Machine Learning Augmentation of the Failure Assessment Diagram Methodology for Enhanced Tubular Structures Integrity Evaluation

Abstract

Failure assessment diagrams (FADs) are essential engineering tools for evaluating the structural integrity of components. However, their widespread application can be limited by complexity and computational expense. This study presents a novel machine learning-based approach to streamline FAD analysis, offering accuracy and efficiency while overcoming these limitations. The approach integrates numerical contour integral-based FADs with artificial neural networks (ANNs). To ensure reliable material modeling for the Finite Element Analysis (FEA) used to generate J-integral based FADs that train the ANNs, careful experimental and numerical procedures were employed. This involved uniaxial tensile tests, an iterative method for obtaining precise true stress–strain curves, and a Ramberg–Osgood material model for accurate material behavior representation. The ANNs themselves not only analyze large datasets to generate precise FAD envelopes but also predict limit loads and the Φ parameter, incorporating the effect of residual stress on the FAD methodology. To verify and test the proposed method, hypothetical fitness-for-service assessment cases were conducted, incorporating experimental residual stress measurements from split-ring tests on P110 and L80 pipes. These assessments were compared to both traditional FAD methods and computationally intensive FEA-based FADs. Results demonstrate a closer agreement with FEA-based calculations than traditional methods provided in engineering standards. Ultimately, this work provides a rather innovative and adaptable approach for structural integrity evaluations and critical engineering assessments through the proposal of an ANN enhanced FAD approach, simplifying these calculations while maintaining high fidelity.