Intelligent detection and quantitative recognition method for defect of concrete

Abstract

The invention discloses an intelligent detection and quantitative recognition method for the defect of concrete. According to the method, a concrete test piece is subjected to impact echo signal sample acquisition, signal noise reduction treatment and characteristic value extraction so as to construct a recognition model for analysis components including feature extraction, defect inspection, defect diagnosis and defect quantification and positioning; and the model is used for detecting and recognizing to-be-detected concrete. The intelligent detection and quantitative recognition method provided by the invention is directed at disadvantages of conventional detection technology for concrete defects and based on theoretical analysis, value simulation and model testing, employs advanced signal processing and artificial intelligence technology and fully digs out characteristic information of a testing signal, thereby establishing the model for intelligent rapid detection and classified recognition based on wavelet analysis and an extreme learning machine; and the model has good classified recognition performance, realizes intelligent rapid quantitative recognition and evaluation of the variety, properties and scope of the defect of concrete and further improves the innovation and application level of non-destructive testing technology for the defect of concrete.

Description

Technical field [0001] The invention relates to the technical field of concrete non-destructive testing, in particular to an intelligent detection and quantitative identification method for concrete defects. Background technique [0002] Concrete is one of the most commonly used structural materials in construction projects. The existence of internal defects in concrete will often seriously affect the bearing capacity and durability of the structure. How to detect the internal defects of concrete structures and correctly identify and evaluate the nature, location and scope of the defects has become the current technical difficulty, and it is also the focus of common concern in the engineering and academic circles at home and abroad. Therefore, in order to diagnose and evaluate the quality of engineering entities accurately, objectively, comprehensively and quickly, it is necessary to conduct in-depth research on non-destructive testing and rapid evaluation of concrete defects. [...

AI Technical Summary

Problems solved by technology

For example, expert systems, artificial neural networks, support vector machines, and extreme learning machines all have strong nonlinear mapping capabilities, and are especially suitable for nonlinear pattern recognition. However, most current applications obtain training samples through simple model tests. In addition, algorithms such as artificial neural networks have limitations such as poor generalization ability and weak adaptability to small sample test environments, which largely affects the effectiveness of artificial intelligence for classification and recognition of concrete defects.

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