Abstract
A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide a comparison value for bridge evaluation. Based on the temperature and deflection data obtained by the health monitoring system of a bridge, establishing the correlation model between temperature and temperature-induced deflection is meaningful. It is difficult to complete a high-quality model only by the girder temperature. The temperature features based on prior knowledge from the mechanical mechanism are used as the input information in this paper. At the same time, to strengthen the nonlinear ability of the model, this paper selects an independent recurrent neural network (IndRNN) for modeling. The deep learning neural network is compared with machine learning neural networks to prove the advancement of deep learning. When only the average temperature of the main girder is input, the calculation accuracy is not high regardless of whether the deep learning network or the machine learning network is used. When the temperature information extracted by the prior knowledge is input, the average error of IndRNN model is only 2.53%, less than those of BPNN model and traditional RNN. Combining knowledge with deep learning is undoubtedly the best modeling scheme. The deep learning model can provide a comparison value of bridge deformation for bridge management.
Highlights
This paper suggests that when establishing the temperature-induced deflection model of the main girder of a cable-stayed bridge, the temperature features can be extracted based on the prior knowledge by the mechanical mechanism
Under the influence of the complex temperature field distributing in all components of a cable-stayed bridge, the main girder of the cable-stayed bridge will produce the temperature-induced deflection
To establish a more accurate temperature-induced deflection model, this paper attempts to use the priori knowledge from the mechanical mechanism to extract the appropriate temperature features, and tries to use a deep learning tool with more powerful nonlinear expression performance
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Based on the big data obtained by SHM, the linear regression model between girder temperature and temperature-induced deflection is established, but the accuracy is poor [8]. The temperature-induced deflection of the main girder of a cable-stayed bridge is affected by each component on the bridge. According to the knowledge from mechanism research, the dispersed temperature field, which affects the temperature-induced deflection of cable-stayed bridge, can be summarized as data features such as the average temperature of main girder, the vertical temperature difference of main girder and the tower temperature. Prior knowledge will be used to extract the temperature features and reduce the data dimension; deep learning is used as a fitting tool to strengthen the performance in time series regression, to enhance the robustness of the established model [11]. The deep learning model is expected to obtain better accuracy, so it can contribute to a more reliable and more efficient recognition of the bridge state
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