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Northwest Alliance for Computational Science & Engineering − Oregon State University

How Computer Science Is Revolutionizing Earthquake Engineering

Cherri PancakeNACSE (Oregon State University)

[email protected]

Northwest Alliance for Computational Science & Engineering

Re-Engineering Earthquake Engineering

George E. Brown Jr. Network for Earthquake Engineering Simulation

NSF funding 4 years contruction + 10 years operationExtend national capacity for EE through new facilities

Unique large-scale physical experimentationIntegration of numerical and physical experiments

Create an IT infrastructure thatCaptures and preserves all relevant informationEnables remote participation in real-timeFacilitates re-use of knowledge gained from experiments

Enhance effectiveness of EE researchers through advanced IT

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Advanced IT Will Let NEES Researchers …

Control and observe experiments from remote sitesReduce requirement for on-site presence

Gain more from experimentsExploit technology to enhance human observation

Share experiments with colleagues/studentsBroaden participation in experimentsExtend useful lifetime of experimental processes

Exploit corpus of experiment resultsFacilitate re-use of previous experimentationSupport integration of computational and experimental modeling


NEES: Distributed Resources and Users

NEESConsortium

ExperimentalData Repositories

ComputationalData Repositories

Unique LaboratoryFacilitiesEquipment

Site 1

EquipmentSite 2

EquipmentSite 3

EquipmentSite 15

. . .

OtherSite A

OtherSite B

Practitioners

EmergencyCommunities

K-14Education

UserCommunities

Earth.Eng.Researchers

NEESgrid

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NEES Poses Unique Challenges for IT

DiversityMany types – and scales – of resourcesPrimary (direct) users• Consortium staff and facility managers• Researchers using experimental/computational facilities

Secondary (indirect) users• Other researchers and students• Practitioners• K-12 and general public

DispersalResources distributed nationallyUsers distributed (inter)nationally


User Prioritization of IT Needs

Experimental data are reliably captured and stored for future use

IT functionality can be accessed and used with no special trainingEarthquake researchers can find and apply experimental data, easily and accuratelyProject-oriented documentation and discussion tools allow earthquake researchers to plan, conduct, and analyze experiments collaboratively

Clear “Winner”

High priority

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How Users Prioritized Needs

Researchers participate in experiments from remote locationsExperiments can span experimental facilitiesSimulation output reliably captured and storedSimulation coupled with experimentationSimulations executed through a shared submission facilityShared tools available for analyzing/mining dataShared tools available for visualizing dataNon-researchers use images/analyses/data for educational and professional purposes


What NEES IT Must Deliver

An infrastructure offering:Reliability – no data will be lostUsability – no special expertise or software will be needed to access informationMinimal effort – fast and efficient access to experiments / collaboration mechanisms / dataFlexibility – data archive will provide search/access features to support• Experiment reuse• Comparison/integration of experiments with numerical

simulation

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Special Need for Reliability

Experiments must be captured as they happenVery expensiveCan be destructive in nature

Lead researcher must have “instant feedback”Results of one run often used to adjust parameters for next

In future, will need to feed real-time data back to simulation


Telepresence: The Raw Ingredients

Sensor data: raw, filtered, graphical summaries10s to 100s of devices operating concurrentlyEventually must scale to 1000s of devices

Data streams from remotely operable cameras and microphones

10s of devices at eye level, suspended from roof, and underwaterIncludes some ultra high-resolution images

Use of computation to merge/analyze real-time data streams

Eventually will use simulation to “play what-if” and change course of experiment

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Without attention to usability ...

