Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. 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 modelled as a quasi-Markov chain. A set of six different scenarios representing various common problems (e.g. small budget, complex project) are created and simulated by suitably defining the transition probabilities between nodes in the Markov chain. A Monte Carlo approach is used to parametrise a Bayesian estimator. By observing the time taken to pass the Review Gateway (as measured by the number of hops between activity nodes), the system is able to determine with good accuracy the problem scenario that the construction process will likely suffer from.