Research on Intelligent Inspection Method of Prestressed Bridge Engineering Based on Machine Learning

Abstract

Abstract As an important engineering structure for national and regional transportation infrastructure construction, bridges have important economic, social, and strategic significance. The research centers on the intelligent detection of prestressed bridge engineering, on the one hand, combined with the finite element analysis of the prestressed beam modal in the obtained area, based on LS-SVM to construct the intelligent detection method of effective prestressing of bridge engineering. On the other hand, the ResNet neural network is selected for feature extraction of bridge characteristic parameters, and LSTM is combined to complete the fusion of bridge spatiotemporal features to construct an intelligent detection model of bridge technical condition based on the ResNet-LSTM joint network. The detection performance of the two methods is evaluated through simulation and experimental tests on the dataset. The analysis shows that the maximum error for effective prestress detection of the LS-SVM model is 15.584%, which is 6.121% lower than that of the BP neural network model. The technical condition detection error of less than 0.1 is basically greater than 90% in both discontinuous and continuous time-span detection. It has been verified that the LS-SVM model has a better identification effect on effective prestressing, while the ResNet-LSTM model has a high accuracy prediction effect on the technical condition of the bridge.