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Deep Analysis of Unbalanced Offline Data in Bridge Monitoring Based on Improved Upper Confidence Bound Algorithm

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  • Lei Fu

    (Guizhou Communications Construction Group Co., Ltd., China)

  • Hongju Han

    (Guizhou Communications Construction Group Co., Ltd., China)

Abstract

The continuous advancement of infrastructure construction and the increase in transportation pressure have caused great damage to bridge structures. Monitoring bridge structures can promptly detect potential structural damage. Therefore, this study proposes an unbalanced offline data monitoring method based on an improved upper confidence bound algorithm, aiming to provide strong support for bridge safety assessment. This study first conducts a deep analysis of the unbalanced offline data of the fusion of multi-armed bandit algorithm and upper confidence bound algorithm, then constructs an improved model, and finally analyzes the results of the proposed model. Therefore, the regret generated by the research algorithm is much smaller than that of the comparative algorithm, indicating that it can be applied to bridge monitoring and has better monitoring ability for unbalanced offline data.

Suggested Citation

  • Lei Fu & Hongju Han, 2025. "Deep Analysis of Unbalanced Offline Data in Bridge Monitoring Based on Improved Upper Confidence Bound Algorithm," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 16(1), pages 1-26, January.
    • Handle: RePEc:igg:jismd0:v:16:y:2025:i:1:p:1-26
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    References listed on IDEAS

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    1. Nandini Kannan & Debasis Kundu & P. Nair & R. C. Tripathi, 2010. "The generalized exponential cure rate model with covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(10), pages 1625-1636.
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