Micro-structural and mechanical properties of PVA fiber reinforced engineered cementitious composite incorporating natural and artificial pozzolanic materials under different temperatures

被引:0
|
作者
Mousavinejad, Seyed Hosein Ghasemzadeh [1 ]
Alemi, Mir Parsa [2 ]
机构
[1] Univ Guilan, Dept Civil Engn, Fac Engn, Rasht, Iran
[2] Univ Guilan, Dept Civil Engn, Rasht, Iran
关键词
Engineered cementitious composite; Polyvinyl alcohol fiber; Compressive strength; Direct tensile strength; Modulus of elasticity; Regression-based model;
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Engineered cementitious composites (ECC), which have high toughness and tensile ductility, as well as multiple fine cracking, satisfy the high safety and durability criteria in the development of sustainable infrastructures. The effect of different temperatures on the mechanical characteristics of polyvinyl alcohol (PVA) reinforced ECC (PVA-ECC) was examined in this work. A study was conducted to compare the modulus of elasticity, tensile strength, and compressive strength of samples containing various dosages of three available pozzolans: zeolites (ZE), fly ash (FA), and ground granulated blast furnace slag (GGBS), as well as silica fume (SF). Scanning electron microscopy was used to examine the microstructure of PVA-ECC samples subjected to various high temperatures (SEM). For 1 h, all specimens were exposed to temperatures of 150, 100, 50, and 20 degrees C. The experimental findings revealed that when the temperature rises to 50 degrees, all three mechanical parameters, modulus of elasticity, tensile strength, and compressive strength, increase, then drop. The study of the microstructure of PVA-ECC reveals a basic explanation for the loss of macro mechanical characteristics. Finally, using ANOVA analysis, the relationship between independent parameters such as the age of the sample (AS), temperature (T), and water to pozzolan materials (W/P), and dependent parameters (i.e., compressive strength, tensile strength, and modulus of elasticity) was evaluated, and the multivariate linear regression (MLR) and the artificial neural network (ANN) were shown to be useful for modeling.
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页数:15
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