Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

Abstract

Recently, the lack of expansion joint gaps on highway bridges in Korea has been increasing. In particular, with the increase in the number of days during the summer heatwave, the narrowing of the expansion joint gap causes symptoms such as expansion joint damage and pavement blow-up, which threaten traffic safety and structural safety. Therefore, in this study, we developed a machine vision (M/V)-technique-based inspection system that can monitor the expansion joint gap through image analysis while driving at high speed (100 km/h), replacing the current manual method that uses an inspector to inspect the expansion joint gap. To fix the error factors of image analysis that happened during the trial application, a machine learning method was used to improve the accuracy of measuring the gap between the expansion joint device. As a result, the expansion gap identification accuracy was improved by 27.5%, from 67.5% to 95.0%, and the use of the system reduces the survey time by more than 95%, from an average of approximately 1 h/bridge (existing manual inspection method) to approximately 3 min/bridge. We assume, in the future, maintenance practitioners can contribute to preventive maintenance that prepares countermeasures before problems occur.