Usability essentials:Seamless synchronization of data streams“Intelligent” choice of what to displayNo requirement for user to download softwareFirst experience “pays off” for later ones


Engineering the User Experience

Steering and observing a remote experimentResearcher(s) sets up and directs experiment in near-real-timeColleagues from same/other institutions participateObserve/assimilate/discuss varying sets of data streams

Data repositoryarchives all aspects of NEES experiments

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Data Reuse: The Raw Ingredients

Extremely large quantities of data must be archived and made publicly available

Storage requirements for video/images will dwarf others Diverse data formats must be integrated

Synchronization markers must be addedMost instruments have no concept of “time”

Metadata will be critical ingredientNeed to be standardized, but no appropriate standards existMust rely on EE researchers for much input

Must be possible to compare experimental data with data from simulations


Engineering the User Experience

After-the-fact experiment replayResearcher(s) observe experiment in simulated timeView/analyze subsets of data streams for targeted uses

Single or collaborative researchers search and explore the data repository

So duplication can be eliminatedSo models can be calibratedSo model results can be validated

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NEES IT Is Largely a “Human Problem”

“Cast of thousands” (varying disciplinary backgrounds, levels of expertise, durations of involvement)

Massive DataComplex Data

Extreme Scales

Extreme Diversity

Many are new tofacilities sharingremote collaboration

Most are new todata sharingdata standardizationdata preservationcollaboration tools

Virtually all are new to metadata


How NEES Will Change EE Research

CaveatsExamples are from tsunami facilityNothing is cast in stone yet

Examples from the Tsunami Wave BasinExperiment setupData modelingMetadata definitionExperiment replayAccess Control

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Example 1: Experiment Design

Researchers shouldn’t have to visit site just to get familiar with itKey needs

Learn about facility layout, capabilitiesSee details about equipment and instrumentsPlan layout of models/specimens and instrumentsPlan locations of camerasLearn about setups that worked well for other researchers

Tsunami Wave Basin approachCreate accurate virtual model of labAnimated walkthroughs for newcomersVirtual lab tool for experiment design


Example 1: Experiment Design

Virtual wave basin is “better than being there”See future facilitiesFly above scene

“Try out” positionsStudy setups from past tests

Being developed by undergrad Chris Moore and Dr. Metoyer

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Example 2: Data Modeling

Team from UIUC used classic approachAbstract, obrject-oriented model

Model for creating specifications (not a specification)Cannot be implemented directly

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Usability Considerations

Too hard for engineers to understandPIs will have trouble entering the needed metadataUsers will have trouble understanding how to query database

Doesn’t map well to existing community standards• E.g., tsunami observation standard

Doesn’t interface well to available tools

Goals of revising model for usabilityEasy for both users and developers to understandSupported by commercial and 3rd-party tools• E.g., report generation tools, database systems, data-

mining tools


Resource IIResource II

Model Revised for Usability

Each configuration is used for 1+ runs

RunRunRunRun ARunRunRunRunRunRun ARunRunRun A

The lab is a collection of resources

Resource I …

ResourceOutput

Each resource produces output for the run

Each configuration uses a set of resources

ResourceConfigurationResource-

ConfigurationResourceConfiguration

Experiment

An experiment is made up of configurations

…Configuration 3Configuration 2

Configuration 1

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Revised ModelExperiments for a project

Expriment at a facility

Experiment has configuration

Multiple test runs for a configuration

Projects publishers and authors

Ack for a project

Roles

Persons project roles

Persons experiment roles

Persons roles

Resource assigned to a config

Configuration of a resource

Type of resourceCalibration for configured resource

Coefficient for calibration

Output from a configured resource

Run has output from resources

Raw data for resource output

Processed output (from raw data)

Project pubs and reports

Calibration for unconfigured resource

Experiment

Project

Facility

Configuration

Run

AuthorsPublishers

AcknowledgementsPersonRole

Role

person

ResourceConfiguration Resource

ResourceTypeCalibrationSet

CalibrationCoefficient

ResourceOutput

RawData

ProcessedOutput

PublicationsReports

Processed data (from processed data)


How the Model Is Used

Doesn’t just serve as the basis for database designUsed to integrate all tools and interfaces

“Experiment Notebook” style interfaceMetadata definition toolsSearch-and-query interfaceReport generation toolSame terms will be used in training materials for experimenters

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Example 3: Metadata Definition

Metadata is critical to the NEES concepts of sharing and reuseKey issues

Who defines the format?Who creates the metadata for experiments?What are the incentives for providing high-quality metadata?

