Bridge Damage Identification Research Based On Flexural Curvature Information Entropy And GA-BP Neural Network

In China,the aging problem of bridge structures is becoming increasingly severe and damage identification and health monitoring have became major research trends.Accurately and rapidly determining the location and extent of damage is a focal point of attention for scholars both domestically and internationally.However,there are still many pressing issues in bridge damage identification that need to be resolved,such as the difficulty in obtaining comprehensive field measurements,the influence of noise,the lack of raw data,and the challenges in quantitative damage analysis.Therefore,based on previous research and advancements in damage identification,this study combines the advantages of generalized flexibility matrices,informati on entropy,and modern intelligent algorithms to investigate structural damage.The main objectives are as follows:(1)Summarize and review domestic and international methods of bridge damage identification,providing a theoretical foundation and research direction.Emphasis is placed on damage identification methods based on flexibility matrices,information entropy,neural networks,and genetic algorithms.(2)To explore the location and extent of damage in simply supported beams and continuous beams,utilize generalized flexibility curvature matrices combined with information entropy to construct the Change Rate of Generalized Flexibility Curva ture Entropy(CRGFCE)index.Validate the accuracy of the index using different reduction coefficients for elastic modulus,analyze the degree of structural damage usi ng MATLAB polynomial fitting,and verify noise resistance by introducing various Gaussian white noises.Results demonstrate that the CRGFCE index can identify the different locations and extent of single and multiple point damage in simply supported beams and continuous beams.(3)To further validate the quantitative damage assessment of the index,introduce neural network methods for damage localization and prediction tasks in beam structures,and evaluate the feasibility and effectiveness of the Backpropagation(BP)and Convolutional Neural Network(CNN)algorithms for this task.Additionally,com bine the BP model with a genetic algorithm to design a GA-BP model,which impr oves prediction accuracy beyond existing models.(4)Implement the optimized GA-BP model in an engineering setting,design an interactive application with integrated functionalities for data import,feature extraction,damage prediction,and visualization.This serves as an attempt to apply the GA-BP model to real-world problem-solving.Figure: [63] table: [32] reference: [68]...