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

• Published in: Journal of Building Engineering Vol. 77; p. 107434
• Main Authors: Tao, Mu-Xuan, Wang, Yu-Lun, Zhao, Ji-Zhi, Wang, Chen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.10.2023
• Subjects: Constitutive model; Layered shell element; Loss function; Machine learning; Particle swarm optimisation; Reinforced concrete shear wall; Loss function; Constitutive model; Layered shell element; Reinforced concrete shear wall; Particle swarm optimisation; Machine learning
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2023.107434

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. •A precise and efficient finite element model based on layered shell elements was developed.•Six parameters that significantly influenced the hysteretic behaviour of RC shear walls were identified.•A comparative analysis of different ML optimisation methods and loss functions were determined.•A parameter analysis was performed based on the optimised parameters of different RC shear walls.•The corresponding constitutive parameters are suggested according to the analysis.