211. UPDATE OF NONLINEAR MODELS FOR REINFORCED MASONRY WALLS IN ASCE 41
Name: Jianyu Cheng
Grad Year: 2020
Reinforced masonry walls are widely used to resist the lateral forces from wind and seismic actions in buildings. They also carry the vertical gravity loads and serve architectural functions in normal service conditions. Thus, the behavior of RM walls is of great significance for life safety and property protection. However, in the current ASCE 41 code, the nonlinear behavior of the reinforced masonry walls is not accurately described. In this project, the nonlinear behaviors of flexure-dominated and shear-dominated reinforced masonry walls are analyzed. The monotonic and cyclic analysis have been conducted and compared to the experimental results. The flexure-dominated walls are modeled with beam-with-hinge and elastic shear spring models in OpenSEES. Fiber section models are applied to obtain the nonlinear behavior. The uniaxial material model for masonry and vertical reinforcement are calibrated. For masonry, an analogy to concrete is applied. For vertical reinforcement, the hysteresis uniaxial material model is used to account for the pinching and damage effects under cyclic loading. The buckling and fracture behaviors of bars are also taken into consideration for both monotonic and cyclic loading protocols. Furthermore, a method is proposed to develop a piecewise linear backbone curve of the flexure-dominated walls based on the moment-curvature relation of the section. Tables of normalized moment-curvature values are established. The comparison between the backbone curves obtained from the proposed tables and the experimental data is shown. The shear-dominated walls are modeled with empirical shear-spring models in OpenSEES. Pinching and damage effects are accounted. The critical values of the shear-spring model are suggested based on the experimental results. Similar to flexure-dominated walls, a table is established for the use of engineers. The corresponding backbone curves are compared to the experimental data.
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