Bayesian project diagnosis for the construction design process

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

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: Nov 14, 2012
• 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.
• Public URL: https://durham-repository.worktribe.com/output/1501461