Bridge damping ratio identification method based on monitoring data and deep learning
A technology of monitoring data and deep learning, applied in neural learning methods, image data processing, image analysis, etc., can solve problems such as time-consuming and laborious, and achieve the effect of reliable calculation
Pending Publication Date: 2022-05-13
山东高速集团有限公司创新研究院 +2
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It is certainly feasible to manually filter the available free decay response segment data by professionals, but under the
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[0031]The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0032] The purpose of the present invention is to provide a bridge damping ratio identification method based on monitoring data and deep learning, which can automatically identify the free attenuation response section suitable for damping ratio calculation in massive monitoring data, and then use the exponential decay method to calculate the damping ratio of the bridge structure .
[0033] In order to make the above objects, features and advantages of the prese...
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The invention provides a bridge damping ratio identification method based on monitoring data and deep learning, and the method improves a U-Net model which can execute a semantic segmentation task on a picture in a deep learning method, enables the U-Net model to be suitable for the processing of time sequence type acceleration monitoring data, constructs a semantic segmentation data set for a free attenuation response segment, and achieves the recognition of the damping ratio of a bridge. Training, performance evaluation and tuning are carried out on the improved U-Net model, semantic segmentation is carried out on mass bridge acceleration monitoring data through the tuned and optimized improved U-Net model, a free attenuation response section with an ideal attenuation shape is extracted from the data, and then an exponential attenuation method is adopted to calculate the damping ratio. According to the bridge damping ratio identification method based on monitoring data and deep learning, the problem that free attenuation response segments cannot be efficiently screened under massive monitoring data is solved, the exponential attenuation method can be applied to bridge monitoring data, and the damping ratio can be calculated more reliably compared with other methods.
Description
technical field [0001] The invention relates to the technical field of bridge structure health monitoring and computer deep learning application, in particular to a bridge damping ratio identification method based on monitoring data and deep learning. Background technique [0002] The damping ratio can characterize the energy dissipation capacity of the bridge structure under dynamic loads, and plays a key role in the dynamic analysis of bridges for the purpose of earthquake resistance, wind resistance, and human-induced vibration control. The different forms set the bridge damping ratio to a certain value between 0.003 and 0.05. However, the bridge in operation will be affected by many factors such as environment and operation load, and the structural damping ratio will not remain constant. If the damping ratio recommended by the code is used in the dynamic analysis, the calculated dynamic response may have a large deviation from the actual situation, making it difficult t...
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Login to View More IPC IPC(8): G06T7/10G06N3/04G06N3/08
CPCG06T7/10G06N3/084G06N3/045
Inventor 尚志强夏烨陈林马乃轩王阳春辛公锋孙利民
Owner 山东高速集团有限公司创新研究院