Quality Control in Civil Engineering Construction Based on Deep Learning

Abstract

In the process of rapid growth of China’s construction industry, for civil engineering construction, its construction quality must be guaranteed to be able to drive China’s construction industry to the world’s advanced level. Based on deep learning, this paper discusses the types of bridges and detects their main components to achieve the quality control of the main components of the bridge. This paper analyzes the quality problems in civil engineering construction, and uses the Alex Net network model in the convolutional neural network to identify the bridge type; Faster R-CNN is used to detect the main components in real time. After testing and verification, the application method proposed in this paper has achieved satisfactory results, and the accuracy of model detection has reached 98.8%. The research in this paper applies advanced science and technology to the field of civil engineering construction to achieve automation and informatization is no longer out of reach, achieve the purpose of construction quality control, and promote the sustainable development of China’s construction industry.