国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Aiming at the problem that the maintenance and repair cost of old bridges is affected by the environment and its own life, this paper designs and develops a comprehensive parameter acquisition and health assessment system for bridge remote structures. First of all, the use of multiple sensors laid on the bridge to form an all-weather hardware monitoring system, the software communicates with the monitoring system through the 5G public network, and at the same time, with the help of the multi-classification convolutional neural network embedded in the software, the unique advantages of the deep neural network in the field of classification are applied to the assessment of the health status of the old bridge. After practical testing, to meet the design requirements, to solve the old bridge due to the long monitoring cycle and low efficiency, resulting in the difficulty of health diagnosis, the system has the advantages of low difficulty in obtaining parameters, high precision, accurate health assessment.