• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.577-578
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• 2009

For cases where the column concrete strength exceeds 1.4 times the slab concrete strength, the KCI Code requires that either: puddled high-strength concrete(HSC) be used in the slab, or the use of vertical dowels and spirals through the joint, or the use of an effective concrete strength in the joint. This paper studies on the third strategy. A prediction model of the effective concrete strength for interior columns was proposed using an analogy of brick and mortar in brick masonry. The proposed prediction model is verified by comparison with experimental results and various design equations.

• Journal of Industrial Technology
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• v.41 no.1
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• pp.1-6
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• 2021

Fly ash and silica fume belong to industry by-products that can be used to produce concrete. This study shows the model of a neural network to evaluate the strength development of blended concrete containing fly ash and silica fume. The neural network model has four input parameters, such as fly ash replacement content, silica fume replacement content, water/binder ratio, and ages. Strength is the output variable of neural network. Based on the backpropagation algorithm, the values of elements in the hidden layer of neural network are determined. The number of neurons in the hidden layer is confirmed based on trial calculations. We find (1) neural network can give a reasonable evaluation of the strength development of composite concrete. Neural network can reflect the improvement of strength due to silica fume additions and can consider the reductions of strength as water/binder increases. (2) When the number of neurons in the hidden layer is five, the prediction results show more accuracy than four neurons in the hidden layer. Moreover, five neurons in the hidden layer can reproduce the strength crossover between fly ash concrete and plain concrete. Summarily, the neural network-based model is valuable for design sustainable composite concrete containing silica fume and fly ash.

• Structural Engineering and Mechanics
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• v.24 no.2
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• pp.137-154
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• 2006

The reliable pushover analysis of RC structures requires a realistic prediction of moment-curvature relations, which can be obtained by utilizing proper constitutive models for the stress-strain relationships of laterally confined concrete members. Theoretical approach of Mander is still a single stress-strain model, which employs a multiaxial failure surface for the determination of the ultimate strength of confined concrete. Alternatively, this paper introduces a simple and practical failure criterion for confined concrete with emphasis on introduction of significant modifications into the two-parameter Drucker-Prager model. The new criterion is only applicable to triaxial compression stress state which is exactly the case in the RC columns. Unlike many existing multi-parameter criteria proposed for the concrete fracture, the model needs only the compressive strength of concrete as an independent parameter and also implies for the influence of the Lode angle on the material strength. Adopting Saenz equation for stress-strain plots, satisfactory agreement between the measured and predicted results for the available experimental test data of confined normal and high strength concrete specimens is obtained. Moreover, it is found that further work involving the confinement pressure is still encouraging since the confinement model of Mander overestimates the ultimate strength of some RC columns.

• Earthquakes and Structures
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• v.8 no.5
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• pp.1171-1190
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• 2015

Enhancement of strength and ductility is the main reason for the extensive use of FRP jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP-confined concrete for practical design purposes. This study presents a new approach to obtain strength enhancement of CFRP (carbon fiber reinforced polymer) confined concrete cylinders by applying artificial neural networks (ANNs). The proposed ANN model is based on experimental results collected from literature. It represents the ultimate strength of concrete cylinders after CFRP confinement which is also given in explicit form in terms of geometrical and mechanical parameters. The accuracy of the proposed ANN model is quite satisfactory when compared to experimental results. Moreover, the results of the proposed ANN model are compared with five important theoretical models proposed by researchers so far and considered to be in good agreement.

• Computers and Concrete
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• v.19 no.5
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• pp.467-475
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• 2017

The purpose of this study is to establish a prediction model for the electrical resistivity ($E_r$) of self-consolidating concrete by using waste LCD (liquid crystal display) glass as part of the fine aggregate and then, to analyze the results obtained from a series of laboratory tests. A hyperbolic function is used to perform nonlinear multivariate regression analysis of the electrical resistivity prediction model, with parameters such as water-binder ratio (w/b), curing age (t) and waste glass content (G). Furthermore, the relationship of compressive strength and electrical resistivity of waste LCD glass concrete is also found by a logarithm function, while compressive strength is evaluated by the electrical resistivity of non-destructive testing (NDT). According to relative regression analysis, the electrical resistivity and compressive strength prediction models are developed, and the results show that a good agreement is obtained using the proposed prediction models. From the comparison between the predicted analysis values and test results, the MAPE value of electrical resistivity is 17.0-18.2% and less than 20%, the MAPE value of compressive strength evaluated by $E_r$ is 5.9-10.6% and nearly less than 10%. Therefore, the prediction models established in this study have good predictive ability for electrical resistivity and compressive strength of waste LCD glass concrete. However, further study is needed in regard to applying the proposed prediction models to other ranges of mixture parameters.

