Advertisement

Severity Measures and Stripe Analysis for Probasbilistic Structural Fire Engineering

  • Mayank Shrivastava
  • Anthony K. Abu
  • Rajesh P. Dhakal
  • Peter J. Moss
Article
  • 88 Downloads

Abstract

This paper presents modifications to the adoption of a Performance-Based Earthquake Engineering (PBEE) framework in Probabilistic Structural Fire Engineering. Potential Fire Severity Measures, which capture significant characteristics of fire scenarios, are investigated. A suitable Fire Severity Measure (FSM), which best relates fire hazard intensity with structural response, is identified by satisfying efficiency and sufficiency criteria as described by the PBEE framework. The study also implements a new analysis method called Fire Stripe Analysis (FSA) to obtain the relationship between FSM and the structural response. In order to obtain the annual rate of exceedance of damage and repair cost/time for an office building, an occurrence model and an attenuation model for office structure fires are generated for both Christchurch city and New Zealand. The process is demonstrated with the help of a case study performed for a steel–concrete composite beam. Structural response is recorded for the beam exposed to several fire profiles which are generated by varying fuel loads from 200 MJ/m2 to 1000 MJ/m2 and ventilation factors from 0.02 m1/2 to 0.08 m1/2. FSA and dispersion curves of structural response are plotted for every fire severity measure. Cumulative incident radiation is found to be the most efficient and sufficient FSM. The mean annual rate of exceedance of given levels of fire severity and structural response are evaluated for both New Zealand and Christchurch city. It is found that Christchurch city has a 15% less probability of exceedance of the given fire severity level in comparison to the whole of New Zealand. The extension of this work would facilitate designers/insurers to evaluate the probability of damage or failure of a structure due to a probable fire hazard.

Keywords

Probabilistic engineering Structural fire Exceedance probability Fire severity measure 

Notes

Acknowledgements

Financial support to the first author by the University of Canterbury International Doctoral Scholarship is greatly appreciated. The authors would also like to thank Dr Trevor Yeow for reviewing and providing his comments which helped to improve the quality of the paper.

