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Bayesian project diagnosis for the construction design process

Matthews, P.C.; Philip, A.D.M.

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Authors

A.D.M. Philip



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.

Citation

Matthews, P., & Philip, A. (2012). Bayesian project diagnosis for the construction design process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 26(4), 375-391. https://doi.org/10.1017/s089006041200025x

Journal Article Type Article
Publication Date Nov 1, 2012
Deposit Date Nov 7, 2012
Publicly Available Date Mar 28, 2024
Journal Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Print ISSN 0890-0604
Electronic ISSN 1469-1760
Publisher Cambridge University Press
Peer Reviewed Peer Reviewed
Volume 26
Issue 4
Pages 375-391
DOI https://doi.org/10.1017/s089006041200025x
Keywords Design Process, Markov Chains, Monte Carlo Simulation, Project Management.

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Accepted Journal Article (218 Kb)
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Copyright Statement
© Copyright Cambridge University Press 2012. This paper has been published in a revised form subsequent to editorial input by Cambridge University Press in "Artificial intelligence for engineering design, analysis and manufacturing" (26: Special issue 4 (2012) 375-391) http://journals.cambridge.org/action/displayJournal?jid=AIE




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