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Model updating of a bridge structure using vibration test data based on GMPSO and BPNN: case study
Earthquake Engineering and Engineering Vibration ( IF 2.8 ) Pub Date : 2021-01-11 , DOI: 10.1007/s11803-021-2015-x
Zhiyuan Xia , Aiqun Li , Huiyuan Shi , Jianhui Li

Model updating issues with high-dimensional and strong-nonlinear optimization processes are still unsolved by most optimization methods. In this study, a hybrid methodology that combines the Gaussian-white-noise-mutation particle swarm optimization (GMPSO), back-propagation neural network (BPNN) and Latin hypercube sampling (LHS) technique is proposed. In this approach, as a meta-heuristic algorithm with the least modification to the standard PSO, GMPSO simultaneously offers convenient programming and good performance in optimization. The BPNN with LHS establishes the meta-models for FEM to accelerate efficiency during the updating process. A case study of the model updating of an actual bridge with no distribution but bounded parameters was carried out using this methodology with two different objective functions. One considers only the frequencies of the main girder and the other considers both the frequencies and vertical displacements of typical points. The updating results show that the methodology is a sound approach to solve an actual complex bridge structure and offers good agreement in the frequencies and mode shapes of the updated model and test data. Based on the shape comparison of the main girder at the finished state with different objective functions, it is emphasized that both the dynamic and static responses should be taken into consideration during the model updating process.



中文翻译:

基于GMPSO和BPNN的振动测试数据对桥梁结构的模型更新:案例研究

大多数优化方法仍未解决高维和强非线性优化过程的模型更新问题。在这项研究中,提出了一种结合高斯白噪声突变粒子群优化(GMPSO),反向传播神经网络(BPNN)和拉丁超立方体采样(LHS)技术的混合方法。在这种方法中,作为对标准PSO修改最少的元启发式算法,GMPSO同时提供了方便的编程和良好的优化性能。具有LHS的BPNN为FEM建立元模型,以在更新过程中加快效率。使用具有两个不同目标函数的这种方法,对没有分布但有界参数的实际桥梁的模型更新进行了案例研究。一个只考虑主梁的频率,另一个则考虑典型点的频率和垂直位移。更新结果表明,该方法是解决实际复杂桥梁结构的合理方法,并且在更新后的模型和测试数据的频率和模式形状方面具有良好的一致性。基于主梁在完成后具有不同目标函数的形状比较,强调在模型更新过程中应同时考虑动态和静态响应。更新结果表明,该方法是解决实际复杂桥梁结构的合理方法,并且在更新后的模型和测试数据的频率和模式形状方面具有良好的一致性。基于主梁在完成后具有不同目标函数的形状比较,强调在模型更新过程中应同时考虑动态和静态响应。更新结果表明,该方法是解决实际复杂桥梁结构的合理方法,并且在更新后的模型和测试数据的频率和模式形状方面具有良好的一致性。基于主梁在完成后具有不同目标函数的形状比较,强调在模型更新过程中应同时考虑动态和静态响应。

更新日期:2021-01-11
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