Tsunami Wave Basin approachMinimize the amount of metadata users must enterEnsure that user is only entering metadata relevant to his/her roleProvide simple-to-use tools that catch as many potential errors as possible


Where TWB Metadata Will Come FromResourceConfigurationconfigurationIDresourceIDx0y0z0x1y1z1redgreenbluepatternlabelactiveangleconfigurationFileURIspecialConfigurationnotesacquisitionDeviceacquisitionChannel

<pk><pk>

Metadata about sensor settings for a particular configuration

position of sensor

sensor calibrations

sensor setup

generated automatically

entered by lab tech

entered by PI

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Carrot-and-Stick Approach

StickUsers will be required to enter key metadata before experiment can beginPart of agreement with PIs

CarrotMetadata will be used to automatically generate lab reportsPreviously, this was tediously done by hand

We’re asking users to type minimal info ahead of time, rather than typing more after-the-fact


Example of Automatic Lab Report

Tsunami Analysis: Experimental and Numerical Modeling of Forces on a Vertical Surface

Generated by a Solitary Wave

byKarl T. Miller

Project Supervisor:Daehyun Yoon

Professor of Civil Engineering

Supported by:National Science Foundation Award No. CMS-51234321

andGeorge E. Brown, Jr., Network for Earthquake Engineering Simulation (NEES)

Tsunami Wave Basin, Oregon State UniversityNational Science Foundation Award No. CMS-0145332

B. Golden Center for Tsunami ResearchDivision of Engineering and Applied Science

University of WhateveritisHollywood, California 80124

Report No. KM-R-95

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Example 4: Experiment Replay

Long-term archiving of experimental data is central to NEESKey needs

Ability to understand what experimenter was trying to doAccess to all details of instrumentation and experiment designAbility to accurately “replay” what happenedAbility to search data using fuzzy criteria, e.g.• Experiments with “similar results”• Experiments “like this one”

Tsunami Wave Basin approachSearch-and-query tool that “understands” experimentsLab notebook style tool for viewing dataEasy downloading


Example 4: Experiment Replay

Lab notebook provides access to everything (even operator comments)

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Example 5: Access Control

Sharing of data is central to NEESKey issues

Researchers need to be first to publishTradeoffs between privacy and shared useTradeoffs between open access and “safe” use of data

Tsunami Wave Basin approachAccess control variesMore data becomes open over time



Some degree of access guaranteed for all NEES experiments

Read high-level descriptive metadataView video from “lab-sweep” camerasView low-resolution video from experiment camerasView selected, partial-resolution data streamsView lab “chat” channel

PI controls who has access to other information, e.g.View full-/partial-resolution data or camera outputControl camerasRead or write in lab notebookWrite to lab “chat” channelAssign privileges to other users (delegation)

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Simple interface for PIs


Conclusions

First-ever IT infrastructure is central to NEES conceptKey challenges have to do with the human element

Diverse usersBroad range of skill sets and familiarity with advanced ITBroad spectrum of needsExtremely high expectations (!)

Ultimate success/failure rides on whether users can access data in ways that are

“Natural”UsefulSafe

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Tsunami Wave Basin softwarebeing developed by undergrads,

grads, and research staff at NACSE

www.nacse.org/nees/

Who Deserves the Credit

CS Faculty/ResearchersRon MetoyerJon HerlockerSally HaererKen FerschweilerTim HoltJoe HanusJason McKerrLeanne Lai

CS GradsDae YoonAnton DragunovSeikyung Jung

CS UndergradsChris MooreMichiko TakedaTammy CulterKami Vaniea

ECETodd Shechter

CCEEHarry YehDan CoxSolomon YimDave StandleyTerry Dibble

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