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Research on the Damage Diagnosis Model Algorithm of Cable-Stayed Bridges Based on Data Mining

Author

Listed:
  • Jie Liu

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    Hebei Key Laboratory of Diagnosis, Reconstruction and Anti-Disaster of Civil Engineering, Zhangjiakou 075000, China
    Innovation Center for Wind Engineering and Wind Energy Technology of Hebei Province, Shijiazhuang 050043, China
    School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Han Cheng

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Qingkuan Liu

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    Innovation Center for Wind Engineering and Wind Energy Technology of Hebei Province, Shijiazhuang 050043, China
    School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Hailong Wang

    (Hebei Key Laboratory of Diagnosis, Reconstruction and Anti-Disaster of Civil Engineering, Zhangjiakou 075000, China
    Hebei Innovation Center of Transportation Infrastructure in Cold Region, Zhangjiakou 075000, China)

  • Jianqing Bu

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

Abstract

To obtain an effective data mining method for cable-stayed bridge damage diagnosis, the algorithm of the cable-stayed bridge damage diagnosis model based on data mining was studied, and a data mining method is proposed. This method is oriented to the damage diagnosis of cable-stayed bridges. After algorithm comparison, the support vector machine (SVM) and limit gradient-boosting (XGBoost) algorithms, with advantages in damage location and quantification, are combined and optimized to obtain the damage diagnosis model for cable-stayed bridges. First, a refined benchmark finite element model is established by Abaqus, and postprocessing data such as vibration frequency and modal curvature are used as a data mining dataset. Second, feature se-lection is conducted, and the damage-sensitive modal curvature change rate index is selected as the feature of data mining. Next, the SVM and XGBoost algorithms are optimized by grid and random search, and the optimized SVM and XGBoost algorithms are used to locate and quantify the damage. Finally, the damage diagnosis model for cable-stayed bridges is obtained. Taking a cable-stayed bridge as an example, the proposed method is applied and analyzed, and the results show the effectiveness of the proposed method.

Suggested Citation

  • Jie Liu & Han Cheng & Qingkuan Liu & Hailong Wang & Jianqing Bu, 2023. "Research on the Damage Diagnosis Model Algorithm of Cable-Stayed Bridges Based on Data Mining," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2347-:d:1048567
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    References listed on IDEAS

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    1. Dan Ling & Chaosong Li & Yan Wang & Pengye Zhang, 2022. "Fault Detection and Identification of Furnace Negative Pressure System with CVA and GA-XGBoost," Energies, MDPI, vol. 15(17), pages 1-19, August.
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