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Item type:Publication, Structural Damage Detection Using an Unmanned Aerial Vehicle-Based 3D Model and Deep Learning on a Reinforced Concrete Arch Bridge(Multidisciplinary Digital Publishing Institute (MDPI), 2025)Visual inspection is a common method for detecting structural damage, but has limitations in terms of subjectivity, time, and access. This research proposes an innovative approach to identify cracks using a 3D model generated from photographs of an unmanned aerial vehicle (UAV) and the use of a convolutional neural network (CNN). These networks are effective in detecting complex patterns, improving the accuracy and efficiency of damage identification based on simple visual inspection. The case study is the old Villena Rey bridge in Lima, Peru. The methodology covers (i) the development of a 3D model of the bridge structure, (ii) the extraction of photographs of the model and its binary segmentation, (iii) the application of deep learning through the training and testing phase of a CNN to achieve crack detection in photographs, and (iv) damage location within the 3D model. An 88.4% accuracy was achieved in crack detection, identifying 18 damage points, of which 3 turned out to be false positives. Additionally, it was determined that the left pillar in the southern area of the bridge presented the highest concentration of damage, which underlines the effectiveness of the method used.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Damage Identification in Concrete Bridges Using Unmanned Aerial Vehicles and Neural Networks(Springer Science and Business Media Deutschland GmbH, 2025)Bridge monitoring systems using cameras and unmanned aerial vehicles (UAV) are increasingly being used worldwide. Additionally, artificial intelligence techniques are being used to improve performance in the structural damage detection and processing stage. This article shows a non-destructive methodology for damage identification using neural networks in a real bridge on the coast of Peru. The 104 m long Villena Rey bridge is the case study inaugurated in 1960 to improve the conditions and vehicular resilience of the Malecon de la Reserva avenue crossing in Lima. As a first step, many images were taken using photogrammetry with a UAV and the noise was filtered for data preparation. The data is then prepared and labeled to train the neural network model in conjunction with flexible training tools and an optimal architecture using one of the most efficient systems known as YOLOv7. The results show an optimal calibration of the system with percentages that exceed 60% in the identification of structural damage in bridges. Finally, this research work has a great contribution since it would be the first time that these modern technologies are used in developing countries such as Peru in South America.1