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An Integrated View of NSF and Infrastructure Systems Research FIATECH 2002 Capital Projects Integrated Technology Workshop November 15, 2002

Miriam Heller, Ph.D. Program Director, Infrastructure & Information Systems National Science Foundation mheller@nsf.gov +1.703.292.8360

Topics ¾ Whirl

Wind Introduction to NSF

¾ Learning

from Urban Disasters

¾ Holistic,

Risk-based, Life-Cycle Framework

¾ Q&A

November 15,2002 - M. Heller ©

NSF Mission – To Initiate & Support: ¾ Basic

scientific research and research fundamental to the engineering process.

¾ Programs

to strengthen scientific and engineering research potential.

¾ Science

& engineering education programs at all levels.

¾ Programs

November 15,2002 - M. Heller ©

that inform policy formulation.

NSF Investment Strategies Goals ¾ People - A diverse, internationally competitive and globally-engaged workforce of scientists, engineers and well-prepared citizens. ¾ Ideas - Discovery at and across the frontier of science and engineering, and connections to its use in the service of society. ¾ Tools - Broadly accessible, state-of-the-art information bases and shared research and education tools. Priorities ¾ Math and Science Partnerships ¾ Graduate Fellowships November 15,2002 - M. Heller ©

Priority Areas Budget amounts in Millions of Dollars

Priority Areas Biocomplexity in the Environment Information Technology Research Nanoscale Science and Engineering Learning for the 21st Century Mathematics Social, Behavioral, Economic Sciences

~ FY 2002 $58 $273 $174 $126

Note: in FY02 - NSF will have a Mathematics priority area, in FY03 - NSF plans for a Social, Behavioral, Economic Sciences priority area. November 15,2002 - M. Heller ÂŠ

National Science Foundation National Science Board Rita Coldwell Joe Bordogna

Staff Offices

$277M Polar and Antarctic Programs

$483M Directorate for Biological Sciences

$431M Directorate for Engineering Esin Gulari November 15,2002 - M. Heller ÂŠ

Director - Rita Colwell

$470M Directorate for Computer and Information Science and Engineering

$559M Directorate for Geosciences

Office of the Inspector General

$4,472M Total $872M

Directorate for Education and Human Resources

$864M Directorate for Mathematical and Physical Sciences

$81M Integrative Activities (MRE, STC)

$163M Directorate for Social, Behavioral, and Economic Sciences

Directorate for Engineering ~ FY2002 Budget Assistant Director â&#x20AC;&#x201C; Esin Gulari

$38.5M Bioengineering & Environmental Systems

Design, Manufacture & Industrial Innovation

$431M

$52.2M Civil & Mechanical Systems

$50.2M Chemical & Transport Systems

Electrical & Communications Systems

Engineering Education & Centers

(incl. SBIR)

$125.7M November 15,2002 - M. Heller ÂŠ

$57.1M

$107.5M

Division of Civil & Mechanical Systems The Mission of CMS: Provide a fundamental underpinning for the engineering profession in application to mechanical systems and the constructed environment including infrastructure systems, and Support the rapid development of new technology in service to society and to reduce risks induced by natural, technological, and intentional hazards. November 15,2002 - M. Heller ÂŠ

National Science Foundation Research Modes ¾ Unsolicited

research (single investigator/small groups) ¾ Special initiatives (CAREER, NSF/USDOT, PATH) ¾ Center-based research (ERC) ¾ Industry partnerships (I/UCRC; GOALI) ¾ International collaborations ¾ Information centers ¾ Education projects (research, curriculum development, informal education) ¾ Workshops/U.S. attendance at international meetings

NSF supported organizations include academe, professional and private sectors November 15,2002 - M. Heller ©

Division of Civil and Mechanical Systems CMS is comprised of six programs: Five disciplinary “super” programs, each with two program officers The NEES (Network for Earthquake Engineering Simulation) program, with two assigned program officers

