• Structural Engineering and Mechanics
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• v.85 no.1
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• pp.29-53
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• 2023

High-strength shielding concrete against gamma radiation is a priority for many medical and industrial facilities. This paper aimed to investigate the gamma-ray shielding properties of high-strength hematite concrete mixed with silica fume (SF) with nanoparticles of lead dioxide (PbO₂), tungsten oxide (WO₃), and bismuth oxide (Bi₂O₃). The effect of mixing steel fibres with the aforementioned binders was also investigated. The reference mixture was prepared for high-strength concrete (HSCC) containing 100% hematite coarse and fine aggregate. Thirteen mixtures containing 5% SF and nanoparticles of PbO₂, WO₃, and Bi₂O₃ (2%, 5%, and 7% of the cement mass, respectively) were prepared. Steel fibres were added at a volume ratio of 0.28% of the volume of concrete with 5% of nanoparticles. The slump test was conducted to workability of fresh concrete Unit weight water permeability, compressive strength, splitting tensile strength, flexural strength, and modulus of elasticity tests were conducted to assess concrete's engineering properties at 28 days. Gamma-ray radiation of 137Cs emits photons with an energy of 662 keV, and that of 60Co emits two photons with energies of 1173 and 1332 keV were applied on concrete specimens to assess radiation shielding properties. Nanoparticles partially replacing cement reduced slump in workability of fresh concrete. The compressive strength of mixtures, including nanoparticles was shown to be greater, achieving 94.5 MPa for the mixture consisting of 7.5 PbO2. In contrast, the mixture (5PbO₂-F) containing steel fibres achieved the highest values for splitting tensile, flexural strength, and modulus of elasticity (11.71, 15.97, and 42,840 MPa, respectively). High-strength shielded concrete (7.5PbO2) showed the best radiation protection. It also showed the minimum concrete thickness required to prevent the transmission of radiation.

• Proceedings of the Korea Concrete Institute Conference
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• 1994.10a
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• pp.223-228
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• 1994

The strength development of polymer concrete using an unsaturated polyester polymer varies depending on many factors. However, the strength development is mostly dependent upon the age and curing temperature if the mixture ratios are the same. This study conducted to experimentally describe the relationship between the strength development and maturity which is defined as a function of $\Sigma$(time $\times$ temperature). The research result may be applied to predict the compressive, tensile and splitting strengths of the polymer concrete by computing the maturinty of the concrete.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.391-396
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• 2003

The concrete corbels consist of various failure mechanisms such as the yielding of the tension reinforcement, the crushing or splitting from compression concrete struts, and localized bearing or shearing failure under the loading plate. However, predicting those failure mechanisms is very difficult. In this study, the ACI 318-02, the softened strut-tie model approach, and the nonlinear strut-tie model approach are applied to ultimate strength analysis of normal strength concrete corbels tested to failure. From the result of the analysis, an effective analysis and design method of normal strength concrete corbels is suggested.

• Computers and Concrete
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• v.22 no.2
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• pp.249-259
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• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.11a
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• pp.423-426
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• 2001

In this paper a large scale direct tension test of plain concrete is represented. Two independently controlled actuators were used to ensure a homogeneous tensile field and to avoid secondary flexural stresses. Fracture energies evaluated by a classical prediction equation and this test are compared. The result indicated that the classical prediction equation is not adequate to predict the fracture energy of large sized specimens. From this test, it was determined that the fracture energy obtained from large scale direct tension tests is significantly higher than the one obtained in wedge splitting tests on laboratory sized specimens. But the tensile strength was about half the value determined from splitting tensile strength test with cylindrical specimens.

• Journal of the Korea Concrete Institute
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• v.25 no.5
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• pp.573-581
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• 2013

In this paper, an experimental investigation of bond properties between steel fiber reinforced concrete and glass fiber reinforced polymer reinforcements was performed. The experimental variables were diameter of reinforcements, volume fraction of steel fiber, cover thickness and compressive strength of concrete. Bond failure mainly occurred with splitting of concrete cover. Main factor for splitting of concrete is tension force occurred by the displacement difference between reinforcements and concrete. Therefore, in order to prevent the bond failure between reinforcements and concrete, capacity of tensile strength of concrete cover should be upgraded. As a results of test, volume fraction of steel fiber significantly increases the bond strength. Cover thickness changes the failure mode. Diameter of reinforcements also changes the failure mode. Generally, diameter of reinforcement also affects the bond properties but this effect is not significant as volume fraction of fiber. Increase of compressive strength increases the bond strength between concrete and reinforcement because compressive strength of concrete directly affects the tensile strength of concrete.