• Structural Engineering and Mechanics
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• v.46 no.1
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• pp.111-135
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• 2013

An experimental study has been carried out on square plain concrete (PC) and reinforced concrete (RC) columns strengthened with carbon fiber-reinforced polymer (CFRP) sheets. A total of 78 specimens were loaded to failure in axial compression and investigated in both axial and transverse directions. Slenderness of the columns, number of wrap layers and concrete strength were the test parameters. Compressive stress, axial and hoop strains were recorded to evaluate the stress-strain relationship, ultimate strength and ductility of the specimens. Results clearly demonstrate that composite wrapping can enhance the structural performance of square columns in terms of both maximum strength and ductility. On the basis of the effective lateral confining pressure of composite jacket and the effective FRP strain coefficient, new peak stress equations were proposed to predict the axial strength and corresponding strain of FRP-confined square concrete columns. This model incorporates the effect of the effective circumferential FRP failure strain and the effect of the effective lateral confining pressure. The results show that the predictions of the model agree well with the test data.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10a
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• pp.430-437
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• 1998

The existing design expressions for shear strength of reinforced concrete columns are lacking in consistent seismic design philosophy and very conservative. However, relatively not so many experiments have been conducted to verify the shear resisting mechanisms of columns. The previous researches concentrated on deriving an experimental model from their test results. So, there is a need to approach this problem from the analytical point of view to be balanced with the experimental effort. This paper presents a method of modeling reinforced concrete columns under seismic shear loading. Lower bound solutions are obtained by using an analogous truss model and concrete arch actions. This model agrees with the precedented test results by some margins.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.1-4
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• 2005

This research is to present experimental materials model of high strength concrete for prediction of fire safety of structural members based on physical properties of materials during heating up to 800$^{circ}C$. The following conclusions are drawn from this study. First of all, between 100 to 200 $^{circ}C$, the physical models of concrete such as specific heat and thermal conductivity, show visible degradation, regardless of concrete strength. Second, between 300 to 600$^{circ}C$, the physical models of the 29MPa and 49MPa concrete show degradation continually at these temperatures. Finally, beyond 600$^{circ}C$, the physical models of 49MPa strength concrete show larger degradation than 29MPa strength concrete due to rise of pore pressure and melting of the interface between aggregate and cement paste.

• 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.

• Computers and Concrete
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• v.26 no.4
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• pp.309-316
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• 2020

The application of multi-variable adaptive regression spline (MARS) in predicting he long-term compressive strength of a concrete with various admixtures has been investigated in this study. The compressive strength of concrete specimens, which were made based on 24 different mix designs using various mineral and chemical admixtures in different curing ages have been obtained. First, The values of fly ash (FA), micro-silica (MS), water-reducing admixture (WRA), coarse and fine aggregates, cement, water, age of samples and compressive strength were defined as inputs to the model, and MARS analysis was used to model the compressive strength of concrete and to evaluate the most important parameters affecting the estimation of compressive strength of the concrete. Next, the proposed equation by the MARS method using particle swarm optimization (PSO) algorithm has been optimized to have more efficient equation from the economical point of view. The proposed model in this study predicted the compressive strength of the concrete with various admixtures with a correlation coefficient of R=0.958 rather than the measured compressive strengths within the laboratory. The final model reduced the production cost and provided compressive strength by reducing the WRA and increasing the FA and curing days, simultaneously. It was also found that due to the use of the liquid membrane-forming compounds (LMFC) for its lower cost than water spraying method (SWM) and also for the longer operating time of the LMFC having positive mechanical effects on the final concrete, the final product had lower cost and better mechanical properties.