长江科学院院报 ›› 2018, Vol. 35 ›› Issue (8): 46-50.DOI: 10.11988/ckyyb.20170062

• 工程安全与灾害防治 • 上一篇    下一篇

基于SVM的混凝土坝变形监控模型预测能力实例分析

钱秋培1a,1b,1c,崔伟杰2,包腾飞1a,1b,1c,李慧1a,1b,1c   

  1. 1.河海大学 a.水文水资源与水利工程科学国家重点实验室;b.水资源高效利用与工程安全国家工程研究中心; c.水利水电学院,南京 210098;
    2.雅砻江流域水电开发有限公司,成都 610051
  • 收稿日期:2017-01-16 出版日期:2018-08-01 发布日期:2018-08-14
  • 通讯作者: 包腾飞(1974-),男,湖北黄冈人,教授,博士,博士生导师,主要研究方向为水工结构及岩土工程的安全监控、光纤传感器在结构健康监测中的应用。E-mail:baotf@hhu.edu.cn
  • 作者简介:钱秋培(1993-),女,江苏南通人,硕士研究生,研究方向为水工结构安全监控。E-mail:781633046@qq.com
  • 基金资助:
    国家自然科学基金项目(51579086,51479054,51379068,51139001);江苏省杰出青年基金项目(BK20140039);江苏高校优势学科建设工程项目(YS11001)

Case Analysis of the Prediction Ability of SVM-based Monitoring Model for Concrete Dam Deformation

QIAN Qiu-pei 1,2,3, CUI Wei-jie4, BAO Teng-fei 1,2,3, LI Hui 1,2,3   

  1. 1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;
    2. National Engineering Research Center of Water Resources Efficient Utilization and Engineering Safety, Hohai University, Nanjing 210098, China;
    3. College of Water Conservancy and Hydropower, Hohai University, Nanjing 210098, China;
    4. Yalong River Hydropower Development Company, Ltd., Chengdu 610051, China
  • Received:2017-01-16 Online:2018-08-01 Published:2018-08-14

摘要: 大坝变形与水位、温度、时效等较多因素非线性相关,支持向量机(Support Vector Machine, SVM)适用于小样本、非线性、高维学习问题,在大坝安全变形监控上具有很大的优越性。阐述了支持向量机的原理,介绍了应用SVM建立混凝土坝变形监控模型的步骤及其参数优化方法。针对预测样本数目的合理取值问题,通过实例分析,研究基于SVM的混凝土坝变形监控模型的预测能力。结果表明,基于SVM的混凝土坝变形监控模型短期预测能力优于长期预测能力,且其预测能力受预测集数目的影响大于算法优化的影响。因此,合理选择预测集数目对变形监控模型有效预测尤为重要。

关键词: 混凝土坝, 变形监控, SVM, 粒子群算法, 预测能力, 实例分析

Abstract: Dam deformation is nonlinearly correlated with water level, temperature, aging and many other factors. Support vector machine (SVM) is of great superiority in dam safety monitoring as it accommodates small sample, nonlinear and high dimensional learning problems. In this article, the principle of SVM is expounded, the procedures of building an SVM-based deformation monitoring model are summarized, and parameter optimization method is introduced as well. The prediction ability of the SVM-based monitoring model for concrete dam deformation is analyzed through a case study. Results demonstrate that the short term prediction ability of the model is better than its long term prediction ability; the prediction ability is affected by the number of prediction sets rather than by algorithm optimization. The results indicate that selecting an appropriate number of prediction sets is important to the validity of the model.

Key words: concrete dam, deformation monitoring, Support Vector Machine, particle swarm optimization, prediction ability, case analysis

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