In the oil industry, underground pipelines are the most preferred means of transporting a large amount of liquid product. However, a considerable number of unforeseen incidents due to corrosion failure are reported each year. Since corrosion in underground pipelines is caused by physicochemical interactions between the material (steel pipeline) and the environment (soil), the assessment of soil as a corrosive environment is indispensable.;Because of the complex characteristics of soil as a corrosion precursor influencing the dissolution process, soil cannot be explained fully by conventional semi-empirical methodologies defined in controlled settings. The uncertainties inherited from the dynamic and heterogeneous underground environment should be considered.;Therefore, this work presents the unification of direct assessment of soil and in-line inspection (ILI) with a probabilistic model to categorize soil corrosion. To pursue this task, we employed a model-based clustering analysis via Gaussian mixture models. The analysis was performed on data collected from southeastern Mexico. The clustering approach helps to prioritize areas to be inspected in terms of underground conditions and can improve repair decision making beyond what is offered by current assessment methodologies.;This study also addresses two important issues related to in-situ data: missing data and truncated data. The typical approaches for treating missing data utilized in civil engineering are ad hoc methods. However, these conventional approaches may cause several critical problems such as biased estimates, artificially reduced variance, and loss of statistical power. Therefore, this study presents a variant of EM algorithms called Informative EM (IEM) to perform clustering analysis without filling in missing values prior to the analysis. This model-based method introduces additional cluster-specific Bernoulli parameters to exploit the nonuniformity of the frequency of missing values across clusters.;In-line inspection tools (ILI) are commonly used for pipeline defect detection and characterization with advanced technologies such as magnetic flux leakage (MFL) and ultrasonic tools (UT). Each technology has its limitation for minimum detectable defect size. As a result, the data measured by different technologies are difficult to compare under the same modeling framework. In the present study, this problem will be addressed, considering two datasets measured by MFL and UT. Moreover, a truncated generalized exponential (TGE) distribution is introduced to describe the observed data. The non-informative Jeffreys' prior is used to establish the Bayesian updating algorithm, and the Markov chain Monte Carlo (MCMC) method is adopted to estimate the posterior distribution of model.
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