Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A resilience-based method for prioritizing post-event building inspections


Despite the wide range of possible scenarios in the aftermath of a disruptive event, each community can make choices to improve its resilience, or its ability to bounce back. A resilient community is one that has prepared for, and can thus absorb, recover from, and adapt to the disruptive event. One important aspect of the recovery phase is assessing the extent of the damage in the built environment through post-event building inspections. In this paper, we develop and demonstrate a resilience-based methodology intended to support rapid post-event decision making about inspection priorities with limited information. The method uses the basic characteristics of the building stock in a community (floor area, number of stories, type of construction, and configuration) to assign structure-specific fragility functions to each building. For an event with a given seismic intensity, the probability of each building reaching a particular damage state is determined, and is used to predict the actual building states and priorities for inspection. Losses are computed based on building usage category, estimated inspection costs, the consequences of erroneous decisions, and the potential for unnecessary restrictions in access. The aim is to provide a means for a community to make rapid cost-based decisions related to inspection of their building inventory. We pose the decision problem as an integer optimization problem that attempts to minimize the expected loss to the community. The advantages of this approach are that it: (1) is simple, (2) requires minimal inventory data, (3) is easily scalable, and (4) does not require significant computing power. Use of this approach before the hazard event can facilitate planning and resources allocation in advance of an event to achieve the desirable resiliency goals of a community.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. Alexander D (2004) Planning for post-disaster reconstruction. In: I-Rec 2004 international conference improving post-disaster reconstruction in developing countries

  2. Angie’s List (2018) How much does a structural engineer cost?

  3. Applied Technology Council (1982) ATC-20.

  4. Atreya A, Kunreuther H (2018) Assessing community resilience: mapping the community rating system (CRS) against the 6C-4R frameworks. Environ Hazards.

  5. Baggio C, Bernardini A, Colozza R, Corazza L, Della Bella M, Di Pasquale G, Dolce M, Goretti A, Martinelli A, Orsini G et al (2007) Field manual for post-earthquake damage and safety assessment and short term countermeasures (aedes). European Commission–Joint Research Centre” Institute for the Protection and Security of the Citizen, EUR 22868

  6. Baker JW (2015) Efficient analytical fragility function fitting using dynamic structural analysis. Earthq Spectra 31(1):579–599

  7. Barrieu P, Albertini L (eds) (2009) Handbook of Insurance-Linked Securities. Wiley, Chichester

  8. Bensi M, Kiureghian AD, Straub D (2014) Framework for post-earthquake risk assessment and decision making for infrastructure systems. ASCE-ASME J Risk Uncertain Eng Syst, Part A: Civ Eng 1(1):04014003

  9. Bolognini D, Borzi B, Pinho R (2008) Simplified pushover-based vulnerability analysis of traditional italian rc precast structures. In: Proceedings of the 14th world conference on earthquake engineering, Beijing, China

  10. Borzi B, Crowley H, Pinho R (2008a) Simplified pushover-based earthquake loss assessment (sp-bela) method for masonry buildings. Int J Archit Herit 2(4):353–376

  11. Borzi B, Pinho R, Crowley H (2008b) Simplified pushover-based vulnerability analysis for large-scale assessment of rc buildings. Eng Struct 30(3):804–820

  12. Bruneau M, Chang SE, Eguchi RT, Lee GC, O’Rourke TD, Reinhorn AM, Shinozuka M, Tierney K, Wallace WA, Von Winterfeldt D (2003) A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq Spectra 19(4):733–752

  13. Cavalieri F, Franchin P, Gehl P, D’Ayala D (2017) Bayesian networks and infrastructure systems: computational and methodological challenges. In: Gardoni P (ed) Risk and reliability analysis: theory and applications. Springer, Cham, pp 385–415

  14. Comerio MC (1998) Disaster hits home: new policy for urban housing recovery. University of California Press, California

  15. Cummins JD, Mahul O (2008) Catastrophe risk financing in developing countries: principles for public intervention. The World Bank, Washington, DC

  16. Cutter SL, Barnes L, Berry M, Burton C, Evans E, Tate E, Webb J (2008) A place-based model for understanding community resilience to natural disasters. Global Environ Change 18(4):598–606

  17. Di Meo A, Borzi B, Faravelli M, Pagano M, Ceresa P, Monteiro R, Al-Dabbeek J (2018) Seismic vulnerability assessment of the urban building environment in nablus, palestine. Int J Archit Herit 12(7–8):1196–1215

  18. Eguchi RT, Goltz JD, Taylor CE, Chang SE, Flores PJ, Johnson LA, Seligson HA, Blais NC (1998) Direct economic losses in the northridge earthquake: a three-year post-event perspective. Earthq Spectra 14(2):245–264

  19. Faravelli M, Borzi B, Di Meo A, Polli D (2017) A mechanic-based model for definition of seismic risk and real time damage scenario of buildings. In: Proceedings of the 6th international conference on computational methods in structural dynamics and earthquake engineering, Rhodes Island, Greece

  20. Fernandez M, Alvarez L, Nixon R (2017) Still waiting for FEMA in Texas and Florida after hurricanes.

  21. Frangopol DM, Estes AC (1999) Optimum lifetime planning of bridge inspection and repair programs. Struct Eng Int 9(3):219–223

  22. Frangopol DM, Soliman M (2016) Life-cycle of structural systems: recent achievements and future directions. Struct Infrastruct Eng 12(1):1–20

  23. Google Optimization Tools (2019) Google OR-tools.

  24. Goulet J-A, Michel C, Kiureghian AD (2015) Data-driven post-earthquake rapid structural safety assessment. Earthq Eng Struct Dyn 44(4):549–562

  25. Grigoratos I, Monteiro R, Ceresa P, Di Meo A, Faravelli M, Borzi B (2018) Crowdsourcing exposure data for seismic vulnerability assessment in developing countries. J Earthq Eng.

