Health diagnosis of concrete dams with continuous missing data for assessing structural deformation based on tSNE–AHC algorithm and deep transfer learning

Abstract

To address the issue of health diagnosis for concrete dams with continuous missing data, this study aimed to develop and validate a health diagnosis model based on domain learning. Initially, the target measuring point is precisely matched with a source measuring point via the tSNE–AHC algorithm (by combining t-distributed stochastic neighbor embedding and agglomerative hierarchical clustering). Subsequently, TR-CNN-LSTM models based on convolutional neural networks, long and short-term memory and transfer learning are built to transfer the knowledge of dam deformation learned from the source measurement points to the target measurement point. Finally, the performance is quantified under different degrees of missing data. The case study revealed that the established model offers optimal prediction accuracy and robustness, which conceptualizes a new approach to structural health monitoring.