MML Mixture Models of Heterogeneous Poisson Processes with Uniform Outliers for Bridge Deterioration

  • T. Maheswaran
  • J. G. Sanjayan
  • David L. Dowe
  • Peter J. Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


Effectiveness of maintenance programs of existing concrete bridges is highly dependent on the accuracy of the deterioration parameters utilised in the asset management models of the bridge assets. In this paper, bridge deterioration is modelled using non-homogenous Poisson processes, since deterioration of reinforced concrete bridges involves multiple processes. Minimum Message Length (MML) is used to infer the parameters for the model. MML is a statistically invariant Bayesian point estimation technique that is statistically consistent and efficient. In this paper, a method is demonstrated estimate the decay-rates in non-homogeneous Poisson processes using MML inference. The application of methodology is illustrated using bridge inspection data from road authorities. Bridge inspection data are well known for their high level of scatter. An effective and rational MML-based methodology to weed out the outliers is presented as part of the inference.


Poisson Process Asset Management Minimum Description Length Kolmogorov Complexity Concrete Bridge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • T. Maheswaran
    • 1
  • J. G. Sanjayan
    • 2
  • David L. Dowe
    • 3
  • Peter J. Tan
    • 3
  1. 1.Previously Department of Civil EngineeringMonash University when the research presented in this paper was carried out
  2. 2.Department of Civil EngineeringMonash UniversityClaytonAustralia
  3. 3.School of Computer Science and Software EngineeringMonash UniversityClaytonAustralia

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