Study on a method for inferring the degree of deterioration of road bridge slabs using convolutional neural networks

Abstract

In Japan, transportation infrastructure such as roads and bridges, which forms the core of public infra- structure facilities, was intensively developed during the period of rapid economic growth in the 1960s. However, more than 50 years have passed since their construction, and they are aging and deteriorating along with the rapid development of motorization. The number of such infrastructure facilities of deterioration is increasing with each passing year, making efficient maintenance and management of aging infrastructure an urgent task. In order to carry out efficient maintenance management, it is necessary to develop a method to accurately and quickly evaluate the degree of deterioration of structures. In this study, we focused on road bridge slabs, which are a major transportation infrastructure structure, and showed that it is possible to infer the degree of deterioration of the slabs by applying a convolutional neural network method to the strain waveforms of the slabs caused by traffic loads traveling on the bridge surface.