A deep LSTM-based constitutive model for describing the impact characteristics of concrete-granite composites with different roughness interfaces.

In this work, the dynamic mechanical properties of concrete‒granite composites with various roughness interfaces were investigated via split Hopkinson pressure bar (SHPB) system to evaluate the impact resistance of the lining‒surrounding rock composite structure that is commonly present in rock engineering. The dynamic uniaxial compressive strength of the composite at an impact speed of 11.3 m/s may increase by 20.55% when the joint roughness coefficient (JRC) increases from 0 to 28.64 according to the experimental results. The JRC increased the strain rate effect of the composite but reduced the confining pressure effect. The relationships between the dynamic deformation parameters, such as the elastic modulus and critical strain, and the study variables, as well as the correlation mechanism between the macroscopic and microscopic failure morphologies of the composite and the stress‒strain curves, were investigated. A developed long short-term memory (LSTM) deep learning method was utilized to predict the dynamic stress‒strain relationships of the composites after 144 sets of experimental data were split into training and testing sets. The prediction issue of peak stress for the composites after varying time steps was presented to the recursive neural network LSTM, which was evaluated and compared with the traditional back propagation neural network (BPNN) and random forest (RF). The LSTM model showed the strongest prediction capacity when considering accuracy and predictive evaluation indicators. Compared with the BPNN model, the RF model was worse at capturing the viscoelastic plastic properties of the constitutive model while having superior assessment indicators.