A nonlinear uniaxial stress-strain constitutive model for viscoelastic membrane materials. (October 2020) - Zhao, Bing; Hu, Jianhui; Chen, Wujun; Chen, Jianwen; Jing, Zhongliang
Abstract: Membrane materials with the excellent thermal, optical, electrical and chemical properties have attracted significant attention in numerous research fields recently. However, while being used to construct the membrane structures, the mechanical behaviors of membrane materials are more foundational than the other properties in evaluating the structure safety. This paper thus proposes a nonlinear stress-strain constitutive model for revealing the viscoelastic behaviors of membrane materials under uniaxial tensile loading. To this end, the constitutive equations for expressing the uniaxial tensile stress-strain relationships of viscoelastic materials are established gradually from the kinematic equations of the generalized Maxwell model that includes several basic Maxwell models and one basic spring element. Meanwhile, the uniaxial tensile tests of two typical viscoelastic membrane materials were carried out in order to examine the proposed constitutive model. The constitutive model parameters of the stress-strain properties of both membrane materials are accurately identified using the least square method. By comparing the true stress-strain curves between experimental results and constitutive models, good agreements with the maximum differences of 4.67% and 3.41% are acquired for the two employed viscoelastic membrane materials, respectively. These observations are able to validate the accuracy and efficiency of this proposed constitutive model in predicting the uniaxial stress-strain behaviors of viscoelastic membrane materials, which are significant in the nonlinear structural analysis of membrane structures. Highlights: A nonlinear constitutive model is proposed for viscoelastic membrane materials. Stress-strain constitutive equations are deduced by the generalized Maxwell model. Two typical membrane materials were tested uniaxially to get the experimental data. Model parameters were identified by fitting the data with the least square method. The 4.67% and 3.41% max. Errors for two materials can validate the proposed model.