Bayesian Model for Cost Estimation of Construction Projects

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.

[1]  Nie-Jia Yau,et al.  Case‐Based Reasoning in Construction Management , 1998 .

[2]  Feng Hong,et al.  Updating Pavement Deterioration Models Using the Bayesian Principles and Simulation Techniques , 2005 .

[3]  C. M. Tam,et al.  Comparative Cost Analysis of Using High-Performance Concrete in Tall Building Construction by Artificial Neural Networks , 1999 .

[4]  A. Boussabaine,et al.  KNOWLEDGE DISCOVERY IN RESIDENTIAL CONSTRUCTION PROJECT COST DATA , 2005 .

[5]  Herbert H. Einstein,et al.  Updating the Decision Aids for Tunneling , 2002 .

[6]  Ian D. Watson,et al.  An Introduction to Case-Based Reasoning , 1995, UK Workshop on Case-Based Reasoning.

[7]  I. Iervolino,et al.  Computer Aided Civil and Infrastructure Engineering , 2009 .

[8]  Paolo Gardoni,et al.  A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress , 2007, Comput. Aided Civ. Infrastructure Eng..

[9]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[10]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  James T. O'Connor,et al.  Preliminary Cost Estimates Using Probabilistic Simulation for Highway Bridge Replacement Projects , 2005 .

[12]  Melvin Alexander.  Applied Statistics and Probability for Engineers , 1995 .

[13]  Robert V. Brill,et al.  Applied Statistics and Probability for Engineers , 2004, Technometrics.

[14]  Chester G. Wilmot,et al.  Neural Network Modeling of Highway Construction Costs , 2005 .

[15]  Brenda McCabe,et al.  Developing Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks , 2007 .

[16]  Tarek Hegazy,et al.  Developing Practical Neural Network Applications Using Back‐Propagation , 1994 .

[17]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[18]  A. Zellner.  An Introduction to Bayesian Inference in Econometrics , 1971 .

[19]  Michael C. Vorster,et al.  Highway Construction Data Collection and Treatment in Preparation for Statistical Regression Analysis , 2009 .

[20]  Simaan M. AbouRizk,et al.  Bayesian Updating Application into Simulation in the North Edmonton Sanitary Trunk Tunnel Project , 2006 .

[21]  Kyung In Kang,et al.  A case-based reasoning cost estimating model using experience by analytic hierarchy process , 2007 .

[22]  Ian D. Watson,et al.  Applying case-based reasoning - techniques for the enterprise systems , 1997 .

[23]  Tarek Hegazy,et al.  NEURAL NETWORK MODEL FOR PARAMETRIC COST ESTIMATION OF HIGHWAY PROJECTS , 1998 .

[24]  Kenneth F. Reinschmidt,et al.  Probabilistic Forecasting of Project Duration Using Bayesian Inference and the Beta Distribution , 2009 .

[25]  Jui-Sheng Chou,et al.  Internet-based preliminary highway construction cost estimating database , 2007 .

[26]  K. V. Pagalthivarthi,et al.  Bayesian Network for E/M Impedance-Based Damage Identification , 2006 .

[27]  Stephen P. Brooks,et al.  Markov chain Monte Carlo method and its application , 1998 .

[28]  Charles T. Jahren,et al.  Journal of Construction Engineering and Management , 1983 .