UBC Theses and Dissertations
Probability of failure analysis and condition assessment of cast iron pipes due to internal and external corrosion in water distribution systems Yaminighaeshi, Hamidreza
Corrosion of cast iron pipes in distribution systems can lead to the development of corrosion pits that may reduce the resistance capacity of the pipe segment, resulting in mechanical failure. These pipes have a tendency to corrode externally and internally under aggressive environmental conditions. The mechanical failure of pipes is mostly the result of this structural weakening coupled with externally, environmental, and internally, operational, imposed stresses. While external corrosion has been shown to significantly affect the likelihood of mechanical failure, the risk of failure may be further heightened if internal corrosion is occurring. This thesis develops a methodology for estimating the probability of mechanical failure of cast iron pipes due to internal corrosion that incorporates the relationship between chlorine consumption and the rate of internal corrosion in a cast iron pipe. A probability analysis is developed that incorporates the internal corrosion model as well as the Two-phase nonlinear external corrosion model to calculate the overall probability of mechanical failure. Monte Carlo Simulation (MCS), First Order Reliability Method (FORM)-, and Second Order Reliability Method (SORM)-based approaches are used to estimate the probability of mechanical failure. Next, a methodology is developed for analyzing pipe condition based on the data resulting from the probability of mechanical failure analysis incorporating internal and external corrosion. A modeling strategy inspired by survival analysis is used to obtain the predicted number of pipe breaks for a given exposure time. The likelihood of failure at a given residual pipe wall thickness is estimated and coupled with the predicted number of pipe breaks as surrogates for pipe condition. These condition indices may support decisions regarding replacement planning and can be coupled with economic assessment models in the development of future asset management strategies.
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