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A Probabilistic Methodology for Assessing Post-Earthquake Fire Ignition Vulnerability in Residential Buildings

  • Pegah FarshadmaneshEmail author
  • Jamshid Mohammadi
Article
  • 27 Downloads

Abstract

Post-earthquake fire (PEF) ignition events constitute a secondary consequence of an earthquake and may result in the loss of life and substantial property damage, especially in urban areas where the potential for fire spread and conflagration exists. These secondary hazards can cause severe structural and non-structural damage, potentially more significant damage than the direct damage caused by the initial earthquake, and can lead to catastrophic structural failures, devastating economic losses, and casualties. To manage the impact of PEF in urban areas, it is important to identify the potential ignition sources and quantify the vulnerabilities of these ignition sources as a result of earthquake-induced structural damage. The results of such analyses can be used to offer resiliency improvement investments and mitigation strategies in urban areas located in seismically active regions. Most of the previous PEF studies are data-driven, utilizing ignitions reported following recent earthquakes. However, in areas for which historical PEF data are not available, such as the Midwestern United States, a different methodology for developing a PEF model is needed. This paper describes an analytical model for quantifying the vulnerabilities of residential buildings to PEF by estimating the failure of ignition sources upon a probable seismic event. The underlying concept in developing the method is that (1) ignition sources in residential buildings remain unchanged before and after an earthquake, and (2) the total probability of PEF occurrence can be estimated by adjusting the probabilistic fire occurrence data for normal conditions (everyday operation of ignition sources) to account for the effect of the earthquake. This paper’s contribution to state of the art is in developing a new framework for estimating the probability of PEF for areas in which historical PEF data is unavailable. The developed framework uses the likelihood of ignition occurrence during normal condition as a baseline; this baseline is then adjusted using certain key parameters to capture spatial characteristics, ignitability, and potential seismic intensity of the study area to estimate the probability of PEF as a function of projected earthquake characteristics. The model was tested for St. Louis City as a populated area with potential future earthquake hazard because of its proximity to the New Madrid Fault zone. Using the National Fire Incident Reporting System dataset, the frequency of normal condition ignitions was determined as 1.97E−03 ignition per unit per year. Using the proposed PEF model considering PEFs caused by damage to drift and acceleration sensitive equipment and human actions, the projected frequency of PEF was estimated between 2.79E−06 and 2.81E−06 ignitions per household per year. Using this model, and the average number of households between 2010 to 2015, 175,854 households, it was estimated that in the next 50 years, approximately 25 households would experience fires related to probable earthquake events in St. Louis City.

Keywords

Post-earthquake fire Vulnerability assessment Probabilistic modeling Ignition sources 

Notes

Acknowledgements

The assistance provided by the National Fire Incident Reporting System (NFIRS) staff, specifically Ms. Kathleen Carter, for sharing NFIRS Public Data Release Files, which formed the basis for this research, is greatly appreciated.

