Available at: https://digitalcommons.calpoly.edu/theses/2016
Date of Award
MS in Civil and Environmental Engineering
Civil and Environmental Engineering
This study compared four different water main failure prediction models: a statistically simple model, a statistically complex model, a statistically complex model with modifications termed the 2019 model, and an age-based model. The statistically complex models compute the probability of failure based on age, size, internal pressure, length of pipe in corrosive soil, land use, and material of the. These two values are then used to prioritize a water main rehabilitation program to effectively use the municipality’s funds. The 2019 model calculates the probability of failure and consequence of failure differently than the statistically complex model by considering corrosive soil data instead of assuming all the pipes are in highly corrosive soil and average daily traffic volume data instead of using street classifications. The statistically simple model only uses the pipe age and material for probability of failure. The age-based model relies purely on the age of the pipe to determine its probability of failure. Consequences of failure are determined by the proximity of the pipe to highly trafficked streets, critical services, pipe replacement cost, and the flow capacity of the pipe. Risk of failure score is the product of the consequence of failure score and probability of failure score. Pipes are then ranked based on risk of failure scores to allow municipalities to determine their pipe rehabilitation schedule.
The results showed that the statistically complex models were preferred because results varied between all four models. The 2019 model is preferred for long-term analysis because it can better account for future traffic growth using the average daily traffic volume. Corrosive soil data did not have a significant impact on the results, which can be attributed to the relatively small regression parameter for corrosive soil. The age-based model is not recommended because results of this study shows it places a significantly high number of pipes in the high and critical risk categories compared to the other models that account for more factors. This could result in the unnecessary replacement of pipes leading to an inefficient allocation of funds.
Keywords: Risk of Failure, Consequence of Failure, Probability of Failure