• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.133-136
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• 2005

Compressive strength of recycled aggregate concrete was tested by the core and by the non-destructive testing. A prediction model of compressive strength considering the replacement level of recycled aggregate was suggested by multi-regression analysis and was compared with test results. Also, Test results showed that the ratio of compressive strength by core and non-destructive testing to actual was somewhat affected by the replacement level of recycled aggregate.

• Computers and Concrete
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• v.10 no.2
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• pp.197-217
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• 2012

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Steel and Composite Structures
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• v.49 no.3
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• pp.337-348
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• 2023

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

• Structural Engineering and Mechanics
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• v.31 no.6
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• pp.697-716
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• 2009

This paper summarizes an experimental and analytical study on the seismic behavior of high strength reinforced concrete columns under cyclic loading. In total six cantilever columns with different sizes and concrete compressive strengths were tested. Three columns, small size, had a $325{\times}325$ mm cross section and the three other columns, medium size, were $520{\times}520$ mm. Concrete compressive strength was 80, 130 and 180 MPa. All specimens were designed in accordance with the Japanese design guidelines. The tests demonstrated that, for specimens made of 180 MPa concrete compressive strength, spalling of cover concrete was very brittle followed by a significant decrease in strength. Curvature was much important for the small size than for the medium size columns. Concrete compressive strength had no effect on the curvature distribution for a drift varying between -2% and +2%. However, it had an effect on the drift corresponding to the peak moment and on the equivalent viscous damping variation. Simple equations are proposed for 1) evaluating the concrete Young's modulus for high strength concrete and for 2) evaluating the moment-drift envelope curves for the medium size columns knowing that of the small size columns. Experimental moment-drift and axial strain-drift histories were well predicted using a fiber model developed by the authors.

• Magazine of the Korea Concrete Institute
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• v.8 no.2
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• pp.139-150
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• 1996

The objective of this study is to provide the information on the manufacturing and exper- , ructures. imental techniques of small scale modeling of precast concrete(P.C.) large panel :-t The ad~~pted scale was one-fifth. 4 types of experiments were performed : nlaterial tests for model concrete and model reinforcement, compressive test of horizontal joint, shear test of vertical joint and cyclic static test of 2-story subassemblage structure. Based on the experimental results, the following conclusions are drawn : i 1) Model concrete had in general larger compressive strength than expected. (2) Model reinforcement showed less ductility if the annealing processes were performed without using vaccuum tube. 131 Failure niotles of horizontal and vertical joints were almost same for both prototype and model. But the strength of model appears to be higher than required by similitude law. (41 Hysteretic behavior of 1 /T, scale subassemblage model can be made quite similar to that of prototype if the ductility of model reinforcement and compressive strength of model concrete could be representative of those of prototype.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4A
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• pp.267-279
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• 2009

A numerical model that can simulate the nonlinear behavior of ultra high strength fiber reinforced concrete (UHSFRC) structures subjected to monotonic loading is introduced. The material properties of UHSFRC, such as compressive and tensile strength or elastic modulus, are different from normal strength reinforced concrete. The uniaxial compressive stress-strain relationship of UHSFRC is designed on the basis of experimental result, and the equivalent uniaxial stress-strain relationship is introduced for proper estimation of UHSFRC structures. The steel is uniformly distributed over the concrete matrix with particular orientation angle. In advance, this paper introduces a numerical model that can simulate the tension-stiffening behavior of tension part of the axial member on the basis of the bond-slip relationship. The reaction of steel fiber is considered for the numerical model after cracks of the concrete matrix with steel fibers are formed. Finally, the introduced numerical model is validated by comparison with test results for idealized UHSFRC beams.

• Advances in concrete construction
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• v.10 no.6
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• pp.479-488
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• 2020

The experimental and numerical works were carried out on high performance fiber reinforced concrete (HPFRC) with w/cm ratios ranging from 0.25 to 0.40, fiber volume fraction (Vf)=0-1.5% and 10% silica fume replacement. Improvements in compressive and flexural strengths obtained for HPFRC are moderate and significant, respectively, Empirical equations developed for the compressive strength and flexural strength of HPFRC as a function of fiber volume fraction. A relation between flexural strength and compressive strength of HPFRC with R=0.78 was developed. Due to the complex mix proportions and non-linear relationship between the mix proportions and properties, models with reliable predictive capabilities are not developed and also research on HPFRC was empirical. In this paper due to the inadequacy of present method, a back propagation-neural network (BP-NN) was employed to estimate the 28-day compressive strength of HPFRC mixes. BP-NN model was built to implement the highly non-linear relationship between the mix proportions and their properties. This paper describes the data sets collected, training of ANNs and comparison of the experimental results obtained for various mixtures. On statistical analyses of collected data, a multiple linear regression (MLR) model with R2=0.78 was developed for the prediction of compressive strength of HPFRC mixes, and average absolute error (AAE) obtained is 6.5%. On validation of the data sets by NNs, the error range was within 2% of the actual values. ANN model has given the significant degree of accuracy and reliability compared to the MLR model. ANN approach can be effectively used to estimate the 28-day compressive strength of fibrous concrete mixes and is practical.

• Journal of the Korean Society for Advanced Composite Structures
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• v.4 no.3
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• pp.13-21
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• 2013

In this study, uniaxial compression tests were performed to investigates the stress-strain relations of Double Skinned Composite Tubular Columns reinforced with steel tube. The confined concrete has been known as the strength of concrete increases significantly. Specimens reinforced with outer and inner steel tube were tested by uniaxial compression test. To investigate the influence of concrete strength increase by confining conditions in steel tubes, 8 specimens with different thickness of tube, hollowness ratio and concrete strength were tested and compared with other researcher's concrete material model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.6
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• pp.82-89
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• 2014

The purpose of this study is to propose a simple model to evaluate the compressive strength development of concrete with various supplementary cementitious materials (SCMs) and cured under different temperatures. For the generalization of the model, the ACI 209 parabola equation was modified based on the maturity function and then experimental constants A and B and 28-day compressive strength were determined from the regression analysis using a total of 265 data-sets compiled from the available literature. To verify the proposed model, concrete specimens classified into 3 Groups were prepared according to the SCM level as a partial replacement of cement and curing temperature. The analysis of existing data clearly revealed that the 28-day compressive strength decreases when the curing temperature is higher and/or lower than the reference curing temperature ($20^{\circ}C$). Furthermore, test results showed that the compressive strength development of concrete cured under $20^{\circ}C$ until an early age of 3 days was marginally affected by the curing temperature afterward. The proposed model accurately predicts the compressive strength development of concrete tested, indicating that the mean and standard deviation of the ratios between predictions and experiments are 1.00 and 0.08, respectively.