Modeling the Mechanical Response of Cement-Admixed Clay Under Different Stress Paths Using Recurrent Neural Networks

Cement–admixed clay (CAC) is a widely-used soil stabilization technique for enhancing the strength and stiffness of soft
clay. However, the stress–strain behavior of CAC is complex and nonlinear, and also depends on various factors such as
mixing proportion, confining pressure, stress path, and shearing condition. In this study, we propose a novel approach for
modeling the stress–strain behavior of CAC using recurrent neural networks (RNNs), which are a type of deep learning (DL)
technique that can well capture the temporal dependencies and nonlinearities in sequential data. We compare three types of
RNNs: traditional RNN, long short-term memory (LSTM) neural network, and gated recurrent unit (GRU) neural network,
and evaluate their performance in simulating the strain- and stress-controlled triaxial test results of 25 CAC specimens with
different mixing proportions and confining pressures. The results demonstrate that the LSTM model, incorporating a 2-time
step backward, exhibits superior prediction accuracy and generalization capability compared to other evaluated models,
achieving a mean absolute percentage error (MAPE) of 4%. This LSTM model is capable of capturing the stress–strain
behavior of CACs across various loading conditions and mixing proportions within a unified framework. Therefore, we suggest
that the LSTM model is a promising tool for modeling and analyzing the mechanical behavior of CAC in geotechnical
engineering applications.