The close-up visual inspection of bridges faces several problems, including a lack of financial resources and human personnel. Hence, there has been increasing use of artificial intelligence (AI) and information and communications technology (ICT) to solve them. We previously investigated remote inspection—in which skilled engineers provided on-site support from a remote location—with the aim of reducing the labor required for on-site work and addressing the lack of personnel through the use of AI and ICT. Sharing images of bridges from inspection sites to remote locations via the Internet enables remote assessment of the sites and the ability to consider and diagnose damage. Mobile communications can be used to upload images, although the volume of image data required for inspection can be enormous and take considerable time to upload. Consequently, in this study, we investigated image uploads using 5G communication—that is, the fifth-generation technology standard for broadband cellular networks. Moreover, we measured the upload times when using 4G and 5G services and examined their operation based on differences in the communication environments. We concluded that the simulated remote inspection can be efficiently performed by adjusting the inspection method to the communication environment.

With the aging of bridges, the efficiency of periodic inspections has become a problem. As issues with the continuing close visual inspection of bridges are surfacing, remote imaging systems are expected to become a new inspection method that replaces close visual inspection. The objective of the study is to develop a classification model of countermeasure categories using the results of past periodic inspections of bridges conducted by skilled inspectors. Focusing on concrete slabs, a model was constructed to classify the countermeasure categories based on the characteristics of the damage maps by random forest classification. As a result, it was possible to classify two classes of countermeasure categories with a macro-average precision rate of about 88%. It became clear that the degree of crack development and the number of cracks are the most important factors in the classification of judgment categories.

Conventional close visual inspection of bridges has high cost and lack of skilled engineers. New technologies, such as AI, UAV, and robots, can be provided to help the inspection process and substitute previous inspection methods to save labor effort and reduce costs. We develop damage detection system for bridge inspection by adopting image recognition technology based on deep learning. It detects damage from bridge images and provides the accurate outline. Such technology can reduce inspection work by detecting the damage instead of inspectors, and they can focus on important tasks such as damage determination. However, it takes a lot of time to collect and annotate for training images. Although linear damage such as cracks requires a fine outline for each pixel, planar damage such as free lime is presumed to be allowable even at low precise boundaries. If low precise boundaries are allowed, training data is obtained in less time. To determine damage with the same accuracy as close visual inspection, the limits of allowable low precision display need to be determined. This study examined the limis of low precise boundaries for free lime. The bridge engineers compared with the detection output of gradually reduced precision boundaries and investigated the limits of the low precision they allow.