie:

Deformation

Renderings9

Original video Combined Perspective npr

Camera Match10

Match Check Match

Lessons Learned11

• The visual system should include the capability of real-time interaction with deformation of a textured 3D model; incorporate contextual elements when possible; represent sensor locations and properties

• Animation packages can be successfully utilized for scientific visualization. They are flexible and extensible for quick prototyping. The visual results are highly realistic with high fidelity.

• Proper registration of data and metadata is important. Without registration, features like camera matching and compositing can be guesswork at best.

• Matching environmental light is still a challenge.

• The visualized results are valuable tools for dissemination of information and suitable for both a broader scientific and non-scientific community.

Visualization Issues12

• Domain knowledge• Multivariate data representation• Temporal coherence• Precision Loss (compression, etc…)• Interaction vs. batch• Perceptual Issues• Personal Bias (author & viewer)

Video Recap13

Acknowledgements14

Steve Cutchin

Mike Rossmasler (Viz Intern)

Ruben Soto-Alcauter (NEES-IT Intern)

Andrew Collier (NEES-IT Intern)

Jose Restrepo

Marios Panagiotou

Lelli Van Den Einde

Anke Kamrath

Study was funded by SDSC

WEB: HTTP://VISSERVICES.SDSC.EDU/

REFERENCE: CHOURASIA, A. (2007) “DIGITAL RECREATION OF A SEVEN STORY BUILDING SHAKE DURING AN EARTHQUAKE”, ACM CROSSROADS, 13-3, 2007

15

Thanks for your patience

? -