With rapid economic development in China’s coastal regions, more oil stations are being built on soft soil foundations, facing risks such as foundation settlement and pipeline failures. Mechanical vibrations of oil pumps can induce resonance in pipelines, leading to rupture, leakage, and fire or explosion, threatening both safety and sustainable operation. Traditional monitoring methods, relying on physical models or data-driven approaches alone, are limited in capturing these coupled risks. This study proposes an ABC-XGBoost hybrid risk warning model, where the artificial bee colony algorithm optimizes XGBoost hyperparameters (iteration number, tree depth, learning rate) to improve predictive accuracy. By using multidimensional data—such as internal pressure, vibration amplitude, and ground settlement—the model evaluates stress and resonance risks in real time, supporting sustainable safety management. Validation with real station data shows an accuracy of 95.22%, 2.61% higher than the unoptimized model, demonstrating effective early warning and contribution to sustainable pipeline operation.
Yu et al. (2026) studied this question.