Improved LSTM deep learning network approach for enhanced creep prediction in concrete-filled steel tubes

Abstract

The long-term performance of concrete-filled steel tubes (CFSTs), particularly creep at the member level and the structural-level creep effect, poses significant challenges to full-life cycle design. Conventional finite-element methods (FEMs) entail high computational costs and exhibit strong parameter dependency. For improvement, we propose a deep learning-based model for predicting CFST creep by leveraging the capabilities of a long short-term memory (LSTM) neural network and its improved versions. The predictions from the machine learning model were compared with experimental results and those obtained from FEM based on the Kelvin chain viscoelastic model. By comparing the performance of various machine learning approaches and FEM in predicting CFST creep, a reliable and efficient method is proposed to accurately predict the long-term creep behavior of CFSTs. Some suggestions are obtained: (1) The hyperparameters of all models were obtained by optimization algorithm. The improved LSTM model outperforms traditional machine learning algorithms and FEM in predicting CFST creep, and the CNN-LSTM-Attention model achieves the highest accuracy, with an R² of 0.92. (2) The prediction accuracy of the CNN-LSTM-Attention model was significantly improved by increasing the data acquisition frequency and sample size. Compared to smaller datasets, when the sample size was increased to 12,960, the R² of this model was raised from 0.92 to 0.96. (3) The future trend of CFST creep was predicted using the optimal CNN-LSTM-Attention model, and the prediction shows that the creep deformation rate gradually decreased, and the creep values tend to stabilize over the following 60 days.