Infrastructure Modeling: Status and Applications

  • R. J. Leclaire
  • D. Pasqualini
  • J. S. Dreicer
  • G. L. Toole
  • N. M. Urban
  • R. W. Bent
  • T. N. Mcpherson
  • N. W. Hengartner
Conference paper
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)


Protecting the Nation’s infrastructure from intentional attacks and natural disasters, including extreme weather events and climate change, is a major national security concern that has only become more critical since the terrorist attacks on September 11, 2001 (This chapter focuses on the work performed at LANL concerning the protection of the critical infrastructures of the United States (the ‘Nation’); however the modeling concepts discussed here are generally applicable). Understanding potential weaknesses of infrastructure assets and how interdependencies across critical infrastructure affect their behavior is essential to predicting and mitigating single and cascading failures, as well as to planning for response and recovery and future infrastructure development. Modeling and simulation (M&S) is an indispensable part of characterizing this complex system of systems and anticipating its response to disruptions. With the advent of more sophisticated infrastructure M&S capabilities, the possible applications have expanded to include the security challenges faced by the U.S. military, which relies on sustainable energy resources and needs to address environmental challenges and husband its water resources. Another key area where infrastructure modeling can play a critical role is in addressing global warming concerns given changes in available technology, evolution of the energy mix toward renewable resources, and many other infrastructure-related factors.

Los Alamos National Laboratory (LANL), a U.S. Department of Energy research laboratory tasked with national and energy security concerns, is at the forefront in the development of sophisticated infrastructure M&S capabilities and provides timely analysis of natural and manmade challenges to the infrastructure. This chapter explores the use of infrastructure models by presenting a representative cross- section of the models developed at LANL and some of the analyses completed with them.


