RMIT University
Browse

Probabilistic physical modelling of corroded cast iron pipes for lifetime prediction

journal contribution
posted on 2024-11-02, 01:11 authored by Jian Ji, Dilan RobertDilan Robert, Chunsun Zhang, David Zhang, Jayantha Kodikara
Cast iron was the dominant material for buried pipes for water networks prior to the 1970s in Australia and overseas. At present, many water utilities still have a significant amount of ageing cast iron pipes. Cast iron is a brittle material and when large diameter cast iron pipes (diameters above 300 mm) further deteriorate, the consequences of failure can be substantial. Focusing on the likelihood of failure to assist risk assessment, this paper examines the performance of large-diameter cast iron pipes using probabilistic analysis, incorporating uncertainties of governing variables. Finite element analysis is first conducted to study the physical mechanism of buried pipes subjected to complex environmental conditions. The deterioration of cast iron pipes due to corrosion is considered on the basis of recent research. The uncertainties of governing variables, such as the physical properties of soil, cast iron, water pressure and corrosion patterns, in pipe failure risk assessment are considered. Using probabilistic physical modelling, the lifetime probability of failure is derived and a time-dependent sensitivity analysis is presented. The results of this probabilistic physical modelling are compared with cohorts of failure data from two Australian water utilities to examine the underlying trends from both physical modelling and statistical analysis.

History

Related Materials

  1. 1.
    DOI - Is published in 10.1016/j.strusafe.2016.09.004
  2. 2.
    ISSN - Is published in 01674730

Journal

Structural Safety

Volume

64

Start page

62

End page

75

Total pages

14

Publisher

Elsevier

Place published

Netherlands

Language

English

Copyright

© 2016 Elsevier

Former Identifier

2006067621

Esploro creation date

2020-06-22

Fedora creation date

2017-02-23

Usage metrics

    Scholarly Works

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC