Prediction of Asbestos Cement Water Pipe Aging and Pipe Prioritization Using Monte Carlo Simulation

  • Wonsiri Punurai Mahidol University
  • Paul Davis CSIRO Urban Water Research


For buried Asbestos cement (AC) pipes in service, internal and external surface degradation occur by dissolution or leaching of cement-based components leading to loss of pipe strength. Since water quality and soil environment cannot be completely specified along a pipeline, a management methodology for AC water pipelines is required to estimate the probability of pipe failure as ageing proceeds. The paper describes the technique and its application to experimental data, which illustrates in three parts. First, the degradation rates in AC pipes are computed from 360 aggregated independent pipe segments residual strength test data taken from different pipe diameter sizes used in various water utilities locations in Thailand. Second, the predictions of service lifetime for AC pipes are estimated using Monte Carlo simulation in conjunction with the physical failure state formulations. Output from the simulation provides a number of failures recorded over time, which then allows the economic analysis for optimal pipe replacement scheduling. All is described in the third part. The end results can be used for water utilities to allocate government funds for future pipe maintenance activities.

Author Biographies

Wonsiri Punurai, Mahidol University
Department of Civil and Environmental Engineering, Mahidol University, Nakorn pathom, Thailand
Paul Davis, CSIRO Urban Water Research
CSIRO Urban Water Research, 37 Graham Road, Highett, Victoria 3190, Australia
How to Cite
PUNURAI, Wonsiri; DAVIS, Paul. Prediction of Asbestos Cement Water Pipe Aging and Pipe Prioritization Using Monte Carlo Simulation. Engineering Journal, [S.l.], v. 21, n. 2, p. 1-13, mar. 2017. ISSN 0125-8281. Available at: <>. Date accessed: 24 may 2017. doi:
Environment, Energy and Natural Resources


Water pipe aging, probability of failure, pipe maintenance, cement-based materials, strength, Monte Carlo method.

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