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

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 ۲۶۵۰ 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 (۶۰%), validation (۲۰%), and testing (۲۰%), and a transfer learning approach was developed using the MobileNet, DenseNet۱۲۱, ResNet۵۰, and VGG۱۶ algorithms from the Keras library, programmed in Python. To validate the performance of each model, the following indicators were used: recall, precision, and F۱ score. The results show that the models studied correctly classified the type of failure in concrete with accuracies of ۹۶, ۹۱, ۸۶, and ۹۰%, with the MobileNet algorithm being the best predictor with ۹۶%. 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: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۳-۰۹-۰۹-۰۱ Full Text: PDF

کلیدواژه ها:

Concrete ، Computer Vision ، Deep Learning ، Crack Detection ، Image Processing.