Application of BP Neural Network Improved by Fireworks Algorithm on Suspender Damage Prediction of Long-Span Half-Through Arch Bridge

In recent decades, with the large-scale construction and rapid development of half-through arch bridges, as well as the increase of bridge service time, the suspender damage of arch bridge has become increasingly prominent. Therefore, real-time monitoring and regular detection of the health of arch bridge suspenders and timely detection and accurate judgment of the damage location and extent of suspenders are of great engineering significance for evaluating the reliability and residual life of arch bridge structures. By analyzing the main difficulties and existing problems of suspender damage identification, this paper takes the change rate of modal curvature as the damage index, introduces fireworks algorithm into the neural network model, optimizes the optimization process of neural network weight and threshold, and proposes a prediction model based on improved BP neural network by fireworks algorithm. According to the measured data of the damage degree of a long-span arch bridge in daily monitoring and on-site inspection, the proposed prediction method is applied to verify the effectiveness and accuracy in engineering health detection. On this basis, the improved BP neural network by fireworks algorithm is used to predict the suspender damage of a certain long-span half-through arch bridge, which provides an important basis for the actual bridge safety assessment.

Automated summary

With the construction and development of half-through arch bridges and the increase of bridge service time, the damage to arch bridge suspenders has become more prominent. Real-time monitoring and regular detection of suspender health, as well as timely and accurate detection of damage location and extent, are of great importance in evaluating reliability and residual life. This paper proposes a prediction model based on an improved BP neural network by fireworks algorithm to address suspender damage identification difficulties. The model is verified and applied to a long-span arch bridge, providing a basis for bridge safety assessment.