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Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating

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  • Jiawang Zhan

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Chuang Wang

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Zhiheng Fang

    (School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The condition of joints in steel truss bridges is critical to railway operational safety. The available methods for the quantitative assessment of different types of joint damage are, however, very limited. This paper numerically investigates the feasibility of using a probabilistic neural network (PNN) and a finite element (FE) model updating technique to assess the condition of joints in steel truss bridges. A two-step identification procedure is developed to achieve damage localization and severity assessment. A series of FE models with single or multiple damages are simulated to generate the training and testing data samples and validate the effectiveness of the proposed approach. The influence of noise on the identification accuracy is also evaluated. The results show that the change rate of modal curvature (CRMC) can be used as a damage-sensitive input of the PNN and the accuracy of preliminary damage localization can exceed 90% when suitable training patterns are utilized. Damaged members can be localized in the correct substructure even with noise contamination. The FE model updating method used can effectively quantify the joint deterioration severity and is robust to noise.

Suggested Citation

  • Jiawang Zhan & Chuang Wang & Zhiheng Fang, 2021. "Condition Assessment of Joints in Steel Truss Bridges Using a Probabilistic Neural Network and Finite Element Model Updating," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1474-:d:490615
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    References listed on IDEAS

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    1. X. T. Zhou & Y. Q. Ni & F. L. Zhang, 2014. "Damage Localization of Cable-Supported Bridges Using Modal Frequency Data and Probabilistic Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, June.
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    Cited by:

    1. Shaoyi Zhang & Yongliang Wang & Kaiping Yu, 2022. "Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges," Sustainability, MDPI, vol. 14(19), pages 1-18, September.

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