Resumen
Recently, the bridge infrastructure in Ukraine has faced the problem of having a significant number of damaged bridges. It is obvious that the repair and restoration of bridges should be preceded by a procedure consisting of visual inspection and evaluation of the technical condition. The problem of fast and high-quality collection, processing and storing large datasets is gaining more and more relevance. An effective way to solve this problem is to use various machine learning methods in bridge infrastructure management. The purpose of this study was to create a model based on convolutional neural networks (CNNs) for classifying images of concrete bridge elements into four classes: ?defect free?, ?crack?, ?spalling? and ?popout?. The eight CNN models were created and used to conduct its training, validation and testing. In general, it can be stated that all CNN models showed high performance. The analysis of loss function (categorical cross-entropy) and quality measure (accuracy) showed that the model on the MobileNet architecture has optimal values (loss, 0.0264, and accuracy, 94.61%). This model can be used further without retraining, and it can classify images on datasets that it has not yet ?seen?. Practical use of such a model allows for the identification of three damage types.