Probabilistic physical modelling of corroded cast iron pipes for lifetime prediction

被引:72
作者
Ji, Jian [1 ]
Robert, D. J. [2 ]
Zhang, Chunshun [1 ]
Zhang, David [3 ]
Kodikara, Jayantha [1 ]
机构
[1] Monash Univ, Dept Civil Engn, Clayton, Vic 3800, Australia
[2] RMIT Univ, Sch Civil Environm & Chem Engn, Melbourne, Vic 3000, Australia
[3] Sydney Water, 210 William Holmes St, Potts Hill, NSW 2143, Australia
基金
美国国家科学基金会;
关键词
Pipeline failure; Stress prediction; Corrosion; Physical modelling; Probabilistic analysis; PITTING CORROSION; RELIABILITY ASSESSMENT; UNDERGROUND PIPELINES; SERVICE LIFE; STRESSES; OIL;
D O I
10.1016/j.strusafe.2016.09.004
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:62 / 75
页数:14
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