Neural Network Algorithms in Civil Engineering Inspection under Artificial Intelligence Technology

Abstract

The health monitoring of civil engineering buildings is inevitable, and it will bring great harm, so it must be safely monitored. In recent years, pattern recognition is mainly through the theoretical analysis of the model, the resulting damage pattern recognition is combined with the actual detection pattern recognition. Traditional pattern recognition technology is difficult to solve the "explosion" of various structural damage and the modal distortion caused by noise well. Due to its superiority in pattern recognition, it has been used by more and more scholars for structural damage recognition. Based on the various disadvantages of using traditional wired methods to detect the state of civil structures, this paper proposes an implementation plan for detecting the state of civil structures based on artificial neural networks and pattern recognition methods based on further analysis of artificial intelligence technology and neural network algorithms. The detection method of civil engineering defects is numerically simulated to verify the feasibility of the formula and its application scope, and to further promote the application of pattern recognition and artificial neural network detection technology in the field of civil engineering. From the experimental results, the error range of neural network algorithms in civil engineering structure detection based on artificial intelligence technology is small. In actual structural detection, it can effectively identify and analyze the characteristics of engineering structures in depth, provide scientific and objective data support for engineering construction, and promote the healthy development of civil engineering construction.