Deformation prediction of large-scale civil structures using spatiotemporal clustering and empirical mode decomposition-based long short-term memory network

Abstract

Structural deformation prediction is important for maintaining the serviceability and safety of civil infrastructure. However, current deep learning-based single prediction models face difficulties in accurately tracking deformation evolution of large-scale civil structures with numerous diverse monitoring data. In this study, a novel deformation prediction framework is proposed based on a spatiotemporal clustering algorithm and an empirical mode decomposition (EMD)-based long short-term memory (LSTM) network. Experiments on a large excavated foundation pit show that the proposed framework has a prediction error of RMSE less than 0.4 mm, outperforming LSTM models without using spatiotemporal clustering or EMD. Such high-precision predictions at many different monitoring locations demonstrate its promising application on large-scale structures. More deformation prediction experiments are required in the future to further validate the capability and performance of the proposed framework.