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By: Ravikant Nanwatkar, Vishwajeet Jangam, Prajyot Devkar, Shreyan Borawake, and Hari Kulkarni.
¹Assistant Professor, STES’s NBNSTIC, Ambegaon, SPPU, Pune, Maharashtra, India.
²UG Students, STES’s NBNSTIC, Ambegaon, SPPU, Pune, Maharashtra, India.
AbstractThe maintenance of dam walls is critical to ensure structural integrity, safety, and efficient water management. Cracks and other structural deficiencies can lead to severe consequences if left undetected. Traditional inspection methods are labor-intensive, time-consuming, and often hazardous, necessitating the development of autonomous systems for effective monitoring. This study focuses on the design and implementation of autonomous robots equipped for crack detection in dam walls, with an emphasis on vision-based algorithms and structural health monitoring techniques. The robots are equipped with advanced cameras and sensors, which utilize machine learning and computer vision algorithms to accurately identify and classify cracks based on size, shape, and propagation. Key challenges addressed in this study include the adaptation of vision algorithms for varied lighting and environmental conditions, as well as the robot’s navigation on complex dam surfaces. The system also incorporates data analytics for predictive maintenance, using real-time data gathered by robots to assess the structural health of dam walls over time. The results demonstrate the effectiveness of autonomous robots in detecting structural anomalies and assessing dam wall integrity, paving the way for safer and more efficient maintenance solutions in civil infrastructure.
Keywords: Dam walls, structural integrity, efficient water management, autonomous monitoring systems, and robots
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