基于BP神经网络的桥梁损伤识别研究

Research on Bridge Damage Identification Based on BP Neural Network

桥梁往往会随时间推移而退化,为避免造成生命财产安全问题,对桥梁结构上监测到的数据进行识别、预测与分析,有利于尽早发现损伤部位并判断其损伤类型。因此,构建BP神经网络对桥梁监测数据进行特征提取,对多类数据进行识别与分类;采用贝叶斯算法进行超参数优化,同时使用经超参数优化后的决策树、邻近算法、随机森林和支持向量机4种机器学习算法进行对照实验。结果证明,所构建的BP神经网络具有更优异的性能,预测正确率达到0.930 2,并且精确率、召回率和F1指数指标也表现较好。

Bridges tend to deteriorate over time. In order to avoid life and property safety problems, identifying, predicting and analyzing the data monitored on the bridge structure is helpful to find the damage location and judge its damage type as soon as possible. Therefore, BP neural network is constructed to extract the characteristics of bridge monitoring data and identify and classify multi class data. Bayesian algorithm is used for hyperparametric optimization, and four machine learning algorithms after hyperparametric optimization, such as decision tree, neighbor algorithm, random forest and support vector machine, are used as control experiments. The experimental results show that the BP neural network has better performance, the prediction accuracy reaches 0.930 2, and its accuracy, recall and F1 index are also better.