Abstract: The proposed dam automation system integrates multiple technologies to enhance the monitoring and safety of dams. The system includes crack detection using image processing and deep learning techniques, leakage, water quality monitoring with pH and turbidity sensors, and water level detection using sensors, all connected to an ESP32 microcontroller. Additionally, rain sensing and automated dam gate control are implemented based on water and rain levels, with emergency intimation features for potential flood and crack situations. The system aims to provide real-time monitoring and automated responses, improving dam safety and management. And the system integrates multiple technologies for enhanced dam monitoring and safety. The detection and monitoring of cracks in dams are crucial for ensuring their structural integrity and safety. Traditional methods of inspecting dam structures, such as manual inspections and visual assessments, are time-consuming, labor-intensive, and prone to human error. This paper presents a novel Dam Crack Detection System that utilizes advanced technologies, including sensors, machine learning algorithms, and image processing techniques, to automate and enhance the crack detection process. The system integrates real-time data from various sources such as strain gauges, acoustic sensors, and high-resolution cameras to identify, analyze, and classify potential cracks in the dam structure. Machine learning models, particularly convolutional neural networks (CNN), are employed to process image data for crack detection and severity analysis. The system's capability to operate autonomously and provide early warning signals for potential structural issues offers significant improvements over traditional methods, enabling proactive maintenance, reducing the risk of catastrophic failures, and ensuring the long-term safety of dam infrastructures. Furthermore, the system's scalability and adaptability make it suitable for a wide range of dam types and environmental conditions.
Keywords: Defect detection, DAM health diagnosis, multimodal sensors, water analysis, crack detection.
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DOI:
10.17148/IJARCCE.2024.131229