References

  1. 1.
    Anderson D (2014) Statistical models of post-earthquake ignitions based on data from the Tohoku, Japan earthquake and tsunami. University of DelawareGoogle Scholar
  2. 2.
    Trifunac M, Todorovska M (1997) Northridge, California, earthquake of 1994: density of red-tagged buildings versus peak horizontal velocity and intensity of shaking. Soil Dyn Earthq Eng 16(3):209–222CrossRefGoogle Scholar
  3. 3.
    Kobayashi M (1985) Urban post-earthquake fires in Japan. In: Proceedings, US-Japan workshop on urban earthquake hazards reduction, pp 85–83Google Scholar
  4. 4.
    Scawthorn C, O’Rourke T, Blackburn F (2006) The 1906 San Francisco earthquake and fire—Enduring lessons for fire protection and water supply. Earthq Spectra 22:135–158CrossRefGoogle Scholar
  5. 5.
    Mohammadi J, Alyasin S, Bak D (1992) Investigation of cause and effects of fires following the Loma Prieta earthquake. Illinois Institute of Technology, Department of Civil Engineering, Armour College of Engineering, ChicagoGoogle Scholar
  6. 6.
    Mousavi S, Bagchi A, Kodur VK (2008) Review of post-earthquake fire hazard to building structures. Can J Civ Eng 35 (7):689–698CrossRefGoogle Scholar
  7. 7.
    Wellington Lifelines Group (2002) Fire Following Earthquake: Identifying Key Issues for New Zealand, Report on a Project Undertaken for the New Zealand Fire Service Contestable Research Fund. Wellington Lifelines Group, Wellington, New ZealandGoogle Scholar
  8. 8.
    Baker GB, Collier PC, Abu AK, Houston B (2012) Post-earthquake structural design for fire-a New Zealand perspective. In: Paper presented at the 7th International Conference on Structures in Fire, Zurich, Switzerland.Google Scholar
  9. 9.
    Lee SW, Davidson RA (2010) Physics-based simulation model of post-earthquake fire spread. J Earthq Eng 14 (5):670–687CrossRefGoogle Scholar
  10. 10.
    Himoto K, Yamada M, Nishino T (2014) Analysis of ignitions following 2011 Tohoku earthquake using Kawasumi model. Fire Saf Sci 11:704–717CrossRefGoogle Scholar
  11. 11.
    Khorasani NE, Gernay T, Garlock M (2017) Data-driven probabilistic post-earthquake fire ignition model for a community. Fire Saf J 94:33–44CrossRefGoogle Scholar
  12. 12.
    Zolfaghari M, Peyghaleh E, Nasirzadeh G (2009) Fire following earthquake, intra-structure ignition modeling. J Fire Sci 27 (1):45–79CrossRefGoogle Scholar
  13. 13.
    Kelly EJ, Tell RN Modeling the number of ignitions following an earthquake: Developing prediction limits for overdispersed count data. In: Energy Department Natural Phenomena Hazards (NPH) Workshop, Maryland, Tech. Rep. LA-UR-11-01857, 2011.Google Scholar
  14. 14.
    Davidson R (2009) Generalized linear (mixed) models of postearthquake fire ignitions. MCEER Tech Rep 09:4Google Scholar
  15. 15.
    Li S, Davidson R (2013) Application of an urban fire simulation model. Earthq Spectra 29(4):1369–1389CrossRefGoogle Scholar
  16. 16.
    Lee SW, Davidson RA (2010) Application of a physics-based simulation model to examine post-earthquake fire spread. J Earthq Eng 14(5):688–705CrossRefGoogle Scholar
  17. 17.
    Li S, Davidson RA (2013) Parametric study of urban fire spread using an urban fire simulation model with fire department suppression. Fire Saf J 61:217–225CrossRefGoogle Scholar
  18. 18.
    Farshadmanesh P, Mohammadi J, Modares M (2016) Further Development in Predicting Post-Earthquake Fire Ignition Hazard. W Acad Sci, Eng Technol, Int J Civil, Environ, Struct, Constr Archit Eng 10(6):681–685Google Scholar
  19. 19.
    Yildiz SS, Karaman H (2012) Developing a physics-based model for post-earthquake ignition. In: Proceedings of the 9th International ISCRAM Conference Vancouver, CanadaGoogle Scholar
  20. 20.
