2023
DOI: 10.28991/cej-2023-09-09-01
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Convolutional Neural Network for Predicting Failure Type in Concrete Cylinders During Compression Testing

Jose Manuel Palomino Ojeda,
Billy Alexis Cayatopa-Calderón,
Lenin Quiñones Huatangari
et al.

Abstract: Cracks in concrete cause structural damage, and it is important to identify and classify them. The objective of the research was to describe the behavior and predict the type of failure in concrete cylinders using convolutional neural networks. The methodology consisted of creating a database of 2650 images of failure types in concrete cylinders tested in compression at the Laboratory of Testing and Strength of Materials of the National University of Jaen, Cajamarca, Peru. To identify cracks on the concrete su… Show more

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Cited by 5 publications
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“…The MobileNet, DenseNet121, ResNet50, and VGG16 algorithms were used with 96, 91, 86, and 90% accuracy, with the MobileNet algorithm being the best predictor with 96%. The work presents an algorithm that can help assess the health of concrete and can also be coupled with the use of drones [7].…”
Section: Introductionmentioning
confidence: 99%
“…The MobileNet, DenseNet121, ResNet50, and VGG16 algorithms were used with 96, 91, 86, and 90% accuracy, with the MobileNet algorithm being the best predictor with 96%. The work presents an algorithm that can help assess the health of concrete and can also be coupled with the use of drones [7].…”
Section: Introductionmentioning
confidence: 99%