A novel stacking ensemble learner for predicting residual strength of corroded pipelines

Abstract

Abstract Accurately assessing the residual strength of corroded oil and gas pipelines is crucial for ensuring their safe and stable operation. Machine learning techniques have shown promise in addressing this challenge due to their ability to handle complex, non-linear relationships in data. Unlike previous studies that primarily focused on enhancing prediction accuracy through the optimization of single models, this work shifts the emphasis to a different approach: stacking ensemble learning. This study applies a stacking model composed of seven base learners and three meta-learners to predict the residual strength of pipelines using a dataset of 453 instances. Automated hyperparameter tuning libraries are utilized to search for optimal hyperparameters. By evaluating various combinations of base learners and meta-learners, the optimal stacking configuration was determined. The results demonstrate that the stacking model, using k-nearest neighbors as the meta-learner alongside seven base learners, delivers the best predictive performance, with a coefficient of determination of 0.959. Compared to individual models, the stacking model also significantly improves generalization performance. However, the stacking model’s effectiveness on low-strength pipelines is limited due to the small sample size. Furthermore, incorporating original features into the second-layer model did not significantly enhance performance, likely because the first-layer model had already extracted most of the critical features. Given the marginal contribution of model optimization to prediction accuracy, this work offers a novel perspective for improving model performance. The findings have important practical implications for the integrity assessment of corroded pipelines.