• Proceedings of the Korea Concrete Institute Conference
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• 1995.04a
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• pp.30-35
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• 1995

This paper presents fundamental experiment for the properties of high performance concrete in its fresh and hardened state made with ground granulated blast-furnace (GGBF) slag. The result is that the effect of decreasing xoncrete temperature is to the mixing ratio of GGBF slag, but it presents disadvantage in the slump loss phase. In addition to, we know that the splitting tensile strength, compressive strength and elastic modulus of concrete mixed with high fineness GGBF slag are increased at age 28days.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.3
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• pp.81-91
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• 1989

The aims of this study were to determine mechanical properties of steel-fiber reinforced concrete under splitting tensile, flexural and compressive loading, and thus to improve the possible applications of concrete. The major factors experimentally investigated in this study were the fiber content and the length and the diameter of fibers. The major results obtained are summarized as follows : 1.The strength, strain, elastic modulus and energy obsorption capability of steel-fiber reinforced concrete under splitting tensile loading were significantly improved by increasing the fiber content or the aspect ratio. 2.The flexural strength, central deflection, and flexural toughness of steel4iber reinforced beams were significantly improved by increasing the fiber content or the aspect ratio. And flexural behavior characteristic was good at the aspect ratio of about 60 to 75. 3.The strength, strain, and energy absorption capability in compression were increased with the increase of the fiber content. These effects were not so sensitive to the aspect ratio. The energy absorption capability was improved only slightly with the increase of the fiber length. 4.The elastic modulus, transverse strains, and poisson's ratios in compression were not influenced by the fiber content. 5.The steel-fibers were considered to be appropriated as the materials covering the weakness of concrete because the mechanical properties of concrete in tension and flexure were significantly improved by steel-fiber reinforcement.

• Steel and Composite Structures
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• v.39 no.1
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• pp.81-93
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• 2021

Splitting tensile strength (STS) is an important mechanical parameter of concrete. This study offers novel methodologies for the early prediction of this parameter. Artificial neural network (ANN), which is a leading predictive method, is synthesized with two metaheuristic algorithms, namely atom search optimization (ASO) and equilibrium optimizer (EO) to achieve an optimal tuning of the weights and biases. The models are applied to data collected from the published literature. The sensitivity of the ASO and EO to the population size is first investigated, and then, proper configurations of the ASO-NN and EO-NN are compared to the conventional ANN. Evaluating the prediction results revealed the excellent efficiency of EO in optimizing the ANN. Accuracy improvements attained by this algorithm were 13.26 and 11.41% in terms of root mean square error and mean absolute error, respectively. Moreover, it raised the correlation from 0.89958 to 0.92722. This is while the results of the conventional ANN were slightly better than ASO-NN. The EO was also a faster optimizer than ASO. Based on these findings, the combination of the ANN and EO can be an efficient non-destructive tool for predicting the STS.

• Journal of the Korea Concrete Institute
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• v.16 no.6 s.84
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• pp.759-766
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• 2004

The addition of steel fiber with concrete significantly improves the engineering properties of structural members, notably shear strength. The purpose of this study is to determine the steel fiber shape, aspect ratio and volume fraction ratio in a point of practical usage as structural members. Steel fiber factor and volume fraction are also considered to verify the strengthening effect in member level. From the reviewing of previous researches and analyzing of consecutive material test results, the optimum shape and length of steel fiber, which can have a good strengthening effects were defined as a hooked end type and larger than 1.5 times of maximum gravel size. Analyzing the test results of strength and deformation capacity, aspect ratio 75 and volume fraction $1.5\%$ can be having a maximum strengthening effect of steel fiber. Also steel fiber factor, tensile splitting strength, and flexural strength are found as key parameter in shear strengthening effect in member level.