Advertisement

Natural Hazards

, Volume 91, Issue 2, pp 717–734 | Cite as

Disaster waste clean-up system performance subject to time-dependent disaster waste accumulation

  • Cheng Cheng
  • Lihai Zhang
  • Russell George Thompson
Original Paper

Abstract

Disasters can produce a substantial amount of waste that can threaten the capacity of waste management systems. This paper presents a methodology for estimating waste accumulation caused by disasters considering the uncertainty of the timing and scale of disasters that can be used to estimate the return period and the reliability of the disaster waste management system. To estimate the reliability of the system, the first-order second-moment reliability assessment method, in which the reliability index (\(\beta\)) is used to judge the reliability of a system, is applied in this paper. In addition, two case studies illustrate how the methods can be applied to the real world. The reliability index curve of the system developed from sensitivity analysis can provide information for decision-makers in terms of disaster waste clean-up arrangements. The approach developed can be used to analyze the effects of different parameters involved in the waste clean-up system after disasters.

Keywords

Disaster waste accumulation Disaster waste clean-up Reliability index First-order second-moment 

Notes

Acknowledgements

The authors would like to acknowledge the support from the Centre for Disaster Management and Public Safety (CDMPS) and the Department of Infrastructure Engineering at the University of Melbourne.

References

  1. Abbasi M, El Hanandeh A (2016) Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manag 56:13–22.  https://doi.org/10.1016/j.wasman.2016.05.018 CrossRefGoogle Scholar
  2. Allen JT, Allen ER (2016) Invited review article: a review of severe thunderstorms in Australia. Atmos Res 178–179:347–366.  https://doi.org/10.1016/j.atmosres.2016.03.011 CrossRefGoogle Scholar
  3. Amirebrahimi S, Rajabifard A, Mendis P, Ngo T (2016) A framework for a microscale flood damage assessment and visualization for a building using BIM–GIS integration. Int J Digit Earth 9(4):363–386CrossRefGoogle Scholar
  4. Araghi A, Adamowski J, Jaghargh MR (2016) Detection of trends in days with thunderstorms in Iran over the past five decades. Atmos Res 172–173:174–185.  https://doi.org/10.1016/j.atmosres.2015.12.022 CrossRefGoogle Scholar
  5. Basnayake B, Chiemchaisri C, Visvanathan C (2006) Wastelands: clearing up after the tsunami in Sri Lanka and Thailand. Waste Manag World, pp 31–38 (March–April)Google Scholar
  6. Baycan F (2004) Emergency planning for disaster waste: a proposal based on the experience of the Marmara Earthquake in Turkey. In: 2004 International conference and student competition on post-disaster reconstruction “planning for reconstruction”, Coventry, UKGoogle Scholar
  7. Brown C, Milke M, Seville E (2011) Disaster waste management: a review article. Waste Manag 31:1085–1098CrossRefGoogle Scholar
  8. Chen S, Fang S-S, Lin C-L, Hung H-C (2013) Generation of solid wastes in typhoon disasters and their emergent cleaning and treatment means. J Mater Cycles Waste Manag 15:269–281CrossRefGoogle Scholar
  9. Doğan K, Süleyman S (2003) Report: cost and financing of municipal solid waste collection services in Istanbul. Waste Manag Res 21:480–485CrossRefGoogle Scholar
  10. FEMA (2007) US FEMA (2007) Public assistance: debris management guide. U.S. Dept. of Homeland Security, Federal Emergency Management Agency, Washington, D.CGoogle Scholar
  11. Hirayama N, Kawata Y, Suzuki S, Harada K (2009) Estimation procedure for potential quantity of tsunami debris on tsunami earthquake disasters. Proceedings of the Twelfth International Waste Management Landfill CD-ROMGoogle Scholar
  12. Jingwei S et al (2014) Simulated annealing based hybrid forecast for improving daily municipal solid waste generation prediction. Sci World J.  https://doi.org/10.1155/2014/834357 Google Scholar
  13. Lai J, Zhang L, Duffield C, Aye L (2013) Engineering reliability analysis in risk management framework: development and application in infrastructure project. Int J Appl Math 43(4):242–249Google Scholar
  14. Lizada JC, Ibabao RA (2013) Building resilience through solid waste management: the case of the Iloilo Province, Central Philippines, pp 698–709Google Scholar
  15. Paulikas MJ (2014) Examining population bias relative to severe thunderstorm hazard reporting trends in the Atlanta, GA metropolitan region. Meteorol Appl.  https://doi.org/10.1002/met.1394 Google Scholar
  16. Rawtec (2009) Disaster waste management scoping study. South AustraliaGoogle Scholar
  17. Swan RC (2000) Debris management planning for the 21st century. Nat Hazards Rev 1:222CrossRefGoogle Scholar
  18. Tan ST, Lee CT, Hashim H, Ho WS, Lim JS (2014) Optimal process network for municipal solid waste management in Iskandar Malaysia. J Clean Prod 71:48–58.  https://doi.org/10.1016/j.jclepro.2013.12.005 CrossRefGoogle Scholar
  19. USACE (2006) Depth-damage relationships for structures, contents, and vehicles and content-to-structure value ratios (CSVR) in support of the Donaldsonville to the Gulf Louisiana, feasibility study. Final Draft prepared by Gulf Engineers and ConsultantsGoogle Scholar
  20. Xiao J, Xie H, Zhang C (2012) Investigation on building waste and reclaim in Wenchuan earthquake disaster area. Resour Conserv Recycl 61:109–117CrossRefGoogle Scholar
  21. Yamanaka M, Toyota N, Hasegawa S, Nonomura A (2013) Estimation method of amount of tsunami disaster wastes during the 2011 off the Pacific coast of Tohoku Earthquake. Int J GEOMATE 4(1):456–461Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Center for Disaster Management and Public SafetyThe University of MelbourneMelbourneAustralia
  2. 2.The Department of Infrastructure EngineeringThe University of MelbourneMelbourneAustralia

Personalised recommendations