Demolished concrete lumps (DCLs) have been introduced as partial coarse aggregate replacements in concrete casting process. However, limited research works have been performed on sensitive behaviors of slenderness concrete filled steel tube (CFST) filled with fresh concrete (FC) and DCLs under axial compression. Based on the previous research work, orthogonal design method was employed to investigate further mechanical performance. Experimental studied involved 12 columns of CFST with diameter 159 mm and lengths 2000, 2200 and 2400 mm. The ratios of the diameter-to-thickness (D/t) were 79, 53 and 40, while replacement ratios of FC in DCLs were 0%, 20%, 40% and 60%, respectively. The results of experimental tests of the CFST filled with DCLs and FC indicated that ratio of slenderness have a greatest effect on the bearing capacity of slender column under axial compression, followed by the D/t ratio, and the weakest effect was observed for DCLs replacement. DCLs slightly affected the mechanical performances of slender CFST columns, so the ultimate bearing capacity of CFST can ignore the influence of replacement rate of DCLs. To calculate CFST slender column ultimate bearing capacity with DCLs, design codes AIJ, AISC, CE4, CECS 28: 2012, DL/T 5085-1999 were used to predicted ultimate bearing capacity, found that the code CECS 28:2012 was employed for the calculation of slender CFST DCLs filled column bearing capacity under axial compression being comparable accuracy.
Abstract: Sprayed steel fiber reinforced recycled concrete (SSFRC), as a green product of small particle size recycled aggregate (RA), was tested for compressive performance using an electro-hydraulic servo pressure testing machine. The replacement rate of RA, the volume ratio of Steel Fiber (SF), and the strain-stress curve of SSFRC were studied. The relationship between the axial compressive strength and the compressive constitutive model of SSFRC was proposed, and theoretical support for expanding the engineering application of recycled concrete (RC) was provided.
Abstract Sprayed steel fiber recycled concrete (SSFRC) is a new type of green composite building material. This study examines the impact of water-cement ratio, recycled aggregate (RA) replacement rate, and steel fiber (SF) volume ratio on concrete properties such as slump, compressive strength, and bending strength. To accomplish this, 9 orthogonal experimental designs with 3 factors and 3 levels are utilized. The primary and secondary order of factors affecting the slump, compressive strength, and bending strength of fresh concrete are obtained by analyzing experimental data. To calculate the mix ratio, the mathematical model is obtained by controlling single factor variables and fitting relationships of relevant factor data based on the analysis results of the significance of orthogonal experiments. By investigating the replacement rates of 0%, 50%, and 100% RA, we examined the ideal sand content for SSFRC. The slump values of 24 mix ratio tests were analyzed, leading to the identification of the optimal outcomes associated with sand content percentages of 49%, 51%, and 53%. Finally, we obtained the calculation method for the mix ratio of SSFRC. Based on the example calculation and verification presented in this article, it can be concluded that the mix design method proposed for SSFRC is accurate, simple, convenient, and practical.
In recent years, the construction industry has developed rapidly. As the largest amount of building materials, the consumption of concrete is increasing day by day. As a kind of green and healthy concrete, the development and application of recycled concrete(RC) can not only alleviate the occupation of natural resources by concrete and ensure the sustainable development of human society, but also have important significance for resource recycling and environmental protection. Based on this, this paper puts forward artificial intelligence algorithm(AIA), discusses its application in RC bridge engineering, and briefly analyzes the basic mechanical properties, processing technology and mix design of RC; The grey neural network predictive control of the durability index of RC bridge is discussed, and the network output results of the predicted and measured strength of RC bridge of AIA are measured through experiments. The results show that the predicted results have a certain guiding effect on the subsequent construction control, and verify the feasibility of applying AIA to RC bridge engineering proposed in this paper.
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