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<p>This paper proposes a novel data-driven framework for accurate cable force monitoring in bridges, which is crucial for ensuring structural integrity and extending service life. Leveraging the power of Digital Twins (DT), a comprehensive dataset of numerical cable models is generated. This synthetic data is used to pre-train a backpropagation (BP) neural network, providing a robust foundation. Subsequently, Transfer Learning (TL) is employed to refine the pre-trained model using limited real-world cable force data. This hybrid approach significantly enhances prediction accuracy, achieving consistently low errors—below 4% for short cables and 2% for long cables. Numerical examples demonstrate the effectiveness of the proposed framework, highlighting its potential for reliable and cost-effective cable force identification in real-world bridge applications.</p>

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High-precision cable force identification in bridges: a data-driven approach integrating digital twins and transfer learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月22日

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  • Xingjun Gao,
  • Mengkai Wang,
  • Gongfa Chen,
  • David Bassir,
  • Mingjun Zhang

摘要

This paper proposes a novel data-driven framework for accurate cable force monitoring in bridges, which is crucial for ensuring structural integrity and extending service life. Leveraging the power of Digital Twins (DT), a comprehensive dataset of numerical cable models is generated. This synthetic data is used to pre-train a backpropagation (BP) neural network, providing a robust foundation. Subsequently, Transfer Learning (TL) is employed to refine the pre-trained model using limited real-world cable force data. This hybrid approach significantly enhances prediction accuracy, achieving consistently low errors—below 4% for short cables and 2% for long cables. Numerical examples demonstrate the effectiveness of the proposed framework, highlighting its potential for reliable and cost-effective cable force identification in real-world bridge applications.