Nonlinear model updating algorithm for biaxial reinforced concrete constitutive models of shear walls

• Published in: Journal of Building Engineering Vol. 44; p. 103215
• Main Authors: Wang, Jia-Ji, Liu, Cheng, Nie, Xin, Fan, Jian-Sheng, Zhu, Ying-Jie
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2021
• Subjects: Biaxial constitutive model; Fixed-angle crack model; Nonlinear model updating; Optimization algorithm; RC shear wall; Fixed-angle crack model; Optimization algorithm; Nonlinear model updating; RC shear wall; Biaxial constitutive model
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2021.103215

A novel biaxial constitutive model and a nonlinear model updating algorithm are proposed and implemented in ABAQUS software for simulating the reinforced concrete (RC) shear walls. First, the proposed model is established using the User Material (UMAT) subroutine to be compatible with the multi-layer shell elements and membrane elements with ABAQUS implicit solver. The modeling scheme, the biaxial and uniaxial constitutive models of concrete, and the uniaxial models of rebars are elaborately illustrated. Second, this research modifies five critical parameters in the developed model to consider the influence of initial cracking and softening of concrete and slip of longitudinal rebars. The modified parameters include the concrete stiffness reduction factor αc, the concrete compressive softening factor ηc, the concrete shear softening strain γu, the steel stiffness reduction factor at initial tensile loading αs, and the steel stiffness reduction factor at peak load αu. Third, an optimization algorithm based on the discrete Fréchet distance is proposed to quantify and minimize the difference between test results and finite element (FE) simulation results of load-displacement curves. Subsequently, a total of 24 RC shear walls are obtained from the literature, and the optimization method is adopted to obtain the most favorable material constitutive parameters based on the force versus lateral displacement curves. The comparison shows the proposed model with the optimization method predicts high accuracy simulation results for RC shear walls. Finally, five neural networks are trained to predict the material constitutive parameters using the 24 RC shear wall tests, and the accuracy of the neural networks is deemed satisfactory. [Display omitted] •Five critical parameters are modified to consider cracking, softening of concrete, and rebar slip.•Nonlinear model updating algorithm is proposed for high-fidelity simulation of RC shear walls.•The stiffness, pinching, capacity and ductility of RC shear walls are well simulated.•The neural networks are developed to predict the constitutive parameters of concrete and steel.