| dc.description.abstract | Nowadays, shotcrete operation, including the spraying of building walls and tunnels, is done mainly by human resources. During the operation, it will cause irreversible damage to the construction personnel due to noise, dust, and a severe construction environment. Buildings and tunnel projects also have the problem of the maintenance period, which still requires manual maintenance and reinforcement.
This research is expected to develop an automatic repairing robot for concrete defects. This robot controls the behavior of each device through a microcontroller and connects the microcontroller with a wireless network (Wi-Fi) on the mobile phone or computer. Remote control, the user interface is a customized web page using HTML and JavaScript to contact the communication server and client. A visual recognition module is carried on the shotcrete spray gun, based on Machine Learning and Visual Recognition technology to collect pictures for visual recognition to replace manual work. Visual recognition training uses Convolutional Neural Network (CNN) algorithm. Then designed, an algorithm automatically scans the target wall, and the microcontroller cooperates with the visual recognition module to report the defect status of the wall. When the defect is identified, the microcontroller will remotely control the microcontroller of the shotcrete pump control box and flip the switch to automate complete the defect automatic spray repair operation. After adjusting the training model many times, the accuracy rate reached 94.2%, the random image prediction accuracy rate was 97.28%, and the F1-Score reached 0.9 in the training model and 0.96 in random image prediction.
This research develops the prototype of the future automated defect identification reinforcement robot. This research integrates automatic robot control, shotcrete, visual recognition system, Internet of Things system, and web remote control. It has been confirmed through experiments that defect identification and shotcrete spraying can be automatically completed. | en_US |