Intelligent identification method for prestressed concrete beam grouting compactness

A concrete beam and intelligent identification technology is applied in the field of intelligent identification of grouting density of prestressed concrete beams, and intelligent identification of grouting density of prestressed concrete beams based on shock echo, which can solve the problems of modeling difficulties and lack of mathematical theory, etc. Achieve the effect of overcoming inefficiency, eliminating the impact of human factors, and enhancing practicality and reliability

Active Publication Date: 2017-07-28
HOHAI UNIV
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However, the main problem of the empirical mode decomposition metho

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  • Intelligent identification method for prestressed concrete beam grouting compactness
  • Intelligent identification method for prestressed concrete beam grouting compactness
  • Intelligent identification method for prestressed concrete beam grouting compactness

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Embodiment Construction

[0025] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0026] Such as figure 1 As shown, it is a flowchart of an intelligent identification method for grouting density of prestressed concrete beams according to the present invention. The present invention uses techniques such as variational mode decomposition, finite element simulation and support vector machine to simulate shock echo vibration signals under different working conditions through finite element, and then uses variational mode decomposition and Hilbert transform to construct support Finally, the impact echo detection signal is used to quantitatively predict the compactness of prestressed concrete beams using the trained support vector machine.

[0027] The me...

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Abstract

The invention discloses an intelligent identification method for prestressed concrete beam grouting compactness. According to the method, variational mode decomposition, finite element simulation, support vector machine and other technologies are used, wherein impact echo vibration signals under different working conditions are simulated through finite elements, a support vector machine training sample is constructed by using variational mode decomposition and hilbert transform, and finally the prestressed concrete beam grouting compactness is quantitatively predicted with the trained support vector machine according to an impact echo detection signal. According to the present invention, with the intelligent identification method, the disadvantages of low efficiency, poor precision and the like during the existing impact echo detection process can be effectively overcome, the influence of the human factors in the detection process can be eliminated, and the detection precision and the working efficiency can be improved to the greatest extent.

Description

technical field [0001] The invention relates to an intelligent identification method for grouting density of prestressed concrete beams, in particular to an intelligent identification method for grouting density of prestressed concrete beams based on impact echo, and belongs to the technical field of civil and structural engineering detection. Background technique [0002] With the development of our country's economy, prestressed concrete bridges are widely used. In order to improve the integrity of the prestressed steel bar and the surrounding concrete, when the prestressed steel bar is stretched to the design value, it is necessary to grout into the bellows. The density of grouting directly affects the corrosion resistance of prestressed steel bars. If the grouting of bellows is not dense, it will have an adverse effect on the durability of the bridge. The quality of grouting must be paid attention to. At present, there are ground-penetrating radar method, ultrasonic met...

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Application Information

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IPC IPC(8): G01N9/00G06F17/50
CPCG01N9/002G06F30/23
Inventor 许军才沈振中任青文沈心哲张湛张卫东
Owner HOHAI UNIV
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