Deep Learning Model of Reinforced Concrete for Detecting Internal Structural Damage and Comprehensive Evaluation

Abstract

In recent years, China has achieved world leading achievements in the field of civil engineering, but the aging and damage problems of large structures have become increasingly prominent. Traditional structural health monitoring (SHM) methods have certain limitations and urgently require innovative technological solutions. This article explores existing structural health detection methods, including manual visual inspection, sensor detection, and digital image processing, analyzes their advantages and disadvantages, and provides a detailed introduction to the application of deep learning, especially convolutional neural network (CNN), in structural damage detection. Finally, this article constructs a SHM model based on deep learning, and improves the detection accuracy of the model through dataset construction and enhancement techniques. The research results indicate that deep learning, especially CNNs, exhibits high accuracy and adaptability in SHM, which can effectively compensate for the shortcomings of traditional detection methods. The research on deep learning provides new technological ideas for the construction of future intelligent SHM systems, which is expected to improve the efficiency and accuracy of structural damage detection in the field of civil engineering. In the future, with the introduction of more datasets, deep learning models will be further optimized.