November 15,2002 - M. Heller ©

Physical Scales with Examples in Civil and Mechanical Systems Materials nano (10 -9) m

Components/Machines micro

(10 -6) m

Molecular Scale

Microns

• nano-mechanics • micromechanics • self-assembly • microstructures • MEMS • nanoscale materials design • smart and engineering materials

November 15,2002 - M. Heller ©

Constructed Facilities

Infrastructure Systems

meso

macro

system

mega-system

(10 -3) m

(10 +0) m

(10 +3) m

(10 +6) m

Millimeters

Meters

Kilometers

Regions

• bridges, dams, buildings • mechanical systems • pavement, tunnels and pipelines • site remediation

• transport networks • urban infrastructure • lifeline systems • information highways

• mesomechanics • interfacial structures • composites

• beams • columns • foundations • nonstructural components • pipes

Division of Civil and Mechanical Systems (CMS) Five Research Programs and One Major Research Equipment Program: Solid Mechanics and Materials Engineering Program Directors: Ken Chong (kchong@nsf.gov, on detail FY02), Oscar Dillon (odillon@nsf.gov, for FY02) and Jorn LarsenBasse (jlarsenb@nsf.gov)

Geotechnical and GeoHazards Engineering Program Directors: Clifford Astill (castill@nsf.gov) and Richard Fragaszy (rfragasz@nsf.gov)

Structural Systems and Engineering Program Directors: Peter Chang (pchang@nsf.gov) and P. Balaguru (pbalagur@nsf.gov)

Dynamic System Modeling, Sensing & Control Program Directors: Alison Flatau (aflatau@nsf.gov) and Shi Chi Liu (sliu@nsf.gov)

Infrastructure and Information Systems Program Directors: Miriam Heller (mheller@nsf.gov) and Dennis Wenger (in 11/01)

George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES) Program Directors: Joy Pauschke (jpauschk@nsf.gov) and Tom Anderson (tanderso@nsf.gov)

And CMS Represents NSF as a NEHRP (National Earthquake Hazards Reduction Program) Agency November 15,2002 - M. Heller ÂŠ

www.eng.nsf.gov/nees

Network for Earthquake Engineering Simulation

他 Change focus from physical testing to seamless integration of testing, analysis and simulation 他 Revolutionize the practice of earthquake engineering research with state-of-the-art experimental equipment and information technology

他 Ultimately, enable new $82 million Major Research earthquake hazard Equipment (MRE) project mitigation technologies: structural, geotechnical, and construction 2000-2004 tsunami operation 2005-2014

George E. Brown Network for Earthquake Engineering Simulation (NEES) A $81.8 million revolution in earthquake engineering full-scale testing of complex structural and lifeline systems – network for real-time experiment sharing, access to curated databases and to advanced computational resources and visualization tools through the NEES collaboratory – national and international resource for research and education • expanded and diverse research community • outreach to the engineering profession, industry, policy makers, and the public ¾FY2003 plans – construction at approx. 20 equipment sites (Phase 2 competed in FY02, all sites operational by September 30, 2005) – network prototype demonstrated (completed in FY04) – NEES Consortium formed, proposal for 10-year operation (FY05-FY14) –

November 15,2002 - M. Heller ©

NEES Awards to 6/2002 University of Colorado, Boulder Fast Hybrid Testing Laboratory

University of Minnesota, Twin Cities Multi-Axial Subassemblage Testing System

Oregon State University Tsunami Wave Basin

University of Nevada, Reno Three (two relocatable) Shake Tables

University of Illinois, Urbana-Champaign System Integration

Rensselaer Polytechnic Institute Geotechnical Centrifuge

University of California, Davis Geotechnical Centrifuge State University of New York, University at Buffalo Dual (one relocatable) Shake Tables and High Performance Actuators

