Anchoring critical value detection method and system based on machine learning and storage medium

Abstract

The invention belongs to the technical field of anchoring detection, and particularly discloses an anchoring critical value detection method and system based on machine learning and a storage medium, and the method comprises the steps: combining an existing standard test and existing data of an anchoring critical value for training, obtaining a training model, and collecting state data of a pre-stress to-be-detected piece; preprocessing related state data according to inherent attribute parameters of a pre-stress to-be-detected piece, carrying out iterative fitting on the preprocessed data, determining an optimal fitting order, generating a self-adaptive empirical range according to training data, capturing a characteristic mutation point of a fitting graph, and reversely solving an initial solution of a stress critical value; and determining an optimal solution in the critical value initial solutions in combination with an anchoring physical process. According to the technical scheme, the method is used for detecting the anchoring critical value of the pre-stress to-be-detected piece.

Description

technical field [0001] The invention belongs to the technical field of anchorage detection, and relates to a machine learning-based anchorage critical value detection method, system and storage medium. Background technique [0002] In slope engineering, bridge engineering and construction engineering, prestressed anchorage technology is widely used to control the deformation of rock and soil and engineering structures. The stability and deformation of the slope reinforced by prestressed anchor cables depend on the working prestress of the anchor cables; for bridges with prestressed structures, the effective prestress after anchorage is directly related to the deformation and cracking of the bridge. The accurate control of the prestress value is directly related to the performance and safety of the project. Therefore, the engineering field requires the detection of the prestress. In the project construction stage, prestress is used to control the quality of the construction ...

AI Technical Summary

Problems solved by technology

Due to the complex working conditions on site, there are still many problems in the implementation process of the strain method and the reverse pull method, such as difficulty in implementation, high cost, and low efficiency.

Especially in the process of data collection and data analysis, a large number of repetitive and cumbersome data analysis and processing tasks are required, which is time-consuming and labor-intensive.

At the same time, the interpretation and judgment of the test results needs to be highly dependent on the personal experience of the technicians, and there are still problems of insufficient objectivity and large uncertainty to a certain extent.

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