Advertisement

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)

Abstract

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.

Keywords

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Comley, J.W., Dowe, D.L.: General Bayesian Networks and Asymmetric Languages. In: Proc. 2nd Hawaii International Conference on Statistics and Related Fields, June 5-8 (2003)Google Scholar
  2. 2.
    Comley, J.W., Dowe, D.L.: Minimum Message Length and Generalized Bayesian Nets with Asymmetric Languages, ch. 11. In: Grunwald, P.D., Myung, I.J., Pitt, M.A. (eds.) Advances in Minimum Description Length Theory and Applications, pp. 265–294. MIT Press, London (2005)Google Scholar
  3. 3.
    Dowe, D.L., Wallace, C.S.: Kolmogorov complexity, minimum message length and inverse learning, abstract. In: 14th Australian Statistical Conference (ASC-14), Gold Coast, Qld, July 6-10, p. 144 (1998)Google Scholar
  4. 4.
    Fitzgibbon, L.J., Allison, L., Dowe, D.L.: Minimum Message Length Grouping of Ordered Data. In: Arimura, H., Sharma, A.K., Jain, S. (eds.) ALT 2000. LNCS (LNAI), vol. 1968, pp. 56–70. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Fitzgibbon, L.J., Dowe, D.L., Vahid, F.: Minimum Message Length Autoregressive Model Order Selection. In: Palanaswami, M., Chandra Sekhar, C., Kumar Venayagamoorthy, G., Mohan, S., Ghantasala, M.K. (eds.) International Conference on Intelligent Sensing and Information Processing (ICISIP), Chennai, India, January 4-7, 2004, pp. 439–444. IEEE, Los Alamitos (2004)CrossRefGoogle Scholar
  6. 6.
    Moore, M., Phares, B., Graybeal, B., Rolander, D., Washer, G.: Reliability of Visual Inspection for Highway Bridges, vol. 1 and 2, Final Report, Report No: FHWA-RD-01-020, NDE Validation Center, Office of Infrastructure Research and Development, Federal Highway Administration, McLean, VA, USA (2001)Google Scholar
  7. 7.
    Rissanen, J.J.: Modeling by Shortest Data Description. Automatica 14, 465–471 (1978)MATHCrossRefGoogle Scholar
  8. 8.
    Tan, P.J., Dowe, D.L.: MML Inference of Oblique Decision Trees. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 1082–1088. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    VicRoads: VicRoads Bridge Inspection Manual, Melbourne, Australia (1995)Google Scholar
  10. 10.
    Viswanathan, M., Wallace, C.S., Dowe, D.L., Korb, K.B.: Finding Cutpoints in Noisy Binary Sequences - A Revised Empirical Evaluation. In: Foo, N.Y. (ed.) Canadian AI 1999. LNCS, vol. 1747, pp. 405–416. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  11. 11.
    Wallace, C.S.: Statistical and inductive inference by minimum message length. Springer, Berlin (2005)MATHGoogle Scholar
  12. 12.
    Wallace, C.S., Boulton, D.M.: An Information Measure for Classification. Computer Journal 11, 185–194 (1968)MATHGoogle Scholar
  13. 13.
    Wallace, C.S., Dowe, D.L.: Intrinsic Classification by MML - the Snob Program. In: Proc. 7th Australian Joint Conference on Artificial Intelligence, UNE, pp. 37–44. World Scientific, Armidale (1994)Google Scholar
  14. 14.
    Wallace, C.S., Dowe, D.L.: Minimum Message Length and Kolmogorov Complexity. Computer Journal (Special issue on Kolmogorov Complexity) 42(4), 270–283 (1999)MATHGoogle Scholar
  15. 15.
    Wallace, C.S., Dowe, D.L.: MML Clustering of Multi-State, Poisson, von Mises Circular and Gaussian Distributions. Statistics and Computing 10, 73–83 (2000)CrossRefGoogle Scholar
  16. 16.
    Wallace, C.S., Freeman, P.R.: Estimation and Inference by Compact Coding. J. Royal Statistical Society (Series B) 49, 240–252 (1987)MATHMathSciNetGoogle Scholar

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

Personalised recommendations