Predicting Axial Force and Bending Moment in Pipelines Affected by Geohazard Using Machine Learning Techniques

Abstract

Pipelines are vital to the safe and efficient transportation of energy resources, playing a critical role in meeting global energy demands and supporting economic stability. However, these critical infrastructures face significant risks from geohazards, particularly landslides, which can lead to sudden ground displacement and severe damage to pipelines. Such events not only compromise the structural integrity of pipelines but also pose environmental, economic, and public safety risks. Understanding the effects of landslides on pipeline design and safety is essential to developing robust strategies for mitigating these risks and ensuring the reliable transport of energy resources under challenging geohazard-induced conditions. To address these challenges, this research focuses on predicting the structural responses of pipelines, including axial force and bending moment, under geohazard-induced conditions, such as landslides. Employing machine learning models, this study aims to provide a robust and efficient alternative to numerical methods. Specifically, Support Vector Regression (SVR), Neural Networks, and Random Forest models are developed and systematically evaluated for their ability to predict these responses, offering insights into the performance and applicability of each technique. The dataset used in this study was generated through Python-based numerical simulations, leveraging theoretical models grounded in the Euler-Bernoulli beam theory. Parameters such as axial displacement (u′), lateral displacement (v′), and curvature (v′′) were sampled over ranges reflective of real-world pipeline deformation scenarios. This comprehensive dataset captures a realistic spectrum of elastic, plastic, and strain-hardening behaviours, ensuring accurate modelling of pipeline responses under diverse loading scenarios. The generated dataset was used to train and evaluate the machine learning models, ensuring a comprehensive representation of diverse pipeline deformation scenarios. Model performance was assessed through key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Coefficient of determination (R²), alongside computational efficiency metrics such as training times. These metrics and comparisons were crucial in verifying that the models did not overfit or underfit the data, ensuring their ability to generalize effectively across unseen scenarios and diverse geohazard-induced conditions. Recall performance and trend comparison were conducted to evaluate the models’ consistency and their ability to generalize across diverse scenarios. The recall comparison assessed the efficiency of each model in sequential and batch tasks, providing insights into their suitability for different operational requirements. Trend analysis examined the models' ability to capture theoretical relationships between input parameters and pipeline responses, validating their alignment with established frameworks. The results demonstrated that Neural Networks provided the best balance of accuracy and computational efficiency, achieving high R² values (0.999 for axial force and 0.997 for bending moment) and moderate training times (37 seconds for axial force and 13 seconds for bending moment). SVR exhibited the highest R² values (0.999 for axial force and 0.996 for bending moment), indicating exceptional predictive accuracy; however, this came at the cost of significantly higher training times, particularly for bending moment predictions (3473 seconds). Random Forest, while computationally efficient in sequential recall tasks, lagged in predictive accuracy (R² values of 0.992 for axial force and 0.983 for bending moment) and struggled to capture complex trends, limiting its applicability to the studied scenarios. This study is subject to several limitations. The dataset was generated using numerical simulations based on predefined parameter ranges, which may not fully capture the variability of real-world pipeline deformation scenarios. Additionally, the reliance on synthetic data and the lack of validation against experimental or field data limit the ability to confirm the models’ robustness in practical applications. This research opens several avenues for future studies. Expanding the range of input parameters, such as u′, v′, and v′′, could enhance the generalizability of the predictive models, allowing them to handle a wider variety of deformation scenarios. Customizing material and geometric properties, such as pipe diameter, wall thickness, and soil characteristics, would provide deeper insights into the influence of these factors on axial force and bending moment predictions. Additionally, validating the findings with real-world data, instead of relying solely on synthetic datasets, would test the robustness of the models under practical conditions and increase their applicability to real-world engineering challenges. These efforts could further refine the models and broaden their relevance in pipeline safety and reliability studies