Failure prediction and optimal scheduling of replacements in asbestos cement water pipes

Abstract

Asbestos cement (AC) pipes are among the oldest assets in many water supply networks. With increasing failure rates and cost consequences, asset management tools are required to pre-empt unplanned failures and schedule future replacement at the most cost-effective time during service. This paper develops a physical probabilistic failure model for AC pipes under combined internal pressure and external loading. Uncertainty in the degradation process is accounted for using the Weibull extreme value probability distribution. Monte Carlo simulation is then used to estimate the probability of pipe failure as ageing proceeds. In the final stage of the model, a cost/benefit analysis is conducted to determine optimal scheduling of future inspection and replacement activities.The end result is a potentially useful asset management methodology in which both the probable physical lifetime and the economic lifetime of an AC pipe can be estimated. C t probable failure costs for new pipe asset at time t (AU$) d discount rate for future cash flows D goodness-of-fit test statistic F(s R ), F(t) cumulative probability distributions for degradation rate and pipe lifetime f(s R ), f(t) probability density functions for degradation rate and pipe lifetime F m bedding factor H soil cover depth (m) h(t) hazard (failure rate) function N number of trials in Monte Carlo simulation NPV net present value (AU$) P internal pressure of pipe (Pa) p c internal pressure required for failure with no external load (Pa) s standard deviation s 0 original, as-produced tensile strength (MPa) SEM standard error of mean s f residual tensile strength at failure (MPa) s R degradation (strength loss) rate (MPa/year) T MAX maximum physical lifetime of pipe (years) w external load (N m 21 ) w c external load required for failure with no internal pressure (N m 21 ) Z AC known cost of failure in existing pipe asset (AU$) Z NEW known cost of failure in new pipe asset (AU$) a scale parameter for Weibull extreme value probability distribution g soil unit weight (kN m 23 ) dt time step for analysis (years) h shape parameter for Weibull extreme value probability distribution

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