• Proceedings of the Korea Concrete Institute Conference
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• 1991.10a
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• pp.119-123
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• 1991

The concrete structures subjected to strong earthquakes may undergo hysteretic behavior and result in severe damage. The inelastic behavior and steffness degradation due to seismic loading must be properly modeled. The present study proposes a realistic model to assess the structural damage of concrete structures under seismic loadings. The present model also takes into account the probabilistic nature of seimic loading and thus the randomness of motion.

• Earthquakes and Structures
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• v.14 no.3
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• pp.273-283
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• 2018

Previous experimental researches indicate that reinforced concrete beam-column joints play an important role in the mechanical properties of moment resisting frame structures, so as to require proper design. In order to get better understanding of the beam-column joint performance, a rational model needs to be developed. Based on the former considerations, two typical models for calculating the shear carrying capacity of the beam-column joint including the inelastic reinforced concrete joint model and the softened strut-and-tie model are selected to be introduced and analyzed. After examining the applicability of two typical models mentioned earlier to interior beam-column joints, several adjustments are made to get better predicting of the test results. For the softened strut-and-tie model, four adjustments including modifications of the depth of the diagonal strut, the inclination angle of diagonal compression strut, the smeared stress of mild steel bars embedded in concrete, as well as the softening coefficient are made. While two adjustments for the inelastic reinforced concrete joint model including modifications of the confinement effect due to the column axial load and the correction coefficient for high concrete are made. It has been proved by test data that predicted results by the improved softened strut-and-tie model or the modified inelastic reinforced concrete joint model are consistent with the test data and conservative. Based on the test results, it is also not difficult to find that the improved beam-column joint model can be used to predict the joint carrying capacity and cracks development with sufficient accuracy.

• Journal of the Korean Society of Safety
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• v.27 no.2
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• pp.49-56
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• 2012

If cement can be manufactured with industrial byproducts such as granulated blast furnace slag, phosphogypsum, and waste lime instead of clinker, there would be many advantages, including maximum use of these industrial byproducts for high value-added resources, conservation of natural resources and energy by omitting the use of clinker, minimized environmental pollution problems caused by CO2 discharge, and reduction of the production cost. By this reason, in this study, mechanical behavior tests of non-burnt cement concrete were performed, and elasticity modulus and stress-strain relationship of non-burnt cement concrete were proposed. 6 test members were manufactured and tested according to reinforcement ratio and concrete compressive strength. By the test results, there was no difference between ordinary concrete and non-burnt cement concrete of flexural behavior. In order to verify the proposed non-burnt cement concrete model, nonlinear analytical model was derived by using strain compatibility method. By the results of comparison between test results, ordinary concrete model and proposed model, The proposed model well predicted the flexural behavior of non-burnt cement concrete.

• Journal of the Korea Concrete Institute
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• v.13 no.5
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• pp.415-422
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• 2001

The present paper focuses on the development of a realistic analysis model for the deformation calculation of reinforced concrete beams subjected to fatigue loadings. The proposed model considers the effect of cyclic creep, which arises from the repeated loading, to calculate the deformation of reinforced concrete beams. A comprehensive experimental program has been set up to identify the deformation accumulation of reinforced concrete beams under repeated loadings. The major test variables were the concrete compressive strength and the magnitude of fatigue loads. The model was calibrated from the present test results. The proposed model allows more realistic analysis of reinforced concrete beams under fatigue loads, especially deformation accumulation of such beams.

