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基于波形神经网络和风驱动优化的桥梁模型更新.

Haifang He1, Baojun Zeng2, Yulong Zhou1

  • 1National Engineering Laboratory of Bridge Safety and Technology (Beijing), Research Institute of Highway Ministry of Transport, Beijing 100088, China.

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概括
此摘要是机器生成的。

一种新方法使用波形神经网络 (WNN) 和风驱动优化 (WDO) 来更新民用基础设施的有限元模型,改善结构健康监测和确保安全.

关键词:
桥梁桥梁的桥梁桥梁的桥梁有限元素模型的更新.代孕模型的代孕模型波形神经网络的神经网络风力驱动的优化优化

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科学领域:

  • 土木工程 土木工程是指土木工程.
  • 结构健康监测 结构健康监测
  • 计算力学 计算力学 计算力学

背景情况:

  • 由于老化,腐蚀和维护不良,民用基础设施的性能恶化.
  • 更新有限元模型对于结构健康监测和确保安全至关重要.
  • 需要准确的模型来反映结构的当前状态.

研究的目的:

  • 提出一种有效的方法来更新有限元模型.
  • 通过使用先进的计算技术,加强结构健康监测.
  • 在现实世界的桥梁模型上验证方法.

主要方法:

  • 使用波形神经网络 (WNN) 作为替代模型.
  • 使用风驱动优化 (WDO) 算法进行参数更新.
  • 将WNN-WDO组合方法应用于连续光束和真实桥梁模型.

主要成果:

  • WNN有效地捕捉了结构反应和参数之间的非线性关系.
  • WDO显著提高了有限元模型更新的效率和准确性.
  • 该方法成功地更新了多参数桥梁模型,差异在5%以内.

结论:

  • WNN-WDO方法是多参数桥梁模型更新的实用和高效方法.
  • 这种方法提供了高可靠性和工程应用的实际意义.
  • 该研究证明了结构健康监测和绩效评估的强大解决方案.