中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (6): 109-114.doi: 10.16265/j.cnki.issn1003-3033.2017.06.019

• 安全工程技术科学 • 上一篇    下一篇

基于RS-SVM的城市埋地燃气管道外腐蚀情况评价

骆正山 教授, 王浩, 毕傲睿   

  1. 西安建筑科技大学 管理学院,陕西 西安 710055
  • 收稿日期:2017-03-13 修回日期:2017-05-05 发布日期:2020-10-16
  • 作者简介:骆正山 (1969—),男,陕西汉中人,博士,教授,主要从事系统工程理论与方法、管道风险评估理论、建模与方法、企业信息化等方面的研究。E-mail: luozhengshan@163.com。
  • 基金资助:
    国家自然科学基金资助(61271278);陕西省重点学科建设专项资助项目(E08001)。

Evaluation of external corrosion of urban buried gas pipeline based on RS-SVM

LUO Zhengshan, WANG Hao, BI Aorui   

  1. School of Management,Xi'an University of Architecture & Technology,Xi'an Shaanxi 710055,China
  • Received:2017-03-13 Revised:2017-05-05 Published:2020-10-16

摘要: 为提高城市埋地燃气管道外腐蚀情况评价的准确性,识别影响管道外腐蚀的主要因素,构建评价指标集,结合粗糙集(RS)与支持向量机(SVM)的优势,建立管道外腐蚀情况预测评价模型。给出具体评价步骤,包括收集样本数据、预处理数据、用属性约简算法筛选核心指标集、用SVM训练器训练数据,形成检验模型。以某条城市燃气管线为例进行实例验证和分析。结果表明:用 RS-SVM 模型预测评价管道的腐蚀等级与实际结果一致,传统方法预测管道腐蚀速率平均相对误差为14.1%,RS-SVM模型预测的平均相对误差为7.9%,较之传统方法精度更高。

关键词: 粗糙集(RS), 支持向量机(SVM), 埋地燃气管道, 外腐蚀, 评价

Abstract: To improve the accuracy of the evaluation of the city buried gas pipeline external corrosion, the main factors affecting pipeline corrosion were identified, an evaluation index set was constructed, combined with the advantages of SVM and RS, a model was built for prediction of external corrosion of city buried gas pipeline. Specific steps were given as follows: sample data collection, sample data preprocessing, attribute reduction algorithm to filtering core index set, and SVM training data and verifying model. A certain city gas pipeline was taken as an example to verify the model. The results show that the results of predicting the corrosion of pipeline level by using RS-SVM model are consistent with the actual results, the traditional multivariant regression method for prediction of pipeline corrosion rate has a 14.1% average relative error, while RS-SVM prediction model of pipeline corrosion rate has a 7.9% average relative error, the accuracy of RS-SVM model is higher than that of the traditional method.

Key words: rough set(RS), support vector machine(SVM), buried gas pipeline, external corrosion, evaluation

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