  26. Grünthal G (1998) European macroseismic scale 1998. Tech. rep, European Seismological Commission (ESC)

  27. Gunderson L (2010) Ecological and human community resilience in response to natural disasters. Ecol Soc 15(2):18.

  28. Hearn G (2007) Bridge inspection practices, vol 375. Transportation Research Board, Washington DC

  29. Home Advisor (2018) Learn how much it costs to hire a structural engineer.

  30. Hutter G, Kuhlicke C, Glade T, Felgentreff C (2013) Natural hazards and resilience: exploring institutional and organizational dimensions of social resilience. Nat Hazards 67(1):1–6

  31. Ingram JC, Franco G, Rumbaitis-del Rio C, Khazai B (2006) Post-disaster recovery dilemmas: challenges in balancing short-term and long-term needs for vulnerability reduction. Environ Sci Policy 9(7–8):607–613

  32. Kellerer H, Pferschy U, Pisinger D (eds) (2004) Multidimensional knapsack problems. In: Knapsack problems. Springer, Berlin, pp 235–283

  33. Klein RJ, Nicholls RJ, Thomalla F (2003) Resilience to natural hazards: how useful is this concept? Glob Environ Change Part B: Environ Hazards 5(1):35–45

  34. Lenjani A, Dyke SJ, Bilionis I, Yeum CM, Kamiya K, Choi J, Liu X, Chowdhury AG (2019a) Towards fully automated post-event data collection and analysis: pre-event and post-event information fusion. Eng Struct (in press)

  35. Lenjani A, Yeum CM, Dyke S, Bilionis I (2019b) Automated building image extraction from \(360^{\circ }\) panoramas for postdisaster evaluation. Comput-Aided Civ Infrastruct Eng.

  36. Markowitz HM (1991) Foundations of portfolio theory. J Finance 46(2):469–477

  37. Marshall JD, Jaiswal K, Gould N, Turner F, Lizundia B, Barnes JC (2013) Post-earthquake building safety inspection: lessons from the Canterbury, New Zealand, earthquakes. Earthq Spectra 29(3):1091–1107

  38. Michel-Kerjan E, Zelenko I, Cardenas V, Turgel D (2011) Catastrophe financing for governments: learning from the 2009–2012 MultiCat Program in Mexico. OECD Working Papers on Finance, Insurance and Private Pensions, No. 9.

  39. Mieler M, Stojadinovic B, Budnitz R, Mahin S, Comerio M (2013) Towards resilient communities: a performance-based engineering framework for design and evaluation of the built environment. PEER 2013

  40. Mieler M, Stojadinovic B, Budnitz R, Comerio M, Mahin S (2015) A framework for linking community-resilience goals to specific performance targets for the built environment. Earthq Spectra 31(3):1267–1283

  41. Miles SB, Chang SE (2011) Resilus: a community based disaster resilience model. Cartogr Geograph Inf Sci 38(1):36–51

  42. Pearce L (2003) Disaster management and community planning, and public participation: how to achieve sustainable hazard mitigation. Nat hazards 28(2–3):211–228

  43. Phan DT, Zhu Y (2015) Multi-stage optimization for periodic inspection planning of geo-distributed infrastructure systems. Eur J Oper Res 245(3):797–804

  44. Porter KA, Kiremidjian AS (2000) Assembly-based vulnerability of buildings and its uses in seismic performance evaluation and risk-management decision-making. SPA Risk LLC, Denver

  45. Ramirez JA, Frosch RJ, Sozen MA, Turk AM (2000) Handbook for the post-earthquake safety evaluation of bridges and roads. Joint transportation research program, Indiana Dept. of Transportation (INDOT) and School of Civil Engineering, Purdue Univ., West Lafayette, IN

  46. Rose A (2004) Defining and measuring economic resilience to disasters. Disaster Prev Manag: Int J 13(4):307–314

  47. Silva V, Akkar S, Baker J, Bazzurro P, Castro JM, Crowley H, Dolsek M, Galasso C, Lagomarsino S, Monteiro R et al (2019) Current challenges and future trends in analytical fragility and vulnerability modelling. Earthq Spectra 35:1927–1952

  48. Straub D, Faber MH (2005) Risk based inspection planning for structural systems. Struct Saf 27(4):335–355

  49. Tierney K, Bruneau M (2007) Conceptualizing and measuring resilience: a key to disaster loss reduction. TR news (250)

  50. Viscusi WK, Aldy JE (2003) The value of a statistical life: a critical review of market estimates throughout the world. J Risk Uncertain 27(1):5–76

  51. Yeum CM, Lenjani A, Dyke SJ, Bilionis I (2018) Automated detection of pre-disaster building images from google street view. In: Proceedings of the 7th world conference on structural control and monitoring, (7WCSCM 2018)

  52. Yousefi N, Coit DW (2019) Dynamic inspection planning for systems with individually repairable components. arXiv:190300932

Download references


The authors wish to acknowledge partial support from the Purdue Center for Resilient Infrastructures, Systems, and Processes (CRISP) and the National Science Foundation under Grant No. NSF 1608762.

Author information

Correspondence to Shirley J. Dyke.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lenjani, A., Bilionis, I., Dyke, S.J. et al. A resilience-based method for prioritizing post-event building inspections. Nat Hazards 100, 877–896 (2020).

Download citation


  • Urban resilience
  • Risk management
  • Uncertainty quantification
  • Inspection planning
  • Dynamic programming
  • Natural hazards