University of California, Berkeley Reconfigurable Reaction Wall

Consortium of Universities for Research in Earthquake Engineering NEES Consortium Development University of California, Los Angeles Field Testing Equipment

November 15,2002 - M. Heller ÂŠ

University of Texas, Austin Field Testing Equipment

Learning from Urban Disasters PI Aboulhassan Astaneh-Asl David Bloomquist J. David Frost John R. Harrald Jose HolguinVeras Robert Paaswell George Lee Kathleen Tierney Dennis Mileti Mary Fran Myers Frederick Mowrer Tom Oâ&#x20AC;&#x2122;Rourke Arthur Lembo W. Al Wallace Joe Chow David Mendonca Rae Zimmerman

November 15,2002 - M. Heller ÂŠ

Institution U California Berkeley (collaboration with LLNL) U Florida (working with NOAA) Georgia Tech (collaboration with UIUC) George Washington University CUNY City College

Title World Trade Center Post-Disaster Reconnaissance and Perishable Structural Engineering Data Collection

MCEER at SUNY Buffalo (with U. Delaware) U. Colorado Boulder

Multidisciplinary Center for Earthquake Engineering Research

U Maryland Cornell University RPI (subaward to NJIT) New York University

Infrastructural Damage Assessment Using Land-Based Laser Swath Mapping Technology Digital Data Collection for Damage Assessment at World Trade Center Observing and Documenting the Inter-Organizational Response to the 9/11 Terror Attacks Impacts of Extreme Events on Passenger Travel Behavior

Natural Hazards Research Application and Information Center World Trade Center Post-Disaster Fire Reconnaissance and Perishable Data Collection Improved Security And Management Of Underground Infrastructure Systems: Lessons Learned From September 11, 2001 "Impact of World Trade Center Disaster on Critical Infrastructure Interdependence SGER - Urban Infrastructure Services in a Time of Crisis: Lessons from September 11th

Structures and Fire

Fig. 1. A Beam from WTC-7 that is Burned

Fig. 2. A Column from Towers Appears to be hit with a Round and Fast Moving Object

Abolhassan Astaneh-Asl, University of California Berkeley • Forensic studies of WTC’s mechanical and structural properties • Goal: realistic computer simulation of impact and fire on structures Fred W. Mowrer, University of Maryland Pre-event condition assessment of • Fireproofing • Exit stairs/elevators • Structural stability • Ventilation systems • Automatic sprinklers November 15,2002 - M. Heller ©

Information David Bloomquist, University of Florida • New land-based laser system • Yields high-resolution 3-D "maps" of interior and exterior of damaged buildings • Identifies displacements and cracks for damage appraisal

Digital Camera

Barcode Scanner November 15,2002 - M. Heller ©

Handheld GPS

Digital Voice Recorder

J. David Frost, Georgia Inst. Technology • Structural damage data collection/integration • Handheld technology for earthquakes data collection • Includes GPS, digital camera, handheld computer

Hand-held Digital Data Collection for Damage Assessment David Frost, Georgia Tech Feature ID & Feature Location ID & Location

Building

Number of Floors

Lifeline Infrastructure

Building Classification

Address

Transportation Facility

Earthquake Feature

Dam/Levee

Geotechnical Structure

Retaining Wall

Landfill

Embankment

Building Type/ Description Structure Classification

Failure Type

Geotechnical Failure

Structural Failure

Foundation Type

Damage Scale

Failure Information November 15,2002 - M. Heller ÂŠ

Failure Information

Nonstructural Failure

Failure Information

Dam Info

Retaining Wall Info

Dam Failure Info

Retaining Wall Failure Info

Embankment Info

Landfill Failure Info

Embankment Failure Info

y Services Thomas O’Rourke, Cornell University • Critical infrastructure case studies • Compile damage information on water supply, electric power, gas, steam, wasterwater conveyance, telecommunications, U/G transportation in GIS • Compare with extreme events damage • Generalize vulnerability & resiliency Rae Zimmerman, NYU • Pre-event assessment of transport, energy, telecommunications, water, sewer, solid waste management services • Characterization of service interruptions • Infrastructure service resiliency • Performance frameworks November 15,2002 - M. Heller ©

