Featured Application: The proposed framework introduces a new feature matching approach for corroded pipelines based on in-line inspections and data clustering, contributing to the broader field of pipeline integrity management. The effectiveness of this framework suggests potential for application in other domains that benefit from spatial feature matching. Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique—directional epsilon neighborhood clustering (DENC)—is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management.

Corrosion is a leading cause of pipeline failures, responsible for up to a third of reported incidents. Given the extensive length and limited accessibility of pipelines, operators rely on frequent in-line inspections (ILIs) to detect and quantify corrosion defects. Feature matching between successive ILIs is therefore utilized to align and compare this data, enabling the identification of corrosion evolution and behavior. However, measurement uncertainties, nonuniform corrosion growth, and spatial interactions between adjacent defects pose significant challenges in achieving accurate matching. This study proposes a two-phase feature matching model designed to address these limitations. The first phase performs Iterative Closest Point (ICP) alignment with a context-aware nearest neighbor selection strategy based on Directional Epsilon Neighborhood Clustering (DENC) to isolate stable feature pairs and minimize ambiguous associations in densely clustered defect regions. The second phase applies a novel proximity–overlap-informed correspondence optimization using linear programming to identify matches and outliers by jointly considering feature positioning and geometric attributes. The model’s effectiveness is evaluated on a 1116 m subsea pipeline segment involving two consecutive inspections reporting 1305 and 1491 features, respectively. Compared to three state-of-the-art models, the proposed approach achieves a recall, precision, and F1 score of 99.2%, demonstrating substantial improvements in accuracy, stability, and robustness to inspection and corrosion-related uncertainties. These results confirm the model’s ability to address critical limitations in existing approaches and underscore its potential to enhance pipeline integrity assessments.

Accurate and efficient evaluation of the intersection area between two axis-aligned ellipses is essential in applications where the coordinate system or underlying geometry naturally imposes alignment. However, most existing numerical integration techniques are designed for arbitrarily oriented ellipses, and their generality typically requires adaptive refinement or solving higher-degree algebraic intersection formulations, leading to greater computational cost than necessary in the axis-aligned case. This study introduces two analytically derived, fixed-cost Gauss–Legendre quadrature formulations for computing the intersection area in the axis-aligned configuration. The first is a sine-mapped Gauss–Legendre quadrature, which applies a trigonometric transformation to improve conditioning near endpoint singularities while retaining constant-time evaluation. The second is an enhanced two-panel affine-normalized formulation, which splits the intersection domain into two sub-intervals to increase local accuracy while maintaining a fixed computational cost. Both methods are benchmarked against adaptive Simpson integration, polygonal discretization, and Monte Carlo sampling over 10,000 randomly generated ellipse pairs. The two-panel formulation achieves a mean relative error of 0.003% with runtimes more than twenty times faster than the adaptive reference and remains consistently more efficient than the polygonal and Monte Carlo approaches while exhibiting comparable or superior numerical behavior across all tested regimes.

Corrosion is responsible for up to one-third of reported pipeline incidents. Given the extensive length and limited accessibility of pipelines, operators rely on in-line inspections (ILIs) to detect and quantify corrosion defects. Although modern ILI tools provide detailed geometric descriptions of corrosion behavior, translating successive inspections into reliable growth models remains challenging. Measurement uncertainty, defect coalescence, and the joint evolution of length, width, and depth complicate learning consistent growth patterns. This study proposes a unified framework for automated corrosion growth modeling that integrates feature matching, feature engineering, and data-driven prediction into a single workflow. The framework targets defect-level modeling by jointly learning multi-dimensional corrosion growth of length, width, and depth, and by reducing correspondence-induced artifacts arising from defect coalescence and measurement uncertainty. The effectiveness of the framework was evaluated using three consecutive ultrasonic testing inspections of an API 5L X52 offshore water-injection pipeline segment. Extreme Gradient Boosting (XGBoost), a feed-forward neural network, Random Forest, and linear regression were used to assess the stability of the proposed geometric representation across learning paradigms. Integrity-oriented evaluation using Estimated Repair Factor (ERF) indicates that integrity-relevant trends are preserved. Furthermore, geometry-oriented evaluation using agreement and error analyses indicates strong performance for axial length and circumferential width, with modestly reduced performance for depth. Overall, the results suggest that the proposed framework provides a practical basis for defect-level automated corrosion growth modeling.