References

  1. 1.
    Hadjisophocleous GV, Benichou N, Tamim AS (1998) Literature review of performance-based fire codes and design environment. J Fire Prot Eng 9(1):12–40.  https://doi.org/10.1177/104239159800900102 CrossRefGoogle Scholar
  2. 2.
    Gibson E (1982) Working with the performance approach in building. CIB Report Publication, RotterdamGoogle Scholar
  3. 3.
    Cornell CA, Krawinkler H (2000) Progress and challenges in seismic performance assessment. PEER Center News 3(2):1–3Google Scholar
  4. 4.
    Deierlein G, Krawinkler H, Cornell C (2003) A framework for performance-based earthquake engineering. In: Pacific conference on earthquake engineering, 2003. Citeseer, pp 1–8Google Scholar
  5. 5.
    Hopkin D (2017) A review of fire resistance expectations for high-rise UK apartment buildings. Fire Technol 53(1):87–106CrossRefGoogle Scholar
  6. 6.
    De Sanctis G, Fischer K, Kohler J, Fontana M, Faber M (2011) A probabilistic framework for generic fire risk assessment and risk-based decision making in buildings. In: 11th International conference on application of statistics and probability in civil engineering, ETH Zurich, Switzerland. CRC PressGoogle Scholar
  7. 7.
    Guo Q, Jeffers AE (2014) Finite-element reliability analysis of structures subjected to fire. J Struct Eng 141(4):04014129CrossRefGoogle Scholar
  8. 8.
    Guo Q, Jeffers AE (2013) Stochastic finite element methods for the reliability-based fire-resistant design of structures. In: 3rd international conference on applications of structural fire engineering, pp 96–101Google Scholar
  9. 9.
    Guo Q, Shi K, Jia Z, Jeffers AE (2013) Probabilistic evaluation of structural fire resistance. Fire Technol 49(3):793–811CrossRefGoogle Scholar
  10. 10.
    Shi K, Guo Q, Jeffers A (2013) Stochastic analysis of structures in fire by monte carlo simulation. J Struct Fire Eng 4(1):37–46CrossRefGoogle Scholar
  11. 11.
    Balogh T, Vigh LG (2016) Complex and comprehensive method for reliability calculation of structures under fire exposure. Fire Saf J 86:41–52CrossRefGoogle Scholar
  12. 12.
    Kirby B, Newman G, Butterworth N, Pagan J, English C (2004) A new approach to specifying fire resistance periods. Struct Eng 82(19):34–37Google Scholar
  13. 13.
    Law A, Stern-Gottfried J, Butterworth N (2015) A risk based framework for time equivalence and fire resistance. Fire Technol 51(4):771–784CrossRefGoogle Scholar
  14. 14.
    Deierlein G, Krawinkler H, Cornell C (2003) A framework for performance-based earthquake engineering. Paper presented at the Pacific conference on earthquake engineeringGoogle Scholar
  15. 15.
    Moehle J, Deierlein GG (2004) A framework methodology for performance-based earthquake engineering. In: 13th world conference on earthquake engineering, pp 3812–3814Google Scholar
  16. 16.
    Porter KA (2003) An overview of PEER’s performance-based earthquake engineering methodology. In: Ninth International Conference on Applications of Statistics and Probability in Civil Engineering, San Francisco, California, USAGoogle Scholar
  17. 17.
    Devaney S (2014) Development of software for reliability based design of steel framed structures in fire. Ph.D. Dissertation, University of EdinburghGoogle Scholar
  18. 18.
    Hamilton SR (2011) Performance-based fire engineering for steel framed structures: a probabilistic methodology. Ph.D. Dissertation, Stranford UniversityGoogle Scholar
  19. 19.
    Lange D, Devaney S, Usmani A (2014) An application of the PEER performance based earthquake engineering framework to structures in fire. Eng Struct 66:100–115. http://dx.doi.org/10.1016/j.engstruct.2014.01.052 CrossRefGoogle Scholar
  20. 20.
    Moss PJ, Abu AK, Dhakal RP (2014) Incremental fire analysis (IFA) for probabilistic fire risk assessment. In: 23rd Australasian conference on the mechanics of structures and materials (ACMSM23), Byron Bay, NSW, Australia, pp 707–712Google Scholar
  21. 21.
    Rini D, Lamont S (2008) Performance based structural fire engineering for modern building design. In: Structures congress 2008: crossing borders, pp 1–12Google Scholar
  22. 22.
    Shrivastava M, Abu A, Dhakal R, Moss P (2016) Efficiency of different intensity measures for probabilistic fire engineering. In: 24th Australasian conference on mechanics of structures and materials, Perth, Australia, 2016. CRC Press, pp 957–962Google Scholar
  23. 23.
    Dhakal RP, Mander JB (2006) Financial risk assessment methodology for natural hazards. Bull New Zealand Soc Earthquake Eng 39(2):91–105Google Scholar
  24. 24.
    Shome N, Cornell C (1999) Probabilistic seismic demand analysis of nonlinear structures, Reliability of Marine Structures Program. Report No RMSGoogle Scholar
  25. 25.
    Luco N, Cornell CA (2007) Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions. Earthquake Spectra 23(2):357–392CrossRefGoogle Scholar
  26. 26.
    Tothong P, Luco N (2007) Probabilistic seismic demand analysis using advanced ground motion intensity measures. Earthquake Eng Struct Dynam 36(13):1837–1860CrossRefGoogle Scholar
