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- W4285992090 abstract "Deflection is an essential index to evaluate the operating state of a bridge but is difficult to measure since it requires a stationary reference. This paper proposes an indirect method to measure the displacement time history. A convolutional neural network (CNN) is adopted to approximate the mapping relationship among bridge responses, with which the required deflections are reconstructed from other types of measurements. The network designing is guided by prior knowledge of the traffic flow-induced bridge displacement reconstruction problem. The network has two individual branches to estimate the quasi-static and dynamic displacement components, and sums them up in a later stem. Besides, the loss function involves a physics-based regularization term, i.e., the calculus relationship between displacement and acceleration. The physical loss can guide the training direction, alleviate overfitting issues, and improve the algorithmic performance on the high-frequency responses. Two numerical examples and a laboratory test are adopted to validate the performance and applicability of the approach." @default.
- W4285992090 created "2022-07-21" @default.
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- W4285992090 date "2022-10-01" @default.
- W4285992090 modified "2023-10-15" @default.
- W4285992090 title "Traffic-induced bridge displacement reconstruction using a physics-informed convolutional neural network" @default.
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- W4285992090 doi "https://doi.org/10.1016/j.compstruc.2022.106863" @default.
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