A BASIC STUDY ON URGENCY CLASSIFICATION OF SEWER PIPE CULVERTS USING 1D-CNN

Abstract

The total length of sewer culverts in Japan will be approximately 490,000 km by the end of FY2020, and the country has a huge stock of sewer pipes. In addition, the proportion of sewer culverts with a standard service life of 50 years will increase rapidly. Currently, inspections of sewer culverts are conducted by visual and TV camera surveys, but the total survey length in FY 2018 was only 6686 km, including both visual and TV camera surveys. In this study, we constructed an urgency classification model using a onedimensional convolutional neural network, which is one of the deep learning methods, utilizing the database of culvert deterioration published by the National Institute for Land and Infrastructure Management of the Ministry of Land, Infrastructure, Transport and Tourism (MLIT).