Emergency Response John Harrald, George Washington University • Extend emergency response knowledge base • Interorganizational coordination of emergency management, medical efforts, law enforcement and military resources ` • Document information flows • Compare to natural disasters

Dennis Mileti, Natural Hazards Center (U Co-Boulder) 17 travel grants awarded for QR, e.g.: • Use of GIS in emergency response (S. Cutter) • Institutional warning response (P. O’Brien) • Victim identification (D. Simpson) • Community response (S. Lowe) • http://www.Colorado.EDU/hazards/qrsept.html • http://www.nyu.edu/icis/Recovery/ November 15,2002 - M. Heller ©

Integrated Risk Management Early Warning / Preparedness Modeling, Simulation, Prediction

Communication / Education

Social/ Cultural Values

Emergency Response

Multi -Hazard Multi-Hazard Multi -Stakeholder Multi-Stakeholder Recovery Decision -Making Decision-Making Financial/ Organizational Insurance Theory Instruments

Sensing & Monitoring

Policy/ Law Hazard Mitigation November 15,2002 - M. Heller ©

Damage Assessment – Internal, Direct Impacts – External, Indirect Impacts

Learning from Everyday Risk WTC : total recovery estimated at over $140 billion

Non-deliberate and everyday risks ¾ FEMA : earthquake costs at $4.4 billion / year ¾ FHWA : $78 billion / year idled away in congestion ¾ EPRI : $1.5 billion for July-Aug 1996 power blackouts ¾ CEIDS : $119 billion / year in power quality disruptions ¾ (ASCE : $1.3 trillion / 5 years for aging infrastructure) November 15,2002 - M. Heller ©

Critical Infrastructures (PDD 63) Deterioration Potable Potable& & Innovation Transportation Transportation Waste WasteWater Water Telecom Telecommunications munications

Natural Threats

Regulation

Banking Banking& & Insurance Insurance

STRESS

Government Government

Electricity Electricity

Deregulation November 15,2002 - M. Heller ÂŠ

Global Global-ization

Oil Oil& &Gas Gas Accidental Threats

Emergency Emergency Response Response

Devol Devol-ution Deliberate Threats

Infrastructure Interdependencies

e-commerce, IT

Trading, transfers

Regulations & enforcement FERC; DOE

Banking & Finance Currency (US Treasury; Federal Reserve )

e-government, IT

Water for cooling, emissions control Pumps, lifts, control systems November 15,2002 - M. Heller ÂŠ

FEMA; DOT

Emergency Response

Fire suppression

Transport of emergency personnel, injured, evacuation

DOT

Transportation

Fuel transport, shipping

Fuels, Heat

Fuels, lubricants

Oil & Natural Gas

Chemicals transport

Water for production, cooling, emissions control

Potable & Waste Water Switches, control systems

Location, EM contact

SCADA

Communications

Fuel transport, shipping Signalization, switches, control systems Generator fuels, lubricants Storage, pumps, control systems, compressors

Personnel/Equipment (Military)

DOE; DOT SCADA

EPA SCADA

Financing, regulations, & enforcement

Detection, 1st responders, repair Medical equipment

Government

Financing & policies

SEC; IRS

Communications

E L E C T R I C I T Y

Financing & policies

Heat Cooling

T E L E C O M

System Risk is a Function of System State P(Ht,s)

= probability of a hazard at time t (and system state s)

P(Ds|Ht,s) = probability of a particular level of vulnerability of a system in state s given a hazard at time t (and system state s) E(L|Ds) = expected losses conditioned on the vulnerability of system in state s E(L)

=Σ

ht,s

November 15,2002 - M. Heller ©

E(L|ds) * P(ds|ht,s) * P(ht,s)

Infrastructure Life-Cycle Management Needs ¾

¾ ¾ ¾

What methods and tools can capture, clarify, and predict the complex behaviors and interdependencies of infrastructure systems? How can maximal efficiency during normal operations be balanced with resiliency, sustainability, and minimal vulnerability to common and catastrophic failures? Which measures of performance adequately capture system(s) complexity? Who are the decision makers and stakeholders, and what are their goals and objectives? How can risk and uncertainty be incorporated into the design and management of infrastructure systems?