    Gao C, Liu J (2017) Network-based modeling for characterizing human collective behaviors during extreme events. IEEE Trans Syst, Man, Cybern: Syst 47(1):171–183CrossRefGoogle Scholar
  21. 21.
    Bernardini G, D’Orazio M, Quagliarini E (2016) Towards a “behavioural design” approach for seismic risk reduction strategies of buildings and their environment. Saf Sci 86:273–294CrossRefGoogle Scholar
  22. 22.
    Jeong J-J, Park K-W, Mizuno M, Ohmiya Y, Ikeda K (2017) Analysis of combustion expansion and heat release rate during combustion of mattress installed at different heights. In: Fire Science and Technology 2015. Springer, pp 409–418Google Scholar
  23. 23.
    Farshadmanesh P (2017) New directions in post-earthquake fire hazard analysis with applications to Midwestern United States. Illinois Institute of Technology, ChicagoGoogle Scholar
  24. 24.
    Mohammadi J, Alaysin S, Bak D (1992) Analysis of post-earthquake fire hazard. In: Proc. 10th World Conf. on Earthquake Engineering, pp 5983–5988Google Scholar
  25. 25.
    Lee S, Davidson R, Ohnishi N, Scawthorn C (2008) Fire following earthquake—reviewing the state-of-the-art of modeling. Earthq Spectra 24(4):933–967CrossRefGoogle Scholar
  26. 26.
    Scawthorn C (2009) Enhancements in HAZUS-MH, Fire following earthquake task 3: updated ignition equation. PBS&J and the National Institute of Building SciencesGoogle Scholar
  27. 27.
    Yildiz S, Karaman H (2013) Post-earthquake ignition vulnerability assessment of Küçükçekmece District. Nat Hazards Earth Syst Sci 13(12):3357–3368CrossRefGoogle Scholar
  28. 28.
    Williamson RB, Groner R (2000) Ignition of fires following earthquakes associated with natural gas and electric distribution systems. Pacific Earthquake Engineering Research Center, University of CaliforniaGoogle Scholar
  29. 29.
    Ren A, Xie X (2004) The simulation of post-earthquake fire-prone area based on GIS. J Fire Sci 22(5):421–439CrossRefGoogle Scholar
  30. 30.
    Elhami Khorasani N, Gernay T, Garlock M (2015) Tools for measuring a City’s resilience in a fire following earthquake scenario. In: Proceedings of IABSE conference-structural engineering: providing solutions to global challenges, pp 886–889Google Scholar
  31. 31.
    Lu X, Zeng X, Xu Z, Guan H (2017) Physics-based simulation and high-fidelity visualization of fire following earthquake considering building seismic damage. J Earthq Eng 1–21Google Scholar
  32. 32.
    Environmental Systems Research Institute (2011) ESRI ArcGIS 10.2.1 for Desktop. CA, RedlandsGoogle Scholar
  33. 33.
    U.S. Geological Survey (2016) USGS ShakeMap Archives. https://earthquake.usgs.gov/data/shakemap/. Accessed Aug 2016
  34. 34.
    NHGIS (2011) National Historical Geographic Information System, MN, Minneapolis. https://www.nhgis.org/. Accessed Aug 2016
  35. 35.
    U.S. Fire Administration-National Fire Data Center (2015) National Fire Incident Reporting System (NFIRS). https://www.nfirs.fema.gov/. Accessed Jan 2016
  36. 36.
    Sandberg M (2004) Statistical determination of ignition frequency. Lund Institute of TechnologyGoogle Scholar
  37. 37.
    Johansen P (1979) Early models describing the fire insurance risk. ASTIN Bull J IAA 10(3):330–334CrossRefGoogle Scholar
  38. 38.
    Tillander K (2004) Utilisation of statistics to assess fire risks in buildings. VTT Technical Research Centre of Finland,Google Scholar
  39. 39.
    Ramachandran G (1980) Statistical methods in risk evaluation. Fire Saf J 2(2):125–145CrossRefGoogle Scholar
  40. 40.
    Cousins W, Smith W (2004) Estimated losses due to post-earthquake fire in three New Zealand cities. In: Proceedings, New Zealand Society of Earthquake Engineering ConferenceGoogle Scholar
  41. 41.
