Journal of the Korea Institute of Building Construction (한국건축시공학회지)

The Korean Institute of Building Construction (한국건축시공학회)
권호 표지
DOI QR코드

DOI QR Code

Bayesian Model for Cost Estimation of Construction Projects

  • Kim, Sang-Yon (School of Construction Management and Engineering, University of Reading)
  • Received : 2011.01.20
  • Accepted : 2011.02.14
  • Published : 2011.02.20
Download PDF
⟨ Previous Next ⟩

Abstract

Bayesian network is a form of probabilistic graphical model. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain cases. In this research, bayesian network is adopted to model the problem of construction project cost. General information, time, cost, and material, the four main factors dominating the characteristic of construction costs, are incorporated into the model. This research presents verify a model that were conducted to illustrate the functionality and application of a decision support system for predicting the costs. The Markov Chain Monte Carlo (MCMC) method is applied to estimate parameter distributions. Furthermore, it is shown that not all the parameters are normally distributed. In addition, cost estimates based on the Gibbs output is performed. It can enhance the decision the decision-making process.

Keywords

References

  1. Chou JS, O'Connor JT. Internet-Based Preliminary Highway Construction Cost Estimating Database. Automation in Construction 2007;17(1):65-74. https://doi.org/10.1016/j.autcon.2007.03.001
  2. Tam CM, Fang CF. Comparative Cost Analysis of Using High Performance Concrete in Tall Building Construction by Artificial Neural Networks. Structural Journal 1999;96(6):927-936.
  3. Chou JS, Wang L, Chong WK, O'Connor JT. Preliminary Cost Estimates Using Probabilistic Simulation for Highway Bridge Replacement Projects. ASCE Proceeding of the Congress 2005.
  4. Al-Tabtabai H, Alex AP, Tantash M. Preliminary Cost Estimation of Highway Construction Using Neural Networks. Cost Engineering 1999;41(3):19-24.
  5. Hegazy T, Ayed A. Neural Network Model for Parametric Cost Estimation of Highway Projects. Journal of Construction Engineering and Management 1998;124(3):210-18. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:3(210)
  6. Wilmot CG, Mei B. Neural Network Modelling of Highway Construction Costs. Journal of Construction Engineering and Management 2005;131(7):765-71. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:7(765)
  7. Watson I. Applying Case-Based Reasoning: Techniques for Enterprise Systems. 1st Ed. Morgan Kaufmann Publishers Inc.; 1997.
  8. An SH, Kim GH, Kang KI. A Case-Based Reasoning Cost Estimating Model Using Experience by Analytic Hierarchy Process. Building and Environment 2007;42(7):2573-79. https://doi.org/10.1016/j.buildenv.2006.06.007
  9. Yau NJ, Yang JB. Case-Based Reasoning in Construction Management. Computer-Aided Civil and Infrastructure Engineering 1998;13(2):143-50. https://doi.org/10.1111/0885-9507.00094
  10. Hegazy T, Fazio P, Moselhi O. Developing Practical Neural Network Applications Using Back-Propagation. Computer-Aided Civil and Infrastructure Engineering 1994;9(2):145-59. https://doi.org/10.1111/j.1467-8667.1994.tb00369.x
  11. Kolodner JL. An Introduction to Case-Based Reasoning. Artificial Intelligence Review 1992;6(1):3-34. https://doi.org/10.1007/BF00155578
  12. Wikipedia. Bayesian Network. http://en.wikipeda.org/ wiki/ Bayesian_network 2010.
  13. Gardoni P, Reinschmidt KF, Kumar R. A Probailistic Framework for Bayesian Adaptive Forecasting of Project Progress. Compter-Aided Civil and Infrastructure Engineering 2007;22(3):182-96 https://doi.org/10.1111/j.1467-8667.2007.00478.x
  14. Kim BC, Reinschmidt KF. Probabilistic Forecasting of Project Duration Using Bayesian Inference and the Beta Distribution. Journal of Construction Engineering and Management 2009;135(3):178-86. https://doi.org/10.1061/(ASCE)0733-9364(2009)135:3(178)
  15. Tang Z, McCabe B. Developing Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks. Journal of Computing in Civil Engineering 2007;21(4):265-276. https://doi.org/10.1061/(ASCE)0887-3801(2007)21:4(265)
  16. Haas C, Einstein HH. Updatng the Decision Aids for Tunneling. Journal of Construction Engineering and Management 2002;128(1):40-48. https://doi.org/10.1061/(ASCE)0733-9364(2002)128:1(40)
  17. Chung TH, Mohamed Y, AbouRizk S. Bayesian Updating Application into Simulation in the North Edmonton Sanitary Trunk Tunnel Project. Journal of Construction Engineering and Management 2006;132(8):882-94. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:8(882)
  18. Boussabaine AH, Elhag TMS. Knowledge Discovery in Residential Construction Project Cost Data. 15th Annual ARCOM Conference 1999:489-98.
  19. Jensen FV. Bayesian Networks and Decision Graphs. 2nd Ed. USA, New York:Springer; 2007.
  20. Naidu ASK, Soh CK, Pagalthivarthi KV. Bayesian Network for E/M Impedance-Based Damage Identification. Journal of Computing in Civil Engineering 2006;20(4):227-36. https://doi.org/10.1061/(ASCE)0887-3801(2006)20:4(227)
  21. Montgomery DC, Runger GC. Applied Statistics and Probability for Engineers, 4th Ed. USA, New York:Wiley; 2006.
  22. Zellner A. An Introduction to Bayesian Inference in Econometrics. 2nd Ed. New York;Wiley; 1996.
  23. Box GEP, Tiao GC. Bayesian Inference in Statistical Analysis. 1st Ed. New York;Wiley;1992.
  24. Geman S, Geman D. Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images, IEEE Transactions on Pattern Analysis and Machine Intelligence 1984;6(6):721-41. https://doi.org/10.1109/TPAMI.1984.4767596
  25. Brooks SP. Markov Chain Monte Carlo Method and Its Application. Journal of the Royal Statistical Society, Series D 1998;47(1):69-100. https://doi.org/10.1111/1467-9884.00117
  26. Hong F, Prozzi JA. Updating Pavement Deterioration Models Using the Bayesian Principles and Simulation Techniques. 1st Annual Inter-University Symposium on Infrastructure Management 2005.
  27. Williams RC, Hildreth JC, Vorster MC. Journal of Construction Engineering and Management 2009;135(12):1299-1306. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000112

Cited by

  1. Mathematical modelling of embodied energy, greenhouse gases, waste, time–cost parameters of building projects: A review vol.59, 2013, https://doi.org/10.1016/j.buildenv.2012.07.014
  2. Elephant poaching risk assessed using spatial and non-spatial Bayesian models vol.338, 2016, https://doi.org/10.1016/j.ecolmodel.2016.08.002
  3. Assessing trends and seasonal changes in elephant poaching risk at the small area level using spatio-temporal Bayesian modeling vol.32, pp.3, 2018, https://doi.org/10.1080/13658816.2017.1404605