Convolutional Neural Network for Predicting Failure Type in Concrete Cylinders During Compression Testing

Jose Manuel Palomino Ojeda; Billy Alexis Cayatopa-Calderón; Lenin Quiñones Huatangari; Jose Luí­s Piedra Tineo; Manuel Emilio Milla Pino; Wilmer Rojas Pintado

doi:10.28991/CEJ-2023-09-09-01

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Jose Manuel Palomino Ojeda Research Institute of Data Science, National University of Jaen, Jaen 06800, Cajamarca,, Peru
Billy Alexis Cayatopa-Calderón Seismological and Construction Research Institute, National University of Jaen, Jaen 06800, Cajamarca,, Peru
Lenin Quiñones Huatangari Research Institute of Data Science, National University of Jaen, Jaen 06800, Cajamarca,, Peru
Jose Luís Piedra Tineo Seismological and Construction Research Institute, National University of Jaen, Jaen 06800, Cajamarca,, Peru
Manuel Emilio Milla Pino
manuel.milla@unj.edu.pe
Faculty of Engineering, National University of Jaen, Jaen 06800, Cajamarca,, Peru https://orcid.org/0000-0003-3931-9804
Wilmer Rojas Pintado Seismological and Construction Research Institute, National University of Jaen, Jaen 06800, Cajamarca,, Peru

Vol. 9 No. 9 (2023): September

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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 surface, the database was divided into training (60%), validation (20%), and testing (20%), and a transfer learning approach was developed using the MobileNet, DenseNet121, ResNet50, and VGG16 algorithms from the Keras library, programmed in Python. To validate the performance of each model, the following indicators were used: recall, precision, and F1 score. The results show that the models studied correctly classified the type of failure in concrete with accuracies of 96, 91, 86, and 90%, with the MobileNet algorithm being the best predictor with 96%. The novelty of the study was the development of deep learning algorithms with different architectures that can be used in structural health assessment as an automated and reliable method compared to traditional ones. In addition, these trained algorithms can be used as source code in drones for structural monitoring.

Doi: 10.28991/CEJ-2023-09-09-01

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Publication Start Year:	2015
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Convolutional Neural Network for Predicting Failure Type in Concrete Cylinders During Compression Testing

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