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Concrete structure defect detection method based on elastic wave and machine learning

A concrete structure and machine learning technology, applied in the field of detection, can solve the problems of low detection precision and accuracy, and achieve the effect of simple and clear detection process, wide application range and accurate judgment.

Active Publication Date: 2019-01-11
四川升拓检测技术股份有限公司
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: the problem of low precision and accuracy in the detection of concrete structure defects. The present invention provides a method for detecting defects in concrete structures based on elastic waves and machine learning that solves the above problems

Method used

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  • Concrete structure defect detection method based on elastic wave and machine learning
  • Concrete structure defect detection method based on elastic wave and machine learning
  • Concrete structure defect detection method based on elastic wave and machine learning

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Embodiment 1

[0078] The defect detection method of concrete structure based on elastic wave and machine learning mainly includes the following steps:

[0079] A. Using shock elastic wave as the detection medium, for the concrete structure of the test object, by obtaining the signal of the sound part, it is used as the benchmark parameter reflecting the mechanical properties of the concrete;

[0080] B. Using the spectral characteristics of the signal and the cumulative frequency of frequency shift to obtain the signal characteristics, and establish the signal characteristic attributes;

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Abstract

The invention discloses a concrete structure defect detection method based on elastic wave and machine learning, which adopts an impact elastic wave as a detection medium, utilizes picked-up signal characteristics and combines machine learning to obtain an analysis model, thereby detecting concrete structure defects. Signal features mainly use the spectral characteristics and spectral cumulative deviation rate, and establish attributes for machine learning; the benchmark parameters reflecting the mechanical properties of concrete are obtained by acquiring the information of the sound part of concrete structure. By testing the sound concrete structure and defective concrete structure under various structural thicknesses and working conditions, the signal characteristic attribute is analyzed, and the training set is built for machine learning and the analysis model is obtained; the test data of concrete structure in unknown state are analyzed by using the analysis model, and the analysisresults are verified. The data and validation results are added to the training set as examples, and the above steps are repeated to optimize the analysis model and improve the accuracy.

Description

technical field [0001] The invention relates to a detection method, in particular to a detection method for concrete structure defects based on elastic waves and machine learning. Background technique [0002] Concrete structure is the most widely used and most important building and civil structure today. Its large-area concrete structure like tunnel lining acts as a supporting superstructure in engineering, which directly affects the safety of the tunnel construction process and operation stage. However, during the construction process, there will still be defects in the concrete structure due to reasons such as cutting corners, backward construction technology, wrong construction steps or human negligence. [0003] Corresponding to the concrete structure of concealed engineering, there are many detection methods for non-destructive testing of concrete structure defects under the premise of no damage, such as shock elastic wave, geological radar, ultrasonic, etc. The two ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06N3/02G06N20/00
CPCG06N3/02G06F30/13G06F30/20
Inventor 吴佳晔罗技明李科杨森苏亚军吴波涛常崟王红印刘媛丽
Owner 四川升拓检测技术股份有限公司
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