Currently, unmanned aerial vehicles are increasingly being used in various construction projects such as housing developments, road construction, and bridge maintenance. If a drone is used at a road construction site, elevation information and orthoimages can be generated to acquire the construction status quantitatively. However, the detection of detailed changes in the site owing to construction depends on visual video interpretation. This study develops a method for automatic detection of the construction area using multitemporal images and a deep learning method. First, a deep learning model was trained using images of the changing area as reference. Second, we obtained an effective application method by applying various parameters to the deep learning process. The application of the time-series images of a construction site to the selected deep learning model enabled more effective identification of the changed areas than the existing pixel-based change detection. The proposed method is expected to be very helpful in construction management by aiding in the development of smart construction technology.

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.