DEVELOPMENT OF CRACK INITIATION MONITORING SYSTEM OF LIGHTING PILLARS BY SOUND PROPAGA-TION CHARACTERISTICS

Abstract

The deterioration of infrastructure is a social issue. Street lights are no exception, and accidents are oc-curring in various places. The main in-spection currently performed is visually inspecting. When abnor-malities such as corrosion are found as a result of visual inspection, ultrasonic testing, magnetic powder testing, or penetrant testing is performed. However, none of these methods obtain continuous data, and it is difficult to detect cracks early and to predict growth of it. Therefore, mechanization and automation are required. To solve this problem, a con-stant street light monitoring system is proposed in this study. Re-garding the method of crack detection, the data is measured by using a measuring apparatus using audible sound for the Street lights. The power spectrum is calculated using the data, and deep learning, which is one of machine learning, is used to detect cracks early and to predict growth of it. In the experi-ment using the test body, the accuracy of the actual crack length and the predicted value of machine learn-ing was 63.6%, and the presence or absence of the crack could be detected with a certain degree of accu-racy. This result indicates that is able to considered practical enough.