摘要：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.

关键字：artificial bee colony algorithm; soft soil foundation station; risk early warning; XGBoost algorithm; sustainable operation

ISSN号：无

卷、期、页：卷: 18期: 5

发表日期：2026-03-09

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,SSCI（社会科学引文索引）,SCIE（科学引文索引网络版）

发表期刊名称：SUSTAINABILITY

通讯作者：禹胜阳,冯祥淞,陈丽文

第一作者：徐晴晴,董绍华

论文类型：期刊论文

论文概要：禹胜阳,冯祥淞,陈丽文,徐晴晴,董绍华，Intelligent Risk Early Warning Model for Coupling Risk of Oil Pump Pipeline System in Station Under Soft Soil Foundation Conditions Based on ABC-XGBoost Algorithm，SUSTAINABILITY，2026，卷: 18期: 5

论文题目：Intelligent Risk Early Warning Model for Coupling Risk of Oil Pump Pipeline System in Station Under Soft Soil Foundation Conditions Based on ABC-XGBoost Algorithm