Integrated External Corrosion Management (IECM) is a novel framework developed for pipeline operators to model, identify, and optimize external corrosion risk and costs using a data-driven approach. Over the last decade, Machine Learning (ML) has transformed industries from consumer technology to product design to industrial systems. In corrosion, the Association for Materials Protection and Performance (AMPP) has added a symposium for specialists designing and optimizing machine learning algorithms detection and management. This work is not about a specific algorithm or technology set. Instead, this work presents a framework for incorporating the output of a predictive algorithm with an IECM framework. This work considers the interplay between in-line inspection (ILI), direct assessment, close interval surveys, and mechanistic modeling. Lastly, this work describes an external corrosion management system that is fully "observable", an environment where the state of any component in a pipeline system can either be directly observed or inferred in near real-time.

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