Strategic Foundations for AI Adoption in Pipeline Corrosion Control: Standardizing and Automating CIPS Data Interpretation (C2026-00015)

Hongbo Ding

The application of Artificial Intelligence (AI) in pipeline external corrosion control and integrity management presents a significant opportunity to improve efficiency and consistency, especially in the context of the Close-Interval Potential Survey (CIPS). CIPS is a government-regulated technique mandated by the Pipeline and Hazardous Materials Safety Administration (PHMSA), which oversees approximately 3 million miles of regulated pipeline infrastructure in the United States. The survey involves collecting pipe-to-soil potential measurements at 1–2 meter intervals along the pipeline right-of-way, producing large volumes of data. Despite its widespread use, CIPS data interpretation remains non-standardized, labor-intensive, and dependent on a shrinking pool of experienced corrosion engineers—making it a strong candidate for AI-driven solutions. However, effective AI implementation depends on the availability of high-quality training data: CIPS profiles with accurate measurements, clearly marked features, and expert interpretations. This paper explores the foundational elements that must be addressed to develop reliable AI models, emphasizing often-overlooked fundamentals in data collection and interpretation. By laying this groundwork, the pipeline industry can move toward automated, AI-assisted analysis and actionable insights based on raw CIPS data, leading to more efficient and reliable external corrosion management practices.