Machine Learning-Based Stress and Fatigue Prediction in Complex Piping Networks Recent Advances

Abstract

The pipelines of oil and gas are very important in the transmission of energy but constantly subjected to mechanical stress, fatigue, and corrosion in complex conditions of operation. Finite element analysis and other traditional methods of physics have good mechanical interpretability and can be computationally costly and incapable of stochastic loading, material uncertainties, and nonlinear system response. The paper is a review of the current developments in machine learning (ML) methods, such as artificial neural networks, convolutional neural networks, and Gaussian process models, as effective and high-fidelity methods to stress distribution and fatigue life prediction. With the combination of data-based learning and the basic principles of mechanics, ML-based approaches are efficient to approach nonlinear relations and multi-axial stress states on the basis of sensor measurements, past experiences, and numerical modeling. In addition, hybrid frameworks and digital twins’ technologies with the implementation of Bayesian inference are also discussed in terms of their ability to provide real-time structural health monitoring and predictive maintenance. These methods provide superior aptitude to ward off catastrophic breakdowns throughout industrial, marine, and water infrastructure piping infrastructure showing that the ML-based predictive modeling represent an efficient route to enhancing reliability, safety and lifecycle results of advanced piping networks.