Statistical analysis of the compressive strength of fabric-reinforced cementitious matrix (FRCM)-confined masonry columns

被引:0
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作者
Jing, Lei [1 ,2 ]
Yin, Shiping [1 ,3 ]
Aslani, Farhad [2 ]
机构
[1] Jiangsu Key Laboratory of Environmental Impact and Structural Safety in Engineering, School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou,Jiangsu,221116, China
[2] Materials and Structures Innovation Group, School of Engineering, University of Western Australia, Perth,WA,6009, Australia
[3] State Key Laboratory for Geomechanics and Deep Underground Engineering, School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou,Jiangsu,221116, China
关键词
Application prospect - Cementitious matrices - Characteristic value - Compressive performance - Mechanical performance - Probabilistic methods - Uniaxial compressive tests - Unreinforced masonry;
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学科分类号
摘要
Strengthening is a valid measure for improving the mechanical performance of unreinforced masonry (URM) structures. Fabric-reinforced cementitious matrix (FRCM) is effectively compatible with masonry materials due to the use of an inorganic matrix. Therefore, FRCM boasts extensive application prospects in strengthening URM structures. Nevertheless, further research still needs to be conducted on the compressive performance of FRCM-confined masonry columns. In this paper, the uniaxial compressive test results of seventy-five FRCM-confined masonry columns were collected from the available literature. Two existing prediction models of the compressive strength of FRCM-confined masonry columns were adopted, and a model was also proposed based on the regression analysis of the collected data. The prediction accuracies of the three models were compared and analyzed, and the characteristic values of the compressive strength of FRCM-confined masonry columns were further obtained via a probabilistic method. These research results can be used to guide the compressive strength design of FRCM-confined masonry columns. © 2021, RILEM.
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