• Advances in concrete construction
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• v.14 no.2
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• pp.115-130
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• 2022

This paper presents an application of the maturation effect on the strength of high-strength concrete which is produced with different origin aggregates. While investigating the maturation effect on HSC 384 specimens were prepared with 22 different origin aggregates. These prepared specimens were subjected to the standard compressive tests which were applied after curing for 2, 7, 28, and 56 days under appropriate conditions. The test results revealed that bright surface-low adherence behavior is valid in normal strength concretes, but is not as effective as expected in high-strength concretes. The application of artificial neural networks (ANNs) to predict 2, 7, 28, and 56 day compressive strength of HSC is also investigated in this paper. An ANN model is built, trained, and tested using the available test data gathered from experimental studies. The ANN model is found to predict 2, 7, 28, and 56 days of compressive strength of high-strength concrete well within the ranges of the input parameters considered. These comparisons show that ANNs have strong potential as a feasible tool for predicting the compressive strength of high-strength concrete within the range of the input parameters considered.

• Structural Engineering and Mechanics
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• v.18 no.5
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• pp.541-563
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• 2004

For reaching large inelastic deformations without a substantial loss in strength, the potential plastic hinge regions of the reinforced concrete structural members should be confined by adequate transverse reinforcement. Therefore, simple and realistic representation of confined concrete behaviour is needed for inelastic analysis of reinforced concrete structures. In this study, a trilinear stress-strain model is proposed for the axial behaviour of confined concrete. The model is based on experimental work that was carried out on nearly full size specimens. During the interpretation of experimental data, the buckling and strain hardening of the longitudinal reinforcement are also taken into account. The proposed model is used for predicting the stress-strain relationships of confined concrete specimens tested by other researchers. Although the proposed model is simpler than most of the available models, the comparisons between the predicted results and experimental data indicate that it can represent the stress-strain relationship of confined concrete quite realistically.

• Computers and Concrete
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• v.8 no.5
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• pp.597-616
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• 2011

With respect to rehabilitation, strengthening and retrofitting of existing and deteriorated columns in buildings and bridges, CFRP sheets have been found effective in enhancing the performance of existing RC columns by wrapping and bonding CFRP sheets externally around the concrete. Concrete columns and piers that are confined by both lateral steel reinforcement and CFRP are sometimes referred to as "hybrid" concrete columns. With the availability of experimental data on concrete columns confined by steel reinforcement and/or CFRP, the study presents modeling using artificial neural networks (ANNs) to predict the compressive strength of hybrid circular RC columns. The prediction of the ultimate confined compressive strength of RC columns is very important especially when this value is used in estimating the capacity of structures. The present ANN model used as parameters for the confining materials the lateral steel ratio (${\rho}_s$) and the FRP volumetric ratio (${\rho}_{FRP}$). The model gave good predictions for three types of confined columns: (a) columns confined with steel reinforcement only, (b) CFRP confined columns, and (c) hybrid columns confined by both steel and CFRP. The model may be used for predicting the compressive strength of existing circular RC columns confined with steel only that will be strengthened or retrofitted using CFRP.

• Steel and Composite Structures
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• v.44 no.3
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• pp.389-406
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• 2022

Recycling concrete construction waste is an encouraging step toward green and sustainable building. A lot of research has been done on recycled aggregate concretes (RACs), but not nearly as much has been done on concrete made with recycled aggregate. Recycled aggregate concrete, on the other hand, has been found to have a lower mechanical productivity compared to conventional one. Accurately estimating the mechanical behavior of the concrete samples is a most important scientific topic in civil, structural, and construction engineering. This may prevent the need for excess time and effort and lead to economic considerations because experimental studies are often time-consuming, costly, and troublous. This study presents a comprehensive data-mining-based model for predicting the splitting tensile strength of recycled aggregate concrete modified with glass fiber and silica fume. For this purpose, first, 168 splitting tensile strength tests under different conditions have been performed in the laboratory, then based on the different conditions of each experiment, some variables are considered as input parameters to predict the splitting tensile strength. Then, three hybrid models as GWO-RF, GWO-MLP, and GWO-SVR, were utilized for this purpose. The results showed that all developed GWO-based hybrid predicting models have good agreement with measured experimental results. Significantly, the GWO-RF model has the best accuracy based on the model performance assessment criteria for training and testing data.

• Magazine of the Korea Concrete Institute
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• v.11 no.2
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• pp.95-103
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• 1999

The reduction phenomena of concrete compressive strength with the size of specimens have been extensively investigated, but till now the adequate analysis technique is not fixed. The existing research results show that the bigger the member size, the smaller the strength. This means the nonlinear fracture mechanics theory is needed in order to analyze the fracture behaviors of concrete and the size effect. There is a few model equations that is to predict the size effect of compressive strength of standard and non-standard cylindrical specimen. However, theses equations did not considered the difference of fracturing mechanism which depends on the strength level. In this paper, model equations to predict compressive strength of concrete considering the size effect and strength level are suggested. The size effect model suggested in this paper shows good prediction compared with the existing test data of various concrete size and strength level.

• Proceedings of the Korea Concrete Institute Conference
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• 1994.10a
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• pp.175-179
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• 1994

This study is for estimating strength of mass concrete by finding out temperature and strength distribution of member inside through manufacture actual model in summer and winter.

• Computers and Concrete
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• v.27 no.1
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• pp.21-28
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• 2021

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

• 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.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.185-188
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• 2005

Bond between reinforcing bar and surrounding concrete is supposed to transfer load safely in the process of design of reinforced concrete structures. Bond strength of ribbed reinforcing bars tends to split concrete cover, by wedging action, or shear the concrete in front of the ribs. In this study, using a reducing bearing angle theory, bond strengths of beam end specimen are predicted. Values of bond strength obtained using the analytical model are in good agreement with the bond test results. The analytical model provides insight into bond mechanism and the effects of bearing angle on the bond strength of deformed bars to concrete.

• Computers and Concrete
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• v.31 no.2
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• pp.111-121
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• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.