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

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.

Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

“…Any distribution can be used in a hyper-parameter model – which is best? He et al 27 use exponential distributions to estimate degradation of railway track while the gamma distribution, in the form of gamma processes, is used by Edirisinghe et al 28 to study the deterioration of building components. Studies of bridges 2 and railways 29 provide two examples showing the use of Weibull distributions to model a range of asset deterioration behaviours.…”

Health prognosis approach for manufacturing systems based on quality state task network

“…Parameters α j , η j , and φ i describing machine performance state are fitted based on reliability test data or actual historical data, and detailed methods and procedures are available in Vlok et al 29 The values for parameters υ i and θ i in gamma processes are derived by means of maximum likelihood. 30 The value for parameter τ j is obtained by the sum of the products of the proportion of each failure mode and the corresponding repair time based on historical data, and the values for the parameters of the process model are derived based on the response surface method, 31 as displayed in Table 1 and equations (36)–(38)…”

Bayesian network models for making maintenance decisions from data and expert judgment

“…Many distributions have been used: for example Guler et al (2011) and He et al (2013) use exponential distributions to estimate degradation of railway track. The gamma distribution, in the form of gamma processes, is used by Edirisinghe et al (2012) to study the deterioration of building components.…”