Study of Concrete Dam Deformation Prediction Based on Temporal Convolutional Network and Transfer Learning
The data loss of the deformation measuring points of concrete dams or the short measurement time of the new installed points will lead to the insufficient amount of data,which will affect the accuracy of deformation prediction.In order to improve the prediction accuracy of these measuring points,a deformation prediction method based on temporal convolution network(TCN)and transfer learning was proposed.Taking the measuring points with sufficient data as the source domain and those lacking data as the target domain,the structure and parame-ters of the TCN model trained in the source domain were transferred to the target model,the frozen layer parameters were fixed,and the ad-justable layer parameters of the target domain model were adjusted by using the target data.Dynamic time warping was used to select the data with the highest similarity to the target data as the best source data to improve the transfer learning.The analysis of engineering example shows that the RMSE and MAE of the target model after transfer learning only have differences of 1.73%and 8.09%respectively compared with the prediction errors of TCN model trained with sufficient data,so that the accuracy of TCN prediction model with small data has been improved.
temporal convolutional networktransfer learningdynamic time warpingdeformation prediction
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