    Zhao S, Xiong L, Ren A (2006) A spatial–temporal stochastic simulation of fire outbreaks following earthquake based on GIS. J Fire Sci 24(4):313–339CrossRefGoogle Scholar
  42. 42.
    USGS (2018) Earthquake Facts and Statistics United States Geological Survey. Accessed Retrieved August 14, 2018Google Scholar
  43. 43.
    Ohta Y, Omote S (1977) An investigation into human psychology and behavior during an earthquake. In: Proc. 6th World Conf. Earthq. Engr, pp 347–352Google Scholar
  44. 44.
    Goltz JD, Bourque LB (2017) Earthquakes and human behavior: a sociological perspective. Int J Disaster Risk Reduct 21:251–265CrossRefGoogle Scholar
  45. 45.
    HAZUS99 User’s Manual: Earthquake loss estimation methodology (1999). Federal Emergency Management Agency, Washington D.C.Google Scholar
  46. 46.
    Reinoso E, Jaimes MA, Esteva L (2010) Seismic vulnerability of an inventory of overturning objects. J Earthq Eng 14(7):1008–1021CrossRefGoogle Scholar
  47. 47.
    Guidelines for earthquake bracing of residential water heater (2004). Department of general services division of the state architectGoogle Scholar
  48. 48.
    Survey Reveals Inadequate Water Heater Seismic Bracing (n.d.). The Golden Gate Chapter, American Society of Home Inspectors, Inc. http://jmcinspections.com/wp-content/uploads/2014/12/Water-Heater-Seismic-Braces.pdf. Accessed Aug 2016
  49. 49.
    Residential Energy Consumption Survey (RECS) (1997) 1997 RECS Survey Data Independent Statistics & Analysis, U.S. Energy Information AdministrationGoogle Scholar
  50. 50.
    American Society of Civil Engineers (ASCE)/Structural Engineering Institute(SEI) 7-10 (2010) Minimum design loads for building and other structuresGoogle Scholar
  51. 51.
    American Society of Mechanical Engineers Guide for verification and validation in computational solid mechanics. In: 2006. ASMEGoogle Scholar
  52. 52.
    Lasdon LS, Fox RL, Ratner MW (1974) Nonlinear optimization using the generalized reduced gradient method. Revue française d’automatique, informatique, recherche opérationnelle Recherche opérationnelle 8(V3):73–103MathSciNetCrossRefGoogle Scholar
  53. 53.
    U.S. Geological Survey (2014) Introduction to the National Seismic Hazard Maps. https://earthquake.usgs.gov/hazards/learn/. Accessed Feb 2017
  54. 54.
    Fire Departments by County Missouri Association of Fire Chiefs. http://www.mochiefs.org/Documents/2010%20Fire%20Departments%20by%20county.pdf. Accessed Feb 2017
  55. 55.
    United States Census Bureau ACS Demographic and Housing Estimates 2011–2015 American Community Survey 5-Year Estimates. https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF. Accessed Aug 2016
  56. 56.
    United States Geological Survey Hazard Curve Application. http://geohazards.usgs.gov/hazardtool/application.php. Accessed Aug 2016
  57. 57.
    UBC Code (1997) Uniform building code. In International Conference of Building Officials. Whittier, CAGoogle Scholar
  58. 58.
    Residential Energy Consumption Survey (RECS) (2009) RECS Survey DataGoogle Scholar
  59. 59.
    Österbring M, Mata É, Thuvander L, Mangold M, Johnsson F, Wallbaum H (2016) A differentiated description of building-stocks for a georeferenced urban bottom-up building-stock model. Energy Build 120:78–84CrossRefGoogle Scholar
  60. 60.
    Spielman SE, Singleton A (2015) Studying neighborhoods using uncertain data from the American community survey: a contextual approach. Ann Assoc Am Geogr 105(5):1003–1025CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Nuclear, Plasma, and Radiological EngineeringUniversity of Illinois at Urbana-ChampaignChampaignUSA
  2. 2.Department of Civil, Architectural, and Environmental EngineeringIllinois Institute of TechnologyChicagoUSA

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