首页|New Machine Learning Data Have Been Reported by Investigators at Xi'an Shiyou University (Addressing the Inspection Selection Challenges of In-service Pipeline Weld Ensemble Tree Models)
New Machine Learning Data Have Been Reported by Investigators at Xi'an Shiyou University (Addressing the Inspection Selection Challenges of In-service Pipeline Weld Ensemble Tree Models)
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NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news reporting originating from Xi'an, People's Republic of China, by NewsRx editors, the research stated, “Natural gas transportation predominantly utilizes pipelines, and the safety of these systems largely hinges on the integrity of girth welds. However, once pipelines are buried post-construction, pinpointing weld inspection sites becomes complex.” Financial supporters for this research include Xianyang Science and Technology Bureau, Graduate Innovation and Practical Ability Cultivation Program of Xi'an Shiyou University. Our news editors obtained a quote from the research from Xi'an Shiyou University, “Presently, weld risk assessments rely on costly and specialized inspections. Hence, enhancing the accuracy in identifying defective welds and discovering superior inspection techniques is paramount. Our research employed machine learning, specifically an ensemble model of RandomForest and CatBoost, to prioritize weld inspections. After analyzing the West-East Gas Pipeline Girth Weld Dataset and addressing data imbalances using the SMOTE technique, we refined the model parameters. The culminating model yielded an F1-score of 0.815 and an average accuracy of 0.836, outperforming standalone models. Employing the SHAP method improved the model's transparency, facilitating a more informed decision-making process regarding pipeline inspections.”
Xi’anPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningXi’an Shiyou University
NETL
NSTL
Elsevier
