BEHAVIOUR AND DURABILITY OF GRAPHENE CONCRETE COMPOSITE AGAINST ACID AND SULPHATE ATTACKS

M. El-Hawary, Mohammad Mataqi, A. Ben-Nakhi
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引用次数: 1

Abstract

Graphene is a Nano-material whose tensile strength is about two-hundred times higher as compared to steel. The effects of graphene-concrete composite on the properties of concrete have been investigated in this paper. A mix with a water-to-cement ratio of 0.4 was designed. The graphene powder was introduced as 0.5 percent, one percent, one-and-a-half percent and two percent (designated as GC2). The control mix was prepared without mixing the graphene powder. The same size and type of aggregates were used for all these concrete mixes. For each mix, resistance to acid (H2SO4) and sulphate (Na2SO4) attacks, compressive strength, water absorption, compressive strength development over time, compressive stress-strain relationship, split tensile strength, and modulus of rupture were determined. It was found that the addition of graphene increased the acid and sulphate resistance, and absorption. The acid resistance of concrete improved for all the employed mixes. The highest improvement was achieved for the GC2 mix. On the other hand, an overall decrease in water absorption of the mixes was noticed. Moreover, the addition of graphene increased the bulk and apparent density of the mix and decreased the volume of permeable voids.
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石墨烯混凝土复合材料抗酸和硫酸盐侵蚀的性能和耐久性
石墨烯是一种纳米材料,其抗拉强度是钢的200倍左右。本文研究了石墨烯-混凝土复合材料对混凝土性能的影响。设计了水灰比为0.4的混合料。石墨烯粉末分别为0.5%、1%、1.5%、2%(代号为GC2)。对照混合物制备时不掺入石墨烯粉末。所有这些混凝土混合料都使用了相同尺寸和类型的骨料。对于每种混合物,测定了抗酸(H2SO4)和硫酸盐(Na2SO4)侵蚀、抗压强度、吸水率、抗压强度随时间的发展、压应力-应变关系、劈裂抗拉强度和断裂模量。结果发现,石墨烯的加入增加了抗酸和硫酸盐的能力,以及吸收率。所有掺合料的混凝土耐酸性能均有提高。在GC2混合中取得了最大的改进。另一方面,注意到混合物的吸水率总体下降。此外,石墨烯的加入增加了混合物的体积和表观密度,减少了可渗透空隙的体积。
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FINITE ELEMENT ANALYSIS OF REINFORCED AND UNREINFORCED DEEP SOIL MIXING COLUMNS INSTALLED IN COHESIVE AND NON-COHESIVE SOILS BEHAVIOUR AND DURABILITY OF GRAPHENE CONCRETE COMPOSITE AGAINST ACID AND SULPHATE ATTACKS A NEW PERSPECTIVE FOR STABILITY ANALYSIS OF STRUCTURES HARNESSING POTENTIALS OF SOLAR RADIATION IN LIBERIA USING ARTIFICIAL NEURAL NETWORK ANALYSING HOUSE PRICE PREDICTIONS ACCORDING TO LIVING STANDARDS BASED ON MACHINE LEARNING METHODS
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