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Bayesian project diagnosis for the construction design process

Published online by Cambridge University Press:  02 November 2012

P.C. Matthews  and
A.D.M. Philip
P.C. Matthews*
Affiliation:
School of Engineering and Computing Sciences, Durham University, Durham, UK
A.D.M. Philip
Affiliation:
School of Engineering and Computing Sciences, Durham University, Durham, UK
*
Reprint requests to: P. C. Matthews, School of Engineering and Computing Sciences, Durham University, Durham DH1 3LE, UK. E-mail: p.c.matthews@durham.ac.uk
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Abstract

This study demonstrates how subtle signals taken from the early stages within a construction process can be used to diagnose potential problems within that process. For this study, the construction process is modeled as a quasi-Markov chain. A set of six different scenarios representing various common problems (e.g., small budget, complex project) is created and simulated by suitably defining the transition probabilities between nodes in the Markov chain. A Monte Carlo approach is used to parameterize a Bayesian estimator. By observing the time taken to pass the review gateway (as measured by number of hops between activity nodes), the system is able to determine with good accuracy the problem scenario that the construction process is suffering from.

Keywords

Design ProcessMarkov ChainsMonte Carlo SimulationProject ManagementUncertainty

Information

Type
Special Issue Articles
Information
AI EDAM , Volume 26 , Issue 4: Intelligent Decision Support and Modeling , November 2012 , pp. 375 - 391
Copyright
Copyright © Cambridge University Press 2012

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