Prediction of Axial Compressive Load–Strain Curves of Circular Concrete-Filled Steel Tube Columns Using Long Short-Term Memory Network

Abstract

No study has been reported to use machine learning methods to predict the full-range test curves of circular CFST columns. In this paper, the long short-term memory (LSTM) network was introduced to calculate the axially compressive load–strain curves of the circular CFST columns according to an experiment database of limited scale. To improve the feasibility of input data for the recurrent neural network algorithm, data preprocessing methods and data configurations were discussed. The prediction results indicate that the LSTM network provides more accurate estimations compared with the artificial neural networks, random forest and support vector regression. Meanwhile, this method can be used to calculate the mechanical properties including the elastic modulus, ultimate bearing capacity, and the ductility of the columns with acceptable accuracy for engineering practice (the prediction error within 20%). For future research, it is expected that the machine learning method will be applied to predict the structural response of different members under various loading conditions.

Keywords

• concrete-filled steel tubular stub columns
• axial load–strain curve
• machine learning
• artificial neural network
• long short-term memory network
• machine-aided intelligent design