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Subway structure deformation forecast method based on BP-time sequence fusion

A technology of time series fusion and subway structure, applied in neural learning methods, neural architecture, biological neural network models, etc., can solve the problems of low prediction accuracy and poor model stability, and achieve excellent forecasting effect, good stability, The effect of high forecast accuracy

Active Publication Date: 2016-07-27
SOUTHEAST UNIV
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Problems solved by technology

These subway structural deformation prediction models have their own advantages and disadvantages, but they generally have the problems of low prediction accuracy and poor model stability. According to the characteristics of subway structural deformation

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  • Subway structure deformation forecast method based on BP-time sequence fusion
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  • Subway structure deformation forecast method based on BP-time sequence fusion

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Embodiment Construction

[0019] The present invention will be further described below in combination with specific embodiments and accompanying drawings.

[0020] The invention discloses a subway structure deformation prediction method based on BP-time series fusion, which includes the following steps:

[0021] S1: Analyze the deformation characteristics of the subway structure over time through the known deformation data of the subway structure.

[0022] Organize the deformation data of the subway structure, and use the cubic polynomial interpolation method to interpolate the missing data to obtain a uniformly sampled time series of the deformation of the subway structure, and select appropriate learning samples and test samples according to the actual project;

[0023] Taking a subway project in Nanjing as an example, three monitoring points YY18-1, YY18-2, and YY18-3 on the tunnel monitoring section YY18 were selected from June 18, 2013 to March 10, 2015 in the Z direction. The cumulative deformat...

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Abstract

The invention discloses a subway structure deformation forecast method based on BP-time sequence fusion. The method comprises the following steps: S1, analyzing a change feature of subway structure deformation along with time through known subway structure deformation data; S2, constructing a time sequence prediction model and predicting subway structure deformation data; and S3, constructing a BP-time sequence fusion model, and forecasting a subway structure deformation residual error obtained through a time sequence prediction model by use of a BP neural network model, such that the time sequence prediction model can be compensated. According to the invention, through combining a time sequence model with a BP neural network, by use of the advantage of the neural network in nonlinear change data fitting, errors of the time sequence prediction model are compensated, the change feature of the subway structure deformation along with the time is fully mined, the prediction precision is high, and the stability is good.

Description

technical field [0001] The invention relates to a subway structure deformation prediction method, in particular to a subway structure deformation prediction method based on BP-time series fusion. Background technique [0002] The structural deformation of the subway during construction and operation is inevitable. Due to the particularity of the tunnel structure, the safety of the subway cannot be guaranteed when deformation occurs. The urban subway project itself produces structural deformation and settlement due to the deformation of the foundation, internal stress and changes in external loads; in addition, in the urban subway that has been built or is being built, the non-metro construction projects above or along the subway tunnel are more and more More and more, these projects have construction procedures and influencing factors such as unloading, loading, precipitation, pumping or vibration, and will also have certain structural deformation, inclination, displacement,...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 胡伍生仲洁潘栋
Owner SOUTHEAST UNIV
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