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http://dx.doi.org/10.5345/JKIC.2011.02.1.091

Bayesian Model for Cost Estimation of Construction Projects  

Kim, Sang-Yon (School of Construction Management and Engineering, University of Reading)
Publication Information
Journal of the Korea Institute of Building Construction / v.11, no.1, 2011 , pp. 91-99 More about this Journal
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
Bayesian; Cost estimating; Markov Chain Monte Carlo;
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1 Hong F, Prozzi JA. Updating Pavement Deterioration Models Using the Bayesian Principles and Simulation Techniques. 1st Annual Inter-University Symposium on Infrastructure Management 2005.
2 Williams RC, Hildreth JC, Vorster MC. Journal of Construction Engineering and Management 2009;135(12):1299-1306.   DOI   ScienceOn
3 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.   DOI   ScienceOn
4 Haas C, Einstein HH. Updatng the Decision Aids for Tunneling. Journal of Construction Engineering and Management 2002;128(1):40-48.   DOI   ScienceOn
5 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.   DOI   ScienceOn
6 Boussabaine AH, Elhag TMS. Knowledge Discovery in Residential Construction Project Cost Data. 15th Annual ARCOM Conference 1999:489-98.
7 Jensen FV. Bayesian Networks and Decision Graphs. 2nd Ed. USA, New York:Springer; 2007.
8 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.   DOI   ScienceOn
9 Montgomery DC, Runger GC. Applied Statistics and Probability for Engineers, 4th Ed. USA, New York:Wiley; 2006.
10 Zellner A. An Introduction to Bayesian Inference in Econometrics. 2nd Ed. New York;Wiley; 1996.
11 Box GEP, Tiao GC. Bayesian Inference in Statistical Analysis. 1st Ed. New York;Wiley;1992.
12 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.   DOI   ScienceOn
13 Brooks SP. Markov Chain Monte Carlo Method and Its Application. Journal of the Royal Statistical Society, Series D 1998;47(1):69-100.   DOI   ScienceOn
14 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.   DOI   ScienceOn
15 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.   DOI   ScienceOn
16 Wilmot CG, Mei B. Neural Network Modelling of Highway Construction Costs. Journal of Construction Engineering and Management 2005;131(7):765-71.   DOI   ScienceOn
17 Watson I. Applying Case-Based Reasoning: Techniques for Enterprise Systems. 1st Ed. Morgan Kaufmann Publishers Inc.; 1997.
18 Wikipedia. Bayesian Network. http://en.wikipeda.org/ wiki/ Bayesian_network 2010.
19 Yau NJ, Yang JB. Case-Based Reasoning in Construction Management. Computer-Aided Civil and Infrastructure Engineering 1998;13(2):143-50.   DOI   ScienceOn
20 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.   DOI
21 Kolodner JL. An Introduction to Case-Based Reasoning. Artificial Intelligence Review 1992;6(1):3-34.   DOI
22 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   DOI   ScienceOn
23 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.   DOI   ScienceOn
24 Al-Tabtabai H, Alex AP, Tantash M. Preliminary Cost Estimation of Highway Construction Using Neural Networks. Cost Engineering 1999;41(3):19-24.
25 Chou JS, O'Connor JT. Internet-Based Preliminary Highway Construction Cost Estimating Database. Automation in Construction 2007;17(1):65-74.   DOI   ScienceOn
26 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.
27 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.