A non-destructive detection system and control method for concrete internal reinforcement based on improved neural network

Abstract

The invention relates to an improved neural network-based non-destructive detection system and control method for concrete internal steel bars, including steel bar positioning, steel bar diameter measurement and protective layer thickness measurement. The system consists of three parts: coil array sensor, lower computer and upper computer. The special coil array sensor enables the present invention to have the ability of precise positioning of steel bars. The lower computer part includes embedded controller, signal generation circuit and signal processing circuit, AD sampling circuit, electronic display screen, function keys, WIFI module and grating ranging module. The upper computer part is to improve the neural network and supplemented with a display interface. The control method is mainly completed by improving the neural network. Based on the special design of the coil array sensor, it can accurately locate the steel bar, which solves the problem that the current steel bar positioning is not sensitive to the angular offset of the steel bar. At the same time, based on the improved neural network algorithm, the accuracy of steel bar detection is improved, and the disadvantages that the current steel bar diameter and the thickness of the protective layer cannot be detected at the same time are solved.

Description

technical field [0001] The invention relates to a nondestructive testing technology, a reinforced concrete testing technology and a mathematical model for soft measurement of steel bars, and belongs to the application field of building intelligent control. Background technique [0002] The most common form of building structure in our country is reinforced concrete structure, and the safety detection of reinforced concrete structure is an inevitable part of safety production. However, in real life, since the steel bars are located inside the concrete, the location and quality of the steel bars are unknown, so the detection of steel bars inside the concrete is an urgent problem that needs to be solved. At present, the method of breaking the wall and exposing the internal steel bars is mostly used for the detection of reinforced concrete, which causes great damage to the building. [0003] At present, the coil structure used in the common eddy current effect-based detection s...

AI Technical Summary

Problems solved by technology

Due to the limited number of detection coils and the relatively simple layout of the coils, the amount of steel bar information that can be obtained is relatively small. At present, similar products can only detect the thickness of the protective layer under the preset steel bar diameter, but the steel bar diameter is the same. It has an important impact on the quality of the building, and the diameter of the steel bar is assumed to be known, which brings great uncertainty to the accuracy of the thickness detection of the protective layer. Therefore, there are great hidden dangers in the practical application of this scheme

In terms of back-end modeling methods, linear function fitting is mostly used at present, and polynomial fitting is common. The mathematical model obtained by this scheme has large deviations, and it is not self-learning, and the effect in practical applications is poor. Difference

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