Research Repository

Prediction Models for Serviceability Deterioration of Stormwater Pipes

Tran, D. H and Ng, A. W. M and McManus, K and Burn, Stewart (2008) Prediction Models for Serviceability Deterioration of Stormwater Pipes. Structure and Infrastructure Engineering, 4 (4). pp. 287-295. ISSN 1573-2479

Full text for this resource is not available from the Research Repository.

Abstract

Reducing the number of annual blockages and the consequential flooding events is one of the most important tasks for stormwater pipe infrastructure managers in Australia. Blockages are more likely to occur with pipes experiencing serviceability deterioration, resulting in a reduction of hydraulic capacity. When changing from a problem-based approach to a proactive maintenance and rehabilitation (M&R) approach, the asset managers need predictive information on the serviceability condition of pipes in order to firstly prepare the necessary resources from limited annual budgets and, secondly, to allocate these resources for the maintenance of the deteriorated pipes as precisely as possible. This paper investigates the application of a Markov model and an ordinal regression model for predictions of serviceability deterioration of stormwater pipes. The first model provides the prediction at a network level, which satisfies the first requirement, and the second model predicts serviceability condition for individual pipes, given the attributes of the pipes, in order to satisfy the second requirement. Both models are calibrated using Bayesian inference and Markov Chain Monte Carlo (MCMC) simulation techniques on a dataset supplied from the City of Greater Dandenong, Australia.

Item Type: Article
Uncontrolled Keywords: ResPubID14866. stormwater pipes, pipe blockages, deterioration model, Bayesian, Markov Chain Monte Carlo simulation, MCMC, ordinal regression, pipe deterioration
Subjects: Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0905 Civil Engineering
SEO Classification > 9004 Water and Waste Services
Related URLs:
Depositing User: VUIR
Date Deposited: 09 Aug 2011 03:39
Last Modified: 10 Jan 2017 11:52
URI: http://vuir.vu.edu.au/id/eprint/4007
ePrint Statistics: View download statistics for this item
Citations in Scopus: 7 - View on Scopus

Repository staff only

View Item View Item

Search Google Scholar