AI Based Crack Detection and Correction System
ABSTRACT
Urban structural health monitoring has taken a revolutionary turn with the introduction of the AI-based Crack Detection and Correction System. This system replaces traditional manual inspection methods, which are frequently hampered by time limits and accessibility problems, by integrating image processing and Convolutional Neural Network (CNN) algorithms. Installed in city environments with a lot of stories, the system carefully uses cameras to take wide-angle pictures so that cracks may be found quickly and accurately. This automated model outperforms conventional inspection techniques by processing data quickly, improving fracture recognition, and producing thorough analysis. Beyond its basic function of fracture detection, the device also plays a crucial role in preventative maintenance by proactively detecting structural problems, particularly in difficult-to-reach places like chimneys where conventional methods may not work as well. The model's automated operations, complemented by its ability to analyze images from various angles, contribute to a holistic evaluation of a structure's health. But for this system to be widely adopted, privacy, data security, and environmental issues must be addressed. Continued research is necessary to improve the model's accuracy and adaptability, making it more useful in a variety of situations. Continuous monitoring and real-time data integration could make the system even more effective as a dynamic tool for maintaining structural health over time. The AI-based Crack Detection and Correction System, in conclusion, offers a revolutionary approach to the monitoring of urban structural health. Its smooth integration of CNN and image processing methods allows it to perform not only quickly crack detection but also proactive preventive actions, which bodes well for the field's future developments and wide-ranging applications. The system's integration of automation, accuracy, and comprehensive assessment places it at the forefront of the development of monitoring technologies, with potentially significant long-term effects