GRNN、BPNN 和 RBFNN 在拱桥悬索频率和张力预测中的应用和比较

IF 3.6 2区 工程技术 Q1 ENGINEERING, CIVIL Journal of Civil Structural Health Monitoring Pub Date : 2024-06-17 DOI:10.1007/s13349-024-00816-7
Zhu Zhang, Eryu Zhu, Bin Wang, Ye Chen
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引用次数: 0

摘要

由于悬带参数的非线性特性,悬带频率和张力的预测很难解决。本文提出了一种利用广义回归神经网络(GRNN)模型预测悬带频率和张力的方法。为了解决悬带参数的非线性问题,有必要选择一些悬带参数作为模型的输入,如长度、单位长度质量、弯曲刚度、基频以及张力,并选择悬带频率或张力作为输出。为了考虑不同边界约束的影响,基于奇异扰动法推导出了悬带参数的解析表达式,并应用于模型训练。此外,还使用了两种不同类型的神经网络模型:反向传播神经网络(BPNN)和径向基函数神经网络(RBFNN)来预测悬带频率和张力,以便与 GRNN 模型进行比较。数据集包括测量数据和文献样本,用于验证模型。此外,还使用 R2、MAE 和 RMSE 来比较模型的性能。结果表明,与 BPNN 和 RBFN 相比,应用 GRNN 预测吊带频率和张力的准确度更高。
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Application and comparison of GRNN, BPNN and RBFNN in the prediction of suspender frequency and tension on arch bridge

The prediction of suspender frequency and tension is difficult to solve due to the non-linear nature of suspender parameters. A method of predicting suspender frequency and tension using the generalized regression neural network (GRNN) model was proposed in this paper. It is necessary to select some suspender parameters as inputs into the model to solve the non-linear nature problem of the suspender parameters, such as length, mass unit per length, bending stiffness, fundamental frequency as well as tension, and to select the suspender frequency or tension as output. To consider the effect of different boundary constraints, analytical expressions of suspender parameters based on the singular perturbation method are derived and applied to train the models. Two different types of neural network models: back propagation neural network (BPNN) and radial basis function neural network (RBFNN), are also used to predict suspender frequency and tension to compare with the GRNN model. Datasets consist of measurements and literature samples are used to verify the models. Furthermore, R2, MAE, and RMSE are used to compare the performance of the models. The results showed that the application of GRNN achieves higher accuracy in predicting suspender frequency and tension compared to BPNN and RBFNN.

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来源期刊
Journal of Civil Structural Health Monitoring
Journal of Civil Structural Health Monitoring Engineering-Safety, Risk, Reliability and Quality
CiteScore
8.10
自引率
11.40%
发文量
105
期刊介绍: The Journal of Civil Structural Health Monitoring (JCSHM) publishes articles to advance the understanding and the application of health monitoring methods for the condition assessment and management of civil infrastructure systems. JCSHM serves as a focal point for sharing knowledge and experience in technologies impacting the discipline of Civionics and Civil Structural Health Monitoring, especially in terms of load capacity ratings and service life estimation.
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