Large bridge structures are pivotal projects in the transportation system and play a crucial role in social life. With the frequent occurrence of bridge accidents, people are paying more and more attention to the safety of bridge structures. However, existing bridge structure damage identification methods have problems such as low recognition accuracy, high damage localization error rate, and poor recognition effect. In response to the appeal issue, this article used data processing methods based on static test data to denoise and clean the experimental data re-collected from static test data, and obtained effective bridge structural damage data. With the help of these data and backpropagation (BP) neural network, a bridge structural damage identification pattern was constructed. Using this pattern to identify bridge structural damage can effectively address the issues of low identification accuracy and high damage localization error rate. Through experiments, it can be found that the recognition pattern based on BP neural network had an accuracy of over 92.16%, 93.44%, and 94.13% for extracting displacement, strain, and deflection of bridges, respectively. The average recognition accuracy was 95.038%, 94.696%, and 95.27%, respectively. Using static test data and BP neural network to construct a bridge structural damage identification pattern can effectively improve the accuracy of bridge structural damage identification and reduce the error rate of identification and positioning.

大型桥梁结构是交通运输系统的关键工程，在社会生活中发挥着至关重要的作用。随着桥梁事故的频繁发生，人们对桥梁结构的安全性越来越关注。然而，现有的桥梁结构损伤识别方法存在识别精度低、损伤定位错误率高、识别效果差等问题。针对上诉问题，本文采用基于静力试验数据的数据处理方法，对静力试验数据重新采集的实验数据进行去噪和清洗，得到有效的桥梁结构损伤数据。借助这些数据和反向传播（BP）神经网络，构建了桥梁结构损伤识别模式。利用该模式进行桥梁结构损伤识别，可以有效解决识别精度低、损伤定位错误率高等问题。通过实验发现，基于BP神经网络的识别模式对桥梁位移、应变、挠度的提取准确率分别达到92.16%、93.44%、94.13%以上。平均识别准确率分别为95.038%、94.696%和95.27%。利用静态测试数据和BP神经网络构建桥梁结构损伤识别模式，可以有效提高桥梁结构损伤识别的准确性，降低识别定位的错误率。