Predictive deep learning for pitting corrosion modeling in buried transmission pipelines
Behnam Akhlaghi, Hassan Mesghali, Majid Ehteshami, Javad Mohammadpour, Fatemeh Salehi, Rouzbeh Abbassi
Abstract
Despite significant efforts and investments in the renewable energy sector, fossil fuels continue to provide the majority of the world's energy supply. Transmission pipelines, which are extensively used in the oil and gas industry, are vulnerable to various failure mechanisms, such as corrosion. Among these, pitting corrosion in offshore pipelines is the most prevalent type of external corrosion. This study explores the potential of deep learning models (Generalization and Generalization-Memorization models) to predict the maximum depth of pitting corrosion in oil and gas pipelines. The models are trained considering various characteristics of the soil where the pipe is buried and different types of the protective coating of the pipes. The application of deep neural networks resulted in a mean squared error of prediction of 0.0055 in training data and 0.0037 in test data. These results demonstrate that deep learning models outperform all empirical and hybrid models applied in previous studies on the same dataset. The proposed model in this study has the potential to predict failure rates of the pipelines due to external corrosion and enhance the safety and reliability of these facilities.