Multi dimensional disease intelligent detection device for underwater pier column structure through machine learning

Abstract

The total number of railway bridges in China has exceeded twenty thousand. As an important part of the railway line, the existing railway bridge bears great live load, excessive concentrated force, obvious dynamic effect and high requirements for foundation stability. However, because the underwater structure of Railway Pier is constantly affected by many factors such as water scouring and ship collision during its service, it leads to a variety of diseases such as concrete peeling and exposed reinforcement. These diseases are often hidden in the water, which poses a serious threat to the structural safety of the bridge and the safety of people's lives and property. However, the traditional detection method of underwater structure of Railway Pier is affected by water flow, water quality, water depth and other environment, which has the pain points of high detection risk, low efficiency, poor detection data quality and missing disease location, so it is difficult to effectively detect underwater structure diseases; In this context, a multi-dimensional disease detection device is designed and developed for Wuhan Yangtze River Bridge (both highway and railway). The device includes a fixed module on water and an underwater detection module, which can realize safe and efficient detection under the condition of rapids and deep water, solve the problems that it is difficult for personnel and existing equipment to reach the structure to be tested, and the detection effect obtained by existing detection means is not ideal The detection information is not comprehensive, which is difficult to be used for key problems such as follow-up structural technical state evaluation.