A feature decomposition-based deep transfer learning framework for concrete dam deformation prediction with observational insufficiency

Abstract

Most current dam deformation prediction models (DDPM) are mainly based on long-term sequential data, but observational insufficiency is still a universal challenge in dam deformation forecasting for old dams with unreliable measurements or newly-built dams after short operation. This study proposes a feature decomposition-based deep transfer learning framework (FD-DTL) with short-term observation for concrete dam deformation prediction. Specifically, the variational mode decomposition (VMD), dispersion entropy (DE), and maximal information coefficient (MIC) are combined to decompose a single sequence into trend, periodic and random components. Then, dynamic time warping (DTW) is applied to match similar feature components from other dams and the target components. Based on transfer learning, convolutional neural network (CNN) and long short-term memory (LSTM), a combined prediction model (TL-CNN-LSTM) is designed for each pair of similar feature components, and it transfers the generic knowledge learned from similar feature components to the target ones. Eventually, the predicted displacement is obtained by superimposing all feature components. The engineering application verifies that the proposed framework can effectively process and predict dam displacement under observational insufficiency with considerable generalization ability and computational accuracy, which provides a novel approach for establishment of DDPM with observational insufficiency.