Artificial Intelligence for Engineering Design, Analysis and Manufacturing

Special Issue Articles

Bayesian project diagnosis for the construction design process

P.C. Matthewsa1 c1 and A.D.M. Philipa1

a1 School of Engineering and Computing Sciences, Durham University, Durham, UK


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.

(Received October 04 2011)

(Accepted May 07 2012)


  • Design Process;
  • Markov Chains;
  • Monte Carlo Simulation;
  • Project Management;
  • Uncertainty


c1 Reprint requests to: P. C. Matthews, School of Engineering and Computing Sciences, Durham University, Durham DH1 3LE, UK. E-mail: