期刊文献+

基于遗传算法的BP神经网络对海底管道受撞击损伤预测

Prediction of Submarine Pipeline Damage Based on Genetic Algorithm
收藏 分享 导出
摘要 人类频繁的海洋活动中难免发生重物落水事故,对海底管道造成撞击损伤,引起环境污染及经济损失。为保证管道在运行期间的安全性,有必要准确快速的对管道损伤进行预测以便为实际工程提供参考。BP神经网络常作为损伤预测的一种数学模型,但本身易陷入局部极小且预测精度较低。针对上述问题,本文提出了基于遗传算法的BP神经网络(GA-BP神经网络)损伤预测模型。利用有限元计算数据构成样本空间,对管道损伤进行预测,并将结果与BP神经网络、有限元计算的结果进行对比。分析表明:与BP神经网络相比,GA-BP神经网络的预测结果与有限元计算的结果较为接近,预测精度较高,其平均误差为1.27%,满足工程精度要求的同时又节省了计算时间。 With the increase of human activities on the ocean, it is inevitable that weights drop into the sea, which may lead to damages of the pipelines once impacted. The failure of pipelines may cause great financial losses and serious environment pollution. In order to ensure the safety of pipelines during operation period, it is necessary to predict the damage fast and accurately, which can provide reference for engineering practice. Back-propagation neural network(hereinafter BP neural network) is a common mathematic model to predict the pipeline damage. However, it is prone to plunge into local minimum which leads to errors. To solve the problem, this paper proposes a modified BP neural network model to predict the pipeline damage based on genetic algorithm(hereinafter GA-BP neural network). Sample space for the construction of the proposed model is consisted of the data by finite element method(hereinafter FEM). Subsequently, damage predictions are made and the results are compared with that by BP neural network and by FEM. The analysis indicates that, compared with the BP neural network, the GA-BP neural network has higher prediction accuracy and are more close to the FEM results. Besides, the average relative error is around 1.27%. Therefore, the engineering accuracy requirements can be satisfied and the considerable calculation time is saved.
作者 姜逢源 赵玉良 董胜 蒙占彬 JIANG Fengyuan;ZHAO Yuliang;DONG Sheng;MENG Zhanbin(College of Engineering, Ocean University of China, Qingdao 266100, China;School of Mechanical, Ship & Offshore Engineering, Beibu Gulf University,Qinzhou 535011, China)
出处 《海洋湖沼通报》 CSCD 北大核心 2019年第3期52-59,共8页 Transaction of Oceanology and Limnology
基金 国家重点研发计划(2016YFC0802301)资助.
关键词 海底管道 遗传算法 神经网络 撞击 submarine pipeline genetic algorithm neural network impact
作者简介 第一作者:姜逢源(1992-),男,硕士研究生,主要从事海岸及海洋工程研究。E-mail:jiangfy_ouc@163.com;通讯作者:蒙占彬(1979-),男.博士,高级工程师。E-mail:zjymzb@163.com.
  • 相关文献

参考文献10

二级参考文献44

  • 1刘爱文,胡聿贤,赵凤新,李小军,高田至郎,赵雷.地震断层作用下埋地管线壳有限元分析的等效边界方法[J].地震学报,2004(S1):141-147. 被引量:34
  • 2刘颖,谢世楞.关于直立式防波堤分项系数的确定[J].港工技术,1993(4):11-17. 被引量:10
  • 3白金泽.LS-DYNA3D基础理论与实例分析[M].北京:科学出版社,2005. 被引量:10
  • 4Det Norske Veritas, DNV-OS-F101. Rules for Submarine Pipeline Systems[S]. 2003. 被引量:1
  • 5Det Norske Veritas, DNV-RP-F107. Risk Assessment of Pipelines Protection [S]. 2002. 被引量:1
  • 6Gu Xiaoyun, Gao Fuping. Pu Qun. Wave-soil-pipe coupling effect upon submarine pipeline on-bottom stability [J]. Acta Mechanica Sinica (English Series), 2001, 17(1): 86-96. 被引量:1
  • 7Jeng D S. Numerical modeling for wave-seabed-pipe interaction in a non-homogeneous porous seabed [J]. Soil Dynamics and Earthquake Engineering, 2001, 21(8): 699-712. 被引量:1
  • 8Katteland L H, Oygarden B. Risk analysis of dropped objects for deep water development [C]. Proceedings of the 14th International Conference on Offshore Mechanics and Arctic Engineering. New York, USA, ASME, 1995: 276-279. 被引量:1
  • 9Moan T, Karsan D, Wilson T. Analytical risk assessment and risk control of floating platforms subjected to ship collision and dropped objects [C]. Proceedings of the 25th Annual Offshore Technology Conference. Huston USA, AIME, 1993: 407-418. 被引量:1
  • 10Legeron F, Paultre P. Uniaxial confinement model for normal and high-strength concrete columns [J]. Journal of Structural Engineering, 2003, 129(2): 241 - 252. 被引量:1

共引文献132

投稿分析

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部 意见反馈