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Structural system reliability analysis of cast iron water mains

Structural system reliability analysis of cast iron water mains

Mahmoodian, Mojtaba and Li, Chun Qing (2011) Structural system reliability analysis of cast iron water mains. In: 2nd Iranian Conference on Reliability Engineering, 24-26 Oct 2011, Tehran, Iran.

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Reliability analysis of infrastructure such as water main systems has become a vital need for engineers and decision makers to have an accurate prediction of service life of the system and assessing the factors that are affecting it most. In this paper, Monte Carlo simulation method will be used for reliability analysis and service life prediction of a pipeline system. As an extension to the previous works, two limit state functions are considered. It will be shown that how different individual failure modes affect the probability of system failure.

Sensitivity analysis is also carried out to show the effect of changing basic variables on the reliability and service life of the system. It will be concluded that the applied methodology can consider different failure modes for estimating service life of the system and it can also provide a reliable guide for rehabilitation and maintenance plans. In addition, the results of the system reliability analysis in this study can be useful for designing more reliable new pipeline systems.

Item Type: Conference or Conference Paper (Paper)
Additional Information: [1] This paper (Reli2011, No. 0451) was first presented at the 2nd Iranian Conference on Reliability Engineering (Reli2011) held from 24-26 October 2011 in Tehran, Iran.
Uncontrolled Keywords: reliability analysis, service life prediction, cast iron pipes, water mains, corrosion, Monte Carlo Simulation
Subjects: T Technology > T Technology (General)
T Technology > TD Environmental technology. Sanitary engineering
Pre-2014 Departments: School of Engineering
School of Engineering > Department of Civil Engineering
Related URLs:
Last Modified: 14 Oct 2016 09:27
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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