Stay cable tension estimation of cable‐stayed bridge under limited information on cable properties using artificial neural networks
Stay cable tension estimation of cable‐stayed bridge under limited information on cable properties using artificial neural networks
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DOI：
10.1002/stc.3015
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发表时间：
2022-06
影响因子：
5.4
通讯作者：
Luu Xuan Le;D. Siringoringo;H. Katsuchi;Y. Fujino
中科院分区：
文献类型：
--
作者：
Luu Xuan Le;D. Siringoringo;H. Katsuchi;Y. Fujino
本文提出了一个框架，用于在有限的拉索性能信息下估算有和无横向附件的斜拉桥的斜拉索张力。对于斜拉索没有横向附件，张力估计使用三个已知的参数，即，电缆的长度，每单位长度的质量，和测量的频率，同时仍然占未知的功能，如电缆弯曲刚度，轴向刚度，电缆倾斜，约束边界条件在电缆两端。该方法被扩展到斜拉索与横向附件（阻尼器和交叉拉杆），其属性也被认为是未知参数。该框架是通过应用反向传播人工神经网络（ANN）提出的。电缆模型的有限差分公式推导出创建数据集的训练，验证和测试的人工神经网络计划。通过数值仿真验证了该框架的可行性和鲁棒性。结果表明，在没有横向附件的电缆张力成功地评估使用至少两个测量频率，而电缆与横向附件需要至少三个测量频率，以达到高精度。最后，该框架被应用于估算斜拉索的张力多良桥，在日本最长的斜拉桥。结果表明，该方法可以估计索力与可接受的精度。
This paper proposes a framework for estimating tensions of stay cables of cable‐stayed bridge with and without lateral attachments under limited information on cable properties. For stay cables without lateral attachments, tension is estimated using three known parameters, namely, cable length, mass per unit length, and measured frequency while still accounting for unknown features like cable bending stiffness, axial stiffness, cable inclination, and restraint boundary conditions at the cable ends. The methodology is then extended to stay cables with lateral attachments (dampers and cross ties), whose properties are also considered as unknown parameters. The framework is proposed via the application of back‐propagation artificial neural networks (ANNs). The finite difference formulation of the cable model is derived to create datasets for training, validation, and testing in the ANNs scheme. The feasibility and robustness of the proposed framework were confirmed through numerical verifications. The results indicated that tension in cables without lateral attachments was successfully evaluated using at least two measured frequencies, whereas cables with lateral attachments needed at least three measured frequencies to achieve high accuracy. Finally, the proposed framework was applied to estimate tensions in stay cables of Tatara Bridge, the longest cable‐stayed bridge in Japan. The results demonstrated that the proposed method could estimate the cable tension with acceptable accuracy.