  27. 27.
    Giovenale P, Cornell CA, Esteva L (2004) Comparing the adequacy of alternative ground motion intensity measures for the estimation of structural responses. Earthquake Eng Struct Dynam 33(8):951–979CrossRefGoogle Scholar
  28. 28.
    Tothong P, Cornell C (2007) Probabilistic seismic demand analysis using advanced ground motion intensity measures, attenuation relationships, and near-fault effects. PEER Report 2006/11, Pacific Earthquake Engineering Research Center. University of California, Berkeley, California, USACrossRefGoogle Scholar
  29. 29.
    Kramer SL, Mitchell RA (2006) Ground motion intensity measures for liquefaction hazard evaluation. Earthquake Spectra 22(2):413–438CrossRefGoogle Scholar
  30. 30.
    Vamvatsikos D, Cornell CA (2002) Incremental dynamic analysis. Earthquake Eng Struct Dynam 31(3):491–514CrossRefGoogle Scholar
  31. 31.
    Baker JW (2007) Probabilistic structural response assessment using vector‐valued intensity measures. Earthquake Eng Struct Dynam 36(13):1861–1883CrossRefGoogle Scholar
  32. 32.
    Bazzurro P, Cornell CA, Shome N, Carballo JE (1998) Three proposals for characterizing MDOF nonlinear seismic response. J Struct Eng 124(11):1281–1289CrossRefGoogle Scholar
  33. 33.
    Jalayer F, Cornell C (2009) Alternative non‐linear demand estimation methods for probability‐based seismic assessments. Earthquake Eng Struct Dynam 38(8):951–972CrossRefGoogle Scholar
  34. 34.
    CEN (2002) Eurocode 1: actions on structures part 1-2: general actions–actions on structures exposed to fire. BrusselsGoogle Scholar
  35. 35.
    Nyman JF (2002) Equivalent fire resistance ratings of construction elements exposed to realistic firesGoogle Scholar
  36. 36.
    Nyman JF, Gerlich HJ, Wade C, Buchanan AH (2008) Predicting fire resistance performance of drywall construction exposed to parametric design fires—a review. J Fire Prot Eng 18 (2):117–139CrossRefGoogle Scholar
  37. 37.
    CEN (2012) Fire resistance tests—part 1: general requirements, EN 1363-1 European Committee for Standardization. BrusselsGoogle Scholar
  38. 38.
    BSI (1987) Fire tests on building materials and structures, BS 476 (Parts 20 to 23). British Standards Institution, UKGoogle Scholar
  39. 39.
    Standard A (1997) Methods for fire tests on building materials. Components and structures (AS 15304) AustraliaGoogle Scholar
  40. 40.
    ISO I (1999) 834-1: 1999-Fire-resistance tests—elements of building construction–part 1: general requirements. Int Organ StandGoogle Scholar
  41. 41.
    Buchanan AH, Abu AK (2017) Structural design for fire safety. Wiley, New YorkGoogle Scholar
  42. 42.
    Gernay T, Elhami Khorasani N, Garlock M Fragility analysis of a steel building in fire. In: Proceedings of the first international conference on structural safety under fire & blast-CONFAB 2015, Glasgow, Scotland, UK, 2015. ASRANet Ltd, pp 252–261Google Scholar
  43. 43.
    Thomas P (1986) Design guide: structure fire safety CIB W14 workshop report. Fire Saf J 10 (2):77–137CrossRefGoogle Scholar
  44. 44.
    Vrouwenvelder T (1997) The JCSS probabilistic model code. Struct Saf 19 (3):245–251CrossRefGoogle Scholar
  45. 45.
    Stevenson P (1993) Computer modelling of structural steel frames in fire. Fire Engineering Research Report University of Canterbury, Christchurch, New ZealandGoogle Scholar
  46. 46.
    Welsh R (2001) 2-D analysis of composite steel-concrete beams in fire. Fire Engineering Research Report. School of Engineering, University of Canterbury, Christchurch, New ZealandGoogle Scholar
  47. 47.
    Wastney C (2002) Performance of unprotected steel and composite steel frames exposed to fire. M.E.F.E. report, University of CanterburyGoogle Scholar
  48. 48.
    Huang Z, Burgess IW, Plank RJ (2003) Modeling membrane action of concrete slabs in composite buildings in fire. I: theoretical development. J Struct Eng 129 (8):1093–1102CrossRefGoogle Scholar
  49. 49.
    Huang Z, Burgess IW, Plank RJ (2003) Modeling membrane action of concrete slabs in composite buildings in fire II: validations. J Struct Eng 129 (8):1103–1112CrossRefGoogle Scholar
  50. 50.
    NZFS (2013) Emergency Incident Statistics 2012–2013. New Zealand Fire Service, Wellington, New ZealandGoogle Scholar
  51. 51.
    Kam W, Pampanin S (2011) General performance of buildings in Christchurch CDB after the 22 Feb 2011 earthquake: a contextual report (prepared for the Department of Building and Housing). Department of Civil and Natural Resources Engineering, University of CanterburyGoogle Scholar
  52. 52.
    Miles S, Brechwald D, Davidson R, Demeter K, Johnston D, Pampanin S, Wilkinson S (2014) Building back better-case study of the 2010–2011 canterbury. New Zealand Earthquake Sequence (A Learning from Earthquakes Report)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil and Natural Resources EngineeringUniversity of CanterburyChristchurchNew Zealand

Personalised recommendations