November 15,2002 - M. Heller ©

Risk-Based Life-Cycle Infrastructure Engineering & Management Detection, Preventive Maintenance, Lifetime Extension, Early Warning Emergency Response, Planning, Training and Preparedness Diagnosis

Modeling, Simulation, Prediction

Communication / Education

Social/ Cultural Values

Recovery, Corrective Maintenance

Multi -Objective Multi-Objective Multi -stakeholder Multi-stakeholder Decision -Making Decision-Making Financial/ Organizational Insurance Theory Instruments

Policy/ Law Life-Cycle Design

Post-Event Analysis – Internal, Direct Impacts – External, Indirect Impacts – Systems Evaluation

Challenges: Complex Systems & Interdependencies Modeling Needs

Theoretical frameworks for large-scale CAS –

Non-linear coupled subsystems

–

System interdependencies

–

Spatially distributed, adaptive

Centralized, decentralized, distributed control

Multiple agents / DM’s –

Multiple system operational objectives: efficiency, reliability, security, resiliency, sustainability

–

Multiple agencies with different missions, resources, timetables, and agendas

Data security, accessibility, and reliability

Organizational and human errors and failures

Complex Systems & Interdependencies Modeling, Simulation, & Prediction ¾

Advanced computing paradigms: NNs, GAs, Complex (Adaptive) Systems, KD-DM

Visualization, Virtual Reality, Haptics, and other Human Computer Interfaces –

models are not in themselves solutions and the translation of model into usable forms draws on a range of disciplines

Develop or enhance computer simulators to predict system behavior, including – – – – –

Materials and design options Internal monitoring, control, and optimization systems Human and organizational behavior interactions System and interdependent systems behavior Multiple objectives, multiple stages

Challenges: Prediction ¾

Various level of prediction (Pielke, Sr., NCAR) –

Guessing

–

Sensitivity Experiments (don’t include all feedbacks)

–

Realization (include all feedbacks)

–

Projection (envelop)

–

Perfect Foresight (unachievable)

Estimating and modeling risk –

Mean Value, MLE, Order Statistics, Extreme Value Theory, Simulation

–

Component vs. system vs. interdependent systems

–

Multi-objective Risk

–

Integrated environmental, health, ecological, financial, technological

Uncertainty: Bayesian, sensitivity, bounding methods

and consequence assessment

Sensors, Monitoring, Control, Predictive Maintenance and Optimization Research on affordable, real-time sensors: ¾ Operations optimization and control (e.g., real-time, timeof-day, pay-by-use pricing) ¾ Civil infrastructure monitoring for improved prediction, detection, real-time diagnosis, maintenance, robust control –

–

Hypersensitive, embedded, distributed, sensing and control systems (e.g., MEMS/NEMS) self-heal at multiple scales through adaptive material properties, geometry, mechanical or electromagnetic output Smart wireless systems for remote sensing and control (from integrated NEMS, MEMS, and Interdigital Transducers with smart materials and composites) of surface transportation (speed, navigation), civil infrastructures (pipe corrosion, pollution)

