Risk Appraisal in Engineering Infrastructure Projects: Examination of Project Risks Using Probabilistic Analysis

  • Jane Lai
  • Lihai Zhang
  • Colin Duffield
  • Lu Aye
Conference paper


Understanding the significant implication of uncertainty is an important step in infrastructure project appraisals. A detailed discussion and application of a risk-based, cost-benefit analytical framework with focus on the analysis of likelihood of risks is presented in this chapter. Three risk analysis tools (i.e. Monte Carlo simulation, Latin Hypercube sampling, and engineering reliability analysis) are presented and compared based on their efficiency and accuracy. Likelihood of risk was represented by a project’s probability of investment loss. The framework was applied to a residential property in Melbourne, Australia, with house price as an uncertain variable. It was shown that engineering reliability analysis was the most accurate and efficient in calculating a probability of loss in a 3-year investment duration. In addition, Latin Hypercube sampling, requiring 50 to 100 iterations for convergence, was superior to Monte Carlo simulation which needed 500 to 1000 iterations. Finally, an integrated model is presented to estimate the project risk in term of expected loss.


Engineering reliability analysis Expected loss Latin hypercube sampling Monte Carlo simulation Probability of loss Risk analysis 


  1. 1.
    A. Haldar, S. Mahadevan, Probability, Reliability and Statistical Methods in Engineering Design (Wiley, New York, 2000)Google Scholar
  2. 2.
    S.E. Chia, Risk assessment framework for project management, in EEE (2006), pp. 376–379Google Scholar
  3. 3.
    N. Gil, B.S. Tether, Project risk management and design flexibility: analysing a case and conditions of complementarity. Res. Policy 40(3), 415–428 (2011)CrossRefGoogle Scholar
  4. 4.
    A. Nieto-Morote, F. Ruz-Vila, A fuzzy approach to construction project risk assessment. Int. J. Project Manage. 29(2), 220–231 (2011)CrossRefGoogle Scholar
  5. 5.
    Standards Australia, in AS/NZS ISO31000:2009 Risk Management—Principles and Guidelines, Sydney, Australia (2009)Google Scholar
  6. 6.
    A. Riabacke, Managerial decision making under risk and uncertainty. IAENG Int. J. Comput. Sci. 32(4), 453–459 (2006)Google Scholar
  7. 7.
    H.M.S. Treasury, The Green Book: Appraisal and Evaluation in Central Government (TSO, London, 2003)Google Scholar
  8. 8.
    P. Iskanius, Risk management in ERP project in the context of SMEs. Eng. Lett. 17(4), 266–273 (2009) Google Scholar
  9. 9.
    P. Bhattacharjee, K. Ramesh Kumar, T. Janardhan Reddy, Structural safety evaluation using modified latin hypercube sampling technique. Int. J. Perform. Eng. 9(5), 515–522 (2013)Google Scholar
  10. 10.
    J. Imai, K.S. Tan, Dimension reduction approach to simulating exotic options in a Meixner Lévy market. IAENG Int. J. Appl. Math. 39(4), 265–275 (2009)MATHMathSciNetGoogle Scholar
  11. 11.
    M.D. McKay, W.J. Conover, R.J. Beckman, Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2), 239–245 (1979)MATHMathSciNetGoogle Scholar
  12. 12.
    S.S. Drew, T. Homem-de-Mello, Some large deviations results for Latin hypercube sampling. Methodol. Comput. Appl. Probab. 14(2), 203–232 (2012)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    A.Z. Grzybowski, Monte Carlo analysis of risk measures for blackjack type optimal stopping problems. Eng. Lett. 19(3), 147–154 (2011)Google Scholar
  14. 14.
    J.P.C. Kleijnen, Sensitivity analysis and related analyses: a review of some statistical techniques. J. Stat. Comput. Simul. 57(1–4), 111–142 (2007)Google Scholar
  15. 15.
    D. Straub, A. Der Kiureghian, Bayesian network enhanced with structural reliability methods: methodology. J. Eng. Mech. 136(10), 1248–1258 (2010)CrossRefGoogle Scholar
  16. 16.
    M. Tichý, Applied Methods of Structural Reliability, vol. 2 (Kluwer Academic Publishers, The Netherlands, 1993)MATHGoogle Scholar
  17. 17.
    J. Lai, L. Zhang, C. Duffield, L. Aye, Economic risk analysis for sustainable urban development: validation of framework and decision support technique. Desalin. Water Treat, 52(4–6), 1109–1121 (2014) Google Scholar
  18. 18.
    J. Lai, L. Zhang, C. Duffield, L. Aye, Financial risk analysis for engineering management: a framework development and testing, in Lecture Notes in Engineering and Computer Science, Proceedings of the World Congress on Engineering and Computer Science 2013, WCECS 2013, San Francisco, USA, 23–25 Oct, (2013), pp. 1042–1046Google Scholar
  19. 19.
    J. Lai, L. Zhang, C. Duffield, L. Aye, Engineering reliability analysis in risk management framework: development and application in infrastructure project. IAENG Int. J. Appl. Math. 43(4), 242–249 (2013)MathSciNetGoogle Scholar
  20. 20.
    S. Adarsh, M.J. Reddy, Reliability analysis of composite channels using first order approximation and Monte Carlo simulations. Stoch. Env. Res. Risk Assess. 27(2), 477–487 (2013)CrossRefGoogle Scholar
  21. 21.
    Australian Bureau of Statistics, in Average Floor Area Of New Dwellings, Building Approvals, 8731.0, Canberra, Australia. (2003), p. 38–40Google Scholar
  22. 22.
    The Real Estate Institute of Victoria, in Market History, Melbourne, Camberwell, Victoria, Australia, 31 July 2013Google Scholar
  23. 23.
    State Government of Victoria, in Land Tax Act 2005, Authorised Version No. 045, No. 88 of 2005, Chief Parliamentary Counsel, Editor, State Government of Victoria, Melbourne, Victoria, Australia (2013)Google Scholar
  24. 24.
    State Government of Victoria, in Duties Act 2000, Authorised Version No. 090, No. 79 of 2000, Chief Parliamentary Counsel, Editor, State Government of Victoria, Melbourne, Victoria, Australia (2012)Google Scholar
  25. 25.
    City of Melbourne, in How Your Rates are Calculated, Melbourne, Victoria, Australia, 20 Feb 2014 (2013)Google Scholar
  26. 26.
    CPA Australia, in Tax and Social Security Guide 2013–2014, CPA Australia, Melbourne, Victoria, Australia (2013)Google Scholar
  27. 27.
    Reserve Bank of Australia, in Interest Rates and Yields, Sydney, New South Wales, Australia, 17 Sept 2013Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Infrastructure EngineeringThe University of MelbourneParkvilleAustralia

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