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Abstract

With the rapid advancement of the social economy and the rapid increase in the number of transportation vehicles, bridge health monitoring has become increasingly important. Using information technology to analyze data and identify damage to bridge structures can effectively ensure bridge safety, thereby avoiding traffic accidents. The current data analysis and damage identification methods have limitations, including poor real-time performance and low accuracy. An improved support vector machine algorithm is developed, in this study, for real-time monitoring data classification. Moreover, a bridge structure damage identification model is proposed based on improved support vector machine and data preprocessing. When compared and analyzed alongside other algorithms, it was found that the accuracy and precision of the improved support vector machine algorithm were 97.4% and 95.7% respectively, outperforming the other algorithms. Subsequently, a performance comparison analysis was conducted between the proposed recognition model and other models. Results denoted that the mean running time of the model was 38.1 s, outperforming the comparison models. The results demonstrated that the improved support vector machine algorithm and recognition model proposed in the study are effective and can help improve the analysis efficiency of bridge monitoring data and the accuracy of identifying bridge structural damage, providing a theoretical basis for bridge structural damage identification.

Keywords

PCA SVM PSO bridge structure damage identification

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Bridge structural damage identification model based on data preprocessing and real-time monitoring data

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