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Multidamage Detection of Bridges Using Rough Set Theory and Naive-Bayes Classifier

机译：基于粗糙集理论和朴素贝叶斯分类器的桥梁多损伤检测

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摘要

This paper is intended to introduce a two-stage detection method to solve the multidamage problem in bridges. Vibration analysis is conducted to acquire the dynamic fingerprints which are regarded as information sources. Bayesian fusion is used to integrate these sources and preliminarily locate the damage. Then, the RSNB method which combines rough set theory and Naive-Bayes classifier is proposed to simplify the sample dimensions and fuse the remaining attributes for damage extent detection. A numerical simulation of a real structure, the Sishui Bridge in Shenyang, China, is conducted to validate the effectiveness of the proposed detection method. Data fusion based method is compared with single-valued index method at the damage localization stage. The proposed RSNB method is compared with the Back Propagation Neural Network (BPNN) method at the damage qualification stage. The results show that the proposed two-stage damage detection method has better performances in regard to transparency, accuracy, efficiency, noise robustness, and stability. Furthermore, an ambient excitation modal test was carried out on the bridge to obtain the vibration responses and assess the damage condition with the proposed method. This novel approach is applicable for early damage detection and provides a basis for bridge management and maintenance.
机译：本文旨在介绍一种两阶段检测方法来解决桥梁中的多损伤问题。进行振动分析以获取被视为信息源的动态指纹。贝叶斯融合被用来整合这些来源并初步定位破坏。然后，提出了一种结合粗糙集理论和朴素贝叶斯分类器的RSNB方法，以简化样本维数，融合剩余属性进行损伤程度检测。进行了真实结构的数值模拟，即中国沉阳的四水桥，以验证所提出的检测方法的有效性。在损伤定位阶段，将基于数据融合的方法与单值索引方法进行了比较。在损伤鉴定阶段，将所提出的RSNB方法与反向传播神经网络（BPNN）方法进行了比较。结果表明，所提出的两阶段损伤检测方法在透明性，准确性，效率，噪声鲁棒性和稳定性方面具有更好的性能。此外，在桥梁上进行了环境激励模态试验，以获取振动响应并通过提出的方法评估损伤情况。这种新颖的方法适用于早期损坏检测，并为桥梁管理和维护提供了基础。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|6752456.1-6752456.13|共13页
  • 作者

    Sun Shuang; Liang Li; Li Ming; Li Xin;

  • 作者单位

    Northeastern Univ, Sch Resources & Civil Engn, Shenyang 110819, Liaoning, Peoples R China;

    Northeastern Univ, Sch Resources & Civil Engn, Shenyang 110819, Liaoning, Peoples R China;

    Northeastern Univ, Sch Resources & Civil Engn, Shenyang 110819, Liaoning, Peoples R China;

    Northeastern Univ, Sch Resources & Civil Engn, Shenyang 110819, Liaoning, Peoples R China;

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