Optimisation of layered shell model for analysis of reinforced concrete shear walls based on machine learning

Layered shell modelling is an effective tool for the efficient simulation of two-dimensional structural members. As machine learning (ML) provides a novel alternative for the optimisation analysis of the traditional layered shell model, it has been widely used in civil engineering applications. This study focuses on the accurate numerical simulation of reinforced concrete (RC) shear wall structures using the ML method. The user subprogram interface HYPELA2 based on the implicit solver of Marc software was compiled to implement a program package to consider the precise constitutive model of concrete. Additionally, different ML methods and loss functions were compared, and the adopted optimisation method was based on the particle swarm optimisation and L1 loss function. The analysis of constitutive parameters was realised according to the optimisation of the ML algorithm. Multiple numerical models were used to verify the stability and accuracy of the proposed layered shell model and ML method. Finally, the results obtained from comprehensive experimental research and numerical simulations were used to determine the recommended values of parameters for finite element analysis of RC shear walls to produce a novel high-precision, efficient, and universal calculation model.