Matthews, P.C. and Philip, A.D.M. (2012) 'Bayesian project diagnosis for the construction design process.', Artificial intelligence for engineering design, analysis and manufacturing., 26 (4). pp. 375-391.
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 Process, Markov Chains, Monte Carlo Simulation, Project Management.|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||http://dx.doi.org/10.1017/S089006041200025X|
|Publisher 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|
|Date accepted:||No date available|
|Date deposited:||14 November 2012|
|Date of first online publication:||November 2012|
|Date first made open access:||No date available|
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