毫米波雷达数据驱动的桥梁挠度预测

为监测公路桥梁健康状况从而保证车辆行驶桥面的安全性，基于毫米波雷达监测的桥梁挠度数据，结合深度学习理论，提出了一种基于卷积神经网络（convolutional neural network, CNN）与门控制循环单元（gate recurrent unit, GRU）组合的桥梁挠度预测模型。首先，获取高速公路大桥高精度挠度数据，通过数据预处理，在保留原始数据特征的基础上，修复部分噪声数据；其次，将处理后的样本数据、时间步长和特征数的三维数据，以桥梁挠度数据序列构造的输入矩阵作为输入层，经过CNN-GRU组合模型的密集连接层后，输出预测桥梁挠度值。最后，选取具有代表性的监测点数据，利用均方根误差 (root mean square error, RMSE)、平均绝对误差 (mean absolute error, MAE)、平均百分比误差 (mean absolute percentage error, MAPE)进行预测效果验证。结果表明，CNN-GRU模型的精度更高：较于传统LSTM（long short-term memory）模型在RMSE上提升了59.65%，MAE提升了61.30%；较于CNN-LSTM模型在RMSE上提升了2.48%，MAE提升了4.87%。其对于桥梁挠度极值及趋势的判断基本准确，可以作为桥梁健康状况预测的科学依据。

Bridge Deflection Prediction Driven by Millimeter Wave Radar Data

To monitor the health status of highway bridges and ensure the safety of vehicle driving decks, with the combination of the bridge deflection data monitored by millimeter wave radar and deep learning theory, a bridge deflection prediction model based on the combination of convolutional neural network (CNN) and gate recurrent unit (GRU) is proposed. Firstly, the high-precision deflection data of the expressway bridge was monitored. Through data preprocessing, some noise data was repaired on the basis of retaining the original data characteristics. Secondly, the processed sample data, time step and three-dimensional data of feature number were taken as the input matrix, and the input matrix constructed by the bridge deflection data sequence was taken as the input layer. After passing through the dense connection layer of the CNN-GRU combined model, the predicted bridge deflection values became output. Finally, the root mean square error (RMSE), mean absolute error(MAE) and mean absolute percentage error(MAPE) were used to verify the prediction effect. The results show that the CNN-GRU model has higher accuracy: compared with the traditional long short-term memory (LSTM) model, RMSE is improved by 59.65 %, and MAE is improved by 61.30 %. Compared with the CNN-LSTM model, RMSE and MAE are increased by 2.48 % and 4.87 %. Its judgment on the extreme value and trend of bridge deflection is basically accurate and can be used as a bridge health.