• Advances in nano research
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• v.12 no.1
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• pp.73-79
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• 2022

Due to the extensive use of concrete structures in various applications, the improvement of their strength and quality has become of great importance. A new way of achieving this purpose is to add different types of nanoparticles to concrete admixtures. In this work, a mathematical model has been employed to analyze the vibration of concrete beams reinforced by graphene oxide (GO) nanoparticles. To verify the accuracy of the presented model, an experimental study has been conducted to compare the compressive strengths of these beams. Since GO nanoparticles are not readily dissolved in water, before producing the concrete samples, the GO nanoparticles are dispersed in the mixture by using a shaker, magnetic striker, ultrasonic devices, and finally, by means of a mechanical mixer. The sinusoidal shear deformation beam theory (SSDBT) is employed to model the concrete beams. The Mori-Tanaka model is used to determine the effective properties of the structure, including the agglomeration influences. The motion equations are calculated by applying the energy method and Hamilton's principle. The vibration frequencies of the concrete beam samples are obtained by an analytical method. Three samples containing 0.02% GO nanoparticles are made and their compressive strengths are measured and compared. There is a good agreement between our results and those of the mathematical model and other papers, with a maximum difference of 1.29% between them. The aim of this work is to investigate the effects of nanoparticle volume fraction and agglomeration and the influences of beam length and thickness on the vibration frequency of concrete structures. The results show that by adding the GO nanoparticles, the vibration frequency of the beams is increased.

• Computers and Concrete
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• v.29 no.2
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• pp.81-92
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• 2022

The influence of normal stress perpendicular to the potential shear plane was always neglected in existing researches, which may lead to a serious deviation of the shear strength of concrete members in practice designs and numerical analyses. In this study, a series of experimental studies are carried out in this paper, which serves to investigate the shear behavior of concrete under compression shear loading. Based on the test results, a three-phase shear failure model for cohesive elements are developed, which is able to take into consideration the influence of normal stress on the shear strength of concrete. To identify the accuracy and applicability of the proposed model, numerical models of a double-noted concrete plate are developed and compared with experimental results. Results show that the proposed constitutive model is able to take into consideration the influence of normal stress on the shear strength of concrete materials, and is effective and accurate for describing the complex fracture of concrete, especially the failure modes under compression shear loadings.

• Computers and Concrete
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• v.33 no.1
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• pp.77-90
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• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.

• Computers and Concrete
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• v.19 no.5
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• pp.477-488
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• 2017

In this paper, an innovative technique for finite element (FE) modeling of steel tube-confined concrete (STCC) columns with active confinement under axial compressive loading is presented. In this method, a new constitutive model for the stress-strain relationship of actively-confined concrete is proposed. In total, 14 series of experimental STCC stub columns having active confinement were modeled using the ABAQUS software. The results obtained from the 3D model including the compressive strength at the initial peak point and failure point, as well as the axial and lateral stress-strain curves were compared with the experimental results to verify the accuracy of the 3D model. It was found that there existed a good agreement between them. A parametric study was conducted to investigate the effect of the concrete compressive strength, steel tube wall thickness, and pre-stressing level on the behavior of STCC columns with active confinement. The results indicated that increasing the concrete core's compressive strength leads to an increase in the compressive strength of the active composite column as well as its earlier failure. Furthermore, a reduction in the tube external diameter-to-wall thickness ratio affects the axial stress-strain curve and the confining pressure, while increasing the pre-stressing level has a negligible effect on the two.

• Steel and Composite Structures
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• v.37 no.1
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• pp.75-90
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• 2020

This paper introduces a new composite joint, which is the composite CFST beam- concrete column joint, and it is more convenient for transportation and erection than conventionally welded joints. The main components of this joint include steel H-beams welded with CFST beams, reinforced concrete columns, and reinforced concrete slabs. The steel H-beams and CFST beams are connected with a concrete slab using shear connectors to ensure composite action between them. An experimental investigation was conducted to evaluate the proposed composite joint performance. A three-dimensional (3D) finite element (FE) model was developed and analyzed for this joint using the ABAQUS/explicit. The FE model accuracy was validated by comparing its results with the relevant test results. Additionally, the parameters that consisted of the steel box beam thickness, concrete compressive strength, steel yield strength, and reinforcement ratio in the concrete slab were considered to investigate their influence on the proposed joint performance.