Construction of an Intelligent Identification System for Concrete Bridge Cracks by Integrating Machine Learning and Neural Network Models

With the increasing use time and frequency of bridges, they will inevitably be subjected to a large number of traffic vehicles and various human damage. In addition, the severe erosion of the natural environment will cause different situations and degrees of damage to the surface of the beam. According to relevant data, cracks are the main factor causing damage to concrete bridges. Therefore, this paper combines machine learning and NN (Neural Network) model to build an intelligent identification system for concrete bridge cracks. Through the system, we can clearly see the recognition of the model on the bridge crack picture. The numerical representation on the diagonal of the Confusion matrix identifies the correct type, and the numerical representation not on the diagonal represents the wrong type. And the research results show that the classification accuracy of this algorithm for the verification set is high, close to the training set, and the Loss function is close to the Loss function of the training set on the basis of the overall fluctuation, so there is no over fitting phenomenon, the uncomfortable training results are good, and the training time is very similar. The algorithm proposed in this paper can accurately identify concrete surface crack images in a short period of time, indicating that the algorithm can be used for crack localization.