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Application of Gamma Process for Deterioration Prediction of Buildings from Discrete Condition Data

Authors:

Ruwini Edirisinghe ,

RMIT University, Melbourne, AU
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Sujeeva Setunge,

RMIT University, Melbourne, AU
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Guomin Zhang

RMIT University, Melbourne, AU
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Abstract

Deterioration prediction of civil infrastructure from discrete condition data is a challenge faced by many asset managers developing effective maintenance and renewal strategies. Due to high variability of data, often, deterministic methods are not readily applicable. Some of the reliability based methods adopted are time dependent reliability analysis, such as Markov chain and more recently gamma process. Whilst such models have been developed for assets with smaller number components, such as bridges and storm water pipes, for complex systems such as buildings reliability based methods are less common. The second largest class of infrastructure assets the local governments own in Australia is the community buildings. As the majority of existing community buildings are maturing, the local governments seek more reliable asset management strategies. Condition based forecasting is a major component of such asset management approaches. This paper presents development of a reliability based methodology for deterioration prediction of community buildings. Gamma process is considered to be an appropriate approach for predicting building element deterioration due to the temporal variability of degradation. The Gamma deterioration process presented in this paper is a stochastic process with independent non-negative increments having gamma distribution with identical scale parameter. Building inspection data from one of the local governments in Victoria are used in the model. Further, analysis of the data and model results are discussed.

DOI: http://dx.doi.org/10.4038/sljastats.v12i0.4965

Sri Lankan Journal of Applied Statistics Vol.12 2011 pp.12-25

How to Cite: Edirisinghe, R., Setunge, S. & Zhang, G., (2012). Application of Gamma Process for Deterioration Prediction of Buildings from Discrete Condition Data. Sri Lankan Journal of Applied Statistics. 12, pp.13–25. DOI: http://doi.org/10.4038/sljastats.v12i0.4965
Published on 02 Dec 2012.
Peer Reviewed

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