Emergency Medical Service Fragility Curve Latin Hypercube Sampling Critical Infrastructure Expected Utility Maximization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bush BW et al (2005) Critical Infrastructure Protection Decision Support System (CIPDSS) Project Overview. Presented at the 2005 international system dynamics conference, LA-UR-05-1870, Mar 2005Google Scholar
  2. 2.
    Bush BW, Cleland TJ, Lauer LJ, Thompson DR (2007) CIPDSS conductor tool, version 1.2.12, LA-CC-07-00045, Aug 2007Google Scholar
  3. 3.
    O’Reilly GP, Jrad A, Kelic A, LeClaire RJ (2007) Telecom critical infrastructure simulations: discrete event simulation vs. dynamic simulation how do they compare, LA-UR-07-1746. Presented at the IEEE globecom 2007 exposition, July 2007Google Scholar
  4. 4.
    LeClaire RJ (2004) Information and telecommunications sector Scenario and modeling (U), Presented at the CIPDSS Program Review for U.S. Department of Homeland Security, LA-CP-04-0143, Feb 2004Google Scholar
  5. 5.
    Conrad SH, LeClaire RJ, O’Reilly GP, et al (2006) Critical national infrastructure reliability modeling and analysis. Bell Labs Tech J 11(3):57–71, published by Wiley Periodicals, doi:10.1002/bltj.20178, LA-UR-06-2520Google Scholar
  6. 6.
    LeClaire RJ (2009) Consequences, uncertainty and risk to critical infrastructures from a dam disruption, LA-UR-09-02182. Presented at the 2009 LANL risk conference, Apr 2009Google Scholar
  7. 7.
    LeClaire RJ, Alde D, Dauelsberg LR, Samsa M, Horschel D (2008) Water system physical disruption: CIPDSS infrastructure dependencies and consequences, LA-UR-08-02748, Apr 2008Google Scholar
  8. 8.
    Murray JD (1993) Mathematical biology, Springer, New York; NCHS (National Center for Health Statistics), 2005, Health, United States, 2005 With Chartbook on Trends in the Health of Americans, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics (hereinafter referred to as Murray, 1993)Google Scholar
  9. 9.
    Fair JM, LeClaire RJ, Wilson ML, Turk AL, DeLand SM, Powell DR, Klare PC, Ewers M, Dauelsberg L, Izraelevitz D (2007) An integrated simulation of pandemic influenza evolution, mitigation and infrastructure response. In: 2007 IEEE conference on technologies for homeland security: enhancing critical infrastructure dependability, 16–17 May 2007Google Scholar
  10. 10.
    Powell DR, Fair JM, LeClaire RJ, Moore L (2006) Sensitivity analysis of an infectious disease model. In: International conference of the system dynamics society, Boston, July 2006Google Scholar
  11. 11.
    Klare PC, Powell DR (2004) Metropolitan CIP/DSS public health sector model, Los Alamos National Laboratory, LA-UR-04-5726, Nov 2004Google Scholar
  12. 12.
    LeClaire RJ, Pasqualini D, Bandlow A, Ewers M, Fair JM, Hirsch GB (2007) A prototype desktop simulator for infrastructure protection: an application to decision support for controlling infectious disease outbreaks. In: Presented at the 25th international conference of the system dynamics society, LA-UR-07-2001, Mar 2007Google Scholar
  13. 13.
    Ambrosiano JJ, Bent RW, Edwards BK (2009) HCSIM: an agent model for urban scale healthcare facility impact. Presented at the 2009 risk symposium, LA-UR-09-02103, Apr 2009Google Scholar
  14. 14.
    Galli EE, Eidenbenz S, Miniszewski SM, Cuellar L, Ewers M, Teuscher C, ActivitySim: large scale agent-based activity generation for infrastructure simulation, LA-UR-09-00705, Feb 2009Google Scholar
  15. 15.
    McPherson TN, Burian SJ (2005) The Water Infrastructure Simulation Environment (WISE) Project. Presented for the EWRI’05 World Water & Environmental Resources Congress, LA-UR-05-1639, Mar 2005Google Scholar
  16. 16.
    Powell DR, LeClaire RJ et al (2007) Critical infrastructure decision support system chemical threat capabilities case study: summary report, LA-UR-07-2382, Mar 2007Google Scholar
  17. 17.
    Powell D, LeClaire R, Dauelsberg L, Klare P, Outkin A, Ivey A (2005) Analysis of the impact of hurricane Katrina on baton rouge, Los Angeles, 20 Sept 2005Google Scholar
  18. 18.
    Berscheid P et al (2006) CIPDSS phase IV architecture and analysis process status report, LA-UR-06-0538, Jan 2006Google Scholar
  19. 19.
    LeClaire RJ, Powell DR, Zagonel A (2009) Tools and techniques for modular programming in vensim, LA-UR-09-04760. Workshop presented at the 27th international conference of the system dynamics society, July 2009Google Scholar
  20. 20.
    Powell DR, Haffenden R, LeClaire RJ (2010) Selected survey of infrastructure disruption events and consequences, presented at the workshop on grand challenges in modeling, simulation, and analysis for homeland security, 17 March 2010Google Scholar
  21. 21.
    LeClaire RJ, Hirsch GB, Bandlow A (2009) Learning Environment Simulator (LES): a tool for local decision makers and first responders, LA-UR-09-01792. Presented at the 27th international conference of the system dynamics society, July 2009Google Scholar
  22. 22.
    Unal C (2002) Interdependence Energy Infrastructure Simulation System – IEISS, LA-UR-02-3307, June 2002Google Scholar
  23. 23.
    Bent RW, Holland JV et al (2011) Interdependent Energy Infrastructure Simulation System (IEISS) technical reference manual, LA-UR-11-06522, Nov 2011Google Scholar
  24. 24.
    Bush B, Holland J, McCown A, Visarraga D, Salazar D, Giguere P, Linger S, Unal C, Werley KA (2003) Interdependent Energy Infrastructure Simulation System (IEISS) user manual, version 1.0, LA-UR-03-1319, Feb 2003Google Scholar
  25. 25.
    Fair Jeanne M et al (2012) Measuring the uncertainties of pandemic influenza. Int J Risk Assessment Manage 16(1/2/3)Google Scholar
  26. 26.
    McKay MD et al (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239–245Google Scholar
  27. 27.
    LeClaire RJ, Powell DR, Ames A, Corbet T (2009) CEMSA execution plan version 1.1, LA-UR-09-00487, Jan 2009Google Scholar
  28. 28.
    LeClaire RJ, Powell DR, Haffenden RA, Belasich DK (2009) CEMSA event historical survey, LA-UR-09-481, Jan 2009Google Scholar
  29. 29.
    LeClaire RJ (2009) CEMS simulation methodology version 1.0, LA-UR-09-00485, Jan 2009Google Scholar
  30. 30.
    LeClaire RJ, Bent RW (2009) Interoperable architecture development for critical infrastructure protection modeling and simulation, LA-UR-09-02197, LANL Risk Conference 2009Google Scholar
  31. 31.
    LeClaire RJ (2009) Operational requirements document for CEMSA, LA-UR-09-00483, Jan 2009Google Scholar
  32. 32.
    Jenkins W (2003) Challenges in achieving interoperable communications for first responders. GAO-04-231 T, 6 Nov 2003Google Scholar
  33. 33.
    Water System Physical Disruption (2008) CIPDSS infrastructure dependencies and consequences. Los Alamos National Laboratory, Argonne National Laboratory, and Sandia National Laboratory, LA-UR-08-2748, Apr 2008Google Scholar
  34. 34.
    US Department of Energy (2012) Climate change and infrastructure, urban systems, and vulnerabilities (Review draft). In: Technical Input to the US National Climate Assessment, 29 Feb 2012Google Scholar
  35. 35.
    Fernandez SJ, Thayer GR, Bush BW, Toole GL, Dauelsberg L, Flaim S, Ivey A (2006) Predicting hurricane impacts on the nation’s infrastructure: lessons learned from the 2005 hurricane season. Presented at the second international conference on global warming and the next ice age, LA-UR-06-4972, Jul 2006Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • R. J. Leclaire
    • 1
  • D. Pasqualini
    • 1
  • J. S. Dreicer
    • 2
  • G. L. Toole
    • 3
  • N. M. Urban
    • 4
  • R. W. Bent
    • 5
  • T. N. Mcpherson
    • 5
  • N. W. Hengartner
    • 6
  1. 1.Energy and Infrastructure Analysis Group, Los Alamos National LaboratoryLos AlamosUSA
  2. 2.Science, Technology and Engineering directorateLos Alamos National LaboratoryLos AlamosUSA
  3. 3.Information Sciences GroupLos Alamos National LaboratoryLos AlamosUSA
  4. 4.Computational Physics and Methods GroupLos Alamos National LaboratoryLos AlamosUSA
  5. 5.Energy and infrastructures groupLos Alamos National LaboratoryLos AlamosUSA
  6. 6.Theoretical Biology and biophysics groupLos Alamos National LaboratoryLos AlamosUSA

Personalised recommendations