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Experimental and numerical evaluation of confined masonry walls retrofitted with engineered cementitious composites
Highlights 12 reverse cyclic tests on unreinforced and ECC-retrofitted confined masonry (CM) walls. Finite element (FE) models were developed to assess ECC-retrofitted CM walls. The behaviors of unretrofitted and retrofitted walls under monotonic loading are effectively simulated. The numerical sensitivity analysis for the response of the walls is conducted.
Abstract This study presents experimental and numerical research on the in-plane behavior of unreinforced confined masonry walls retrofitted using engineered cementitious composite (ECC) coatings. The experimental results, obtained from cyclic loading tests, demonstrated that this retrofitting technique effectively increased the lateral strength of the walls and changed the failure modes under specific conditions. Simplified micro finite element models were established, in which non-thickness cohesive elements were used to represent the mortar joints, and the material model of the mortar was implemented in a subroutine and linked with ABAQUS finite element software using the VUMAT interface. The mechanical behaviors of the ECC were simulated using the concrete damaged plasticity model. The predictive capacity of the numerical models was verified through a comparable study between the simulation results and experimental data. The models, which were applied under monotonic loading, could adequately reproduce the main behavior of the experimental specimens. Subsequently, a numerical sensitivity analysis was conducted, the results of which suggested that the in-plane behavior of the retrofitted walls were significantly affected by the tensile properties of the ECC, and that all walls were affected by the mortar strength mainly in terms of the cracking load and stiffness.
Experimental and numerical evaluation of confined masonry walls retrofitted with engineered cementitious composites
Highlights 12 reverse cyclic tests on unreinforced and ECC-retrofitted confined masonry (CM) walls. Finite element (FE) models were developed to assess ECC-retrofitted CM walls. The behaviors of unretrofitted and retrofitted walls under monotonic loading are effectively simulated. The numerical sensitivity analysis for the response of the walls is conducted.
Abstract This study presents experimental and numerical research on the in-plane behavior of unreinforced confined masonry walls retrofitted using engineered cementitious composite (ECC) coatings. The experimental results, obtained from cyclic loading tests, demonstrated that this retrofitting technique effectively increased the lateral strength of the walls and changed the failure modes under specific conditions. Simplified micro finite element models were established, in which non-thickness cohesive elements were used to represent the mortar joints, and the material model of the mortar was implemented in a subroutine and linked with ABAQUS finite element software using the VUMAT interface. The mechanical behaviors of the ECC were simulated using the concrete damaged plasticity model. The predictive capacity of the numerical models was verified through a comparable study between the simulation results and experimental data. The models, which were applied under monotonic loading, could adequately reproduce the main behavior of the experimental specimens. Subsequently, a numerical sensitivity analysis was conducted, the results of which suggested that the in-plane behavior of the retrofitted walls were significantly affected by the tensile properties of the ECC, and that all walls were affected by the mortar strength mainly in terms of the cracking load and stiffness.
Experimental and numerical evaluation of confined masonry walls retrofitted with engineered cementitious composites
Deng, Mingke (author) / Yang, Shuo (author)
Engineering Structures ; 207
2020-01-15
Article (Journal)
Electronic Resource
English
Cyclic testing of unreinforced masonry walls retrofitted with engineered cementitious composites
British Library Online Contents | 2018
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