Sensors, Monitoring, Control, Predictive Maintenance and Optimization ¾

¾ ¾ ¾

Data Storage: new storage capabilities for abundant sensed data, including multimedia/internet-based systems and domainspecific data architectures Data Transmission: Remote sensing, storage, or processing will require GIS, GPS, and wireless technologies transmission Data Quality: source, reliability, durability, accuracy, uncertainty, security, privacy Optimal Design and Configuration of Sensor System: minimize cost / maximize value of data, energy management, local processing / central control for emergent system behavior, (multi-) scaleability Data Processing: new algorithms for large-scale, real-time data and signal processing of sensed data for monitoring, controlling, and optimization November 15,2002 - M. Heller ©

Planning, Training, Preparedness ¾ (Multi-objective,

Multi-stage) Decision Theory/Optimization under Risk and Uncertainty ¾ Expert systems/simulation ¾ Standards development ¾ Certification ¾ Organization theory ¾ Risk management/communication November 15,2002 - M. Heller ©

Detection, Preventive Maintenance, Lifetime Extension, Early Warning ¾ ¾ ¾ ¾ ¾ ¾ ¾ ¾ ¾

(Real-time) signal processing for Knowledge Discovery-Data Mining Condition Assessment Failure characterization Information flows Group dynamics Organization theory Risk communication Risk perception Psychological biases in decision making

Emergency Response, Diagnosis ¾ Infrastructure

databases ¾ Database on emergency responders and their role ¾ Information flows ¾ Risk management / communication ¾ Group dynamics ¾ Organization theory ¾ Psychological biases in decision making November 15,2002 - M. Heller ©

Recovery, Corrective Maintenance ¾ ¾ ¾

System resiliency modeling Short-term restoration vs long-term recovery Recovery vis-à-vis the rest of cycle: – – – – –

Pre-event maintenance (predictive, preventive) Pre-event recovery planning Mitigation: “window of opportunity” post-event Funding options Federal programs (in search of a policy)

Political science / public decision-making

Post-Event Analysis ¾ ¾ ¾ ¾ ¾

Impact (natural, built, human, social, cultural systems) characterization and inventories Data storage and transmission Impact valuation (e.g., engineering economics, contingent valuation; environmental risk assessment) Multiobjective risk assessment Post-event systems evaluation – – – – –

Sensing, monitoring, control, and optimization systems Modeling and prediction systems Preparedness, detection, early warning Emergency response, diagnosis, repair Renewal, recovery, reconstitution

Life-Cycle Design ¾

Engineering and technological aspects of design – –

¾ ¾ ¾ ¾ ¾

Thermal stresses, impact loading, plasticity, and stability of structural systems as well as fire proofing and blast resistance New (smart) materials and designs to prevent or postpone collapse of building structures

Social and behavioral aspects of design (worker productivity, organizational communication, egress modes) Simulation of structural and users’ response to design Concurrent design methods Risk-based life-cycle assessment Non-structural design hedging mechanisms Multi-objectives, multiple stakeholder DM

Multi-Objective Multi-stakeholder Decision-Making 他

他

Allocation problem over various investment options, over various stages of development (R&D, development, implementation) over time with risk/uncertainty Multiple objectives : efficiency, reliability, security, resiliency, sustainability 1 2

B/C ( S&M)

1 ~ 2 ~ 3 : indifferent wrt ER 1 is infeasible wrt obj. S&M 2 >> 3 : 2 dominates 3

他

B/C (ER) Multiple stakeholders : different missions, resources, timetables, and agendas November 15,2002 - M. Heller 息

Overarching Issues ¾

More than terrorism – – –

Need a portfolio of risk intervention options – –

At different stages of technological develop Over the system life-cycle

Long-view / sustainability – –

Multiple hazards and stressors System of interdependent infrastructure systems Vulnerability is a function of system state

Address security without compromising other objectives Exploit synergistic objectives and collateral benefits

Non-inconsequential issues that must be addressed – – –

Workforce education & training (White House Workshop) Sensors, “Acts of God” versus responsibility Data sensitivity, accessibility, quality

Thank You for Facing the Challenge

November 15,2002 - M. Heller ÂŠ