Steel aqueduct structure extreme stress early warning method based on sliding window Gaussian process model

A Gaussian process model, aqueduct technology, applied in design optimization/simulation, probabilistic CAD, stochastic CAD, etc., can solve the problem of huge amount of derived stress data in online monitoring, and achieve the goal of overcoming low efficiency problems, ensuring prediction accuracy and reducing prediction efficiency. Effect

Pending Publication Date: 2022-01-28
安徽省七星工程测试有限公司
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In addition, the online monitoring-derived stress data extracted

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  • Steel aqueduct structure extreme stress early warning method based on sliding window Gaussian process model
  • Steel aqueduct structure extreme stress early warning method based on sliding window Gaussian process model
  • Steel aqueduct structure extreme stress early warning method based on sliding window Gaussian process model

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[0075] Experimental verification: The steel structure aqueduct mentioned in the engineering background adopts a three-span truss-type beam-arch composite system, and the span layout is (68+110+68)m. The design longitudinal slope of the aqueduct is 1 / 15000, and the steel structure aqueduct adopts a three-span truss-type beam-arch combination system, and the span layout is (68+110+68)m. Horizontally, the layout is divided into frames. The width of a single tank is 24m, the net distance between two tanks is 10m, and the overall width is 58.0m. The site layout of the aqueduct is as follows: figure 2 shown.

[0076] In order to test whether the bearing capacity of the aqueduct structure meets the requirements in the water-filled test state, the aqueduct structure was monitored in real time during the water-filled test. The types of monitoring data include: strain, support displacement, environmental data, etc. In order to verify the applicability of the method proposed in the pre...

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Abstract

The invention discloses a steel aqueduct structure extreme stress early warning method based on a sliding window Gaussian process model, and the method utilizes the advantages of a Gaussian process regression model in the aspect of nonlinear time sequence prediction to predict the stress state of a steel aqueduct structure under the effects of temperature and water load, and achieves the real-time early warning of the safety performance of a section. The method selects training data by using a window function synchronously updated with the prediction process; since the window length is not increased along with the increase of the time sequence, therefore, when the monitoring data volume is greatly increased, the prediction and early warning efficiency of the structural strain extreme value can be obviously enhanced; the invention has important theoretical significance and engineering application value for long-term health monitoring and structural safety evaluation of the large steel aqueduct structure.

Description

technical field [0001] The invention relates to the field of structural health monitoring, in particular to a method for early warning of extreme stress of a steel aqueduct structure based on a sliding window Gaussian process model. Background technique [0002] As a key hub of water diversion projects, the aqueduct structure has been widely used in large-scale water conservancy projects such as the South-to-North Water Transfer Project and the diversion of the Yangtze River to the Huaihe River. However, under the long-term service conditions of the aqueduct structure, due to harsh environmental factors and water flow loads, it usually leads to different degrees of defects and diseases of the aqueduct structure, which has a significant impact on the safe operation of the aqueduct structure. Since the aqueduct structure often flows out of areas with many rivers, the geographical environment is complex, and the cost of manual inspection is high. Therefore, long-term monitoring...

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Application Information

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IPC IPC(8): G06F30/27G06F111/08
CPCG06F30/27G06F2111/08
Inventor 陈发军付书林程勇刚李迪鲍大春叶刘克陈翔张菲却杨玉盟葛磊
Owner 安徽省七星工程测试有限公司
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