A predictive model for pitting corrosion in buried pipelines is proposed. The model takes into consideration the chemical and physical properties of the soil and pipe to predict the time dependence of pitting depth and rate. Maximum pit depths were collected together with soil and pipe data at more than 250 excavation sites over a three-year period. The time dependence of the maximum pit depth was modeled as dmax = κ(t − t0)ν, where t is the exposure time, t0 is the pit initiation time, and κ and ν are the pitting proportionality and exponent parameters, respectively. A multivariate regression analysis was conducted with dmax as the dependent variable and the pipeline age, and the soil and pipe properties as the independent variables. The dependence of κ and ν on the predictor variables was found for the three soil textural classes identified in this study: clay, clay loam, and sandy clay loam. The proportionality parameter κ was found to be primarily influenced by the redox potential, pH value, soil resistivity, and the dissolved ion concentrations. In contrast, the pitting exponent ν was found to be influenced mainly by the pipe-to-soil potential, water content, bulk density, and the pipe coating type. A real-life pipeline integrity assessment is used as a case study to illustrate the application of the proposed model and to show how it can have a positive impact on integrity management programs.

The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth) Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorov's forward equations is used to describe the transition probability function in a discrete pit depth space. The transition probability function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorov's system of equations is also found for the transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.

Recently, the authors proposed a new predictive model for pitting corrosion in underground pipelines. The model is based on field measurements of maximum pitting corrosion depth together with local soil and pipeline characteristics. The pitting corrosion data collection was conducted over a three-year period, for onshore buried pipelines operating in southern Mexico. This technical note contains a detailed description of the results of the field measurements, indicating the data entries classified as outlier observations and the textural soil class ascribed to each data entry.