• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2005년도 추계 학술논문 발표대회
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• pp.75-78
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• 2005

This raper investigates the effect of curing temperature on strength development of concrete incorporating cement kiln dust(CKD) and blast furnace slag (BS) quantitatively. Estimation of compressive strength of concrete was conducted using equivalent age equation and rate constant model. An increasing curing temperature results in an increase in strength at early age, but with the elapse of age, strength development at high curing temperature decreases compared with that at low curing temperature. Especially, the use of 35 has a remarkable strength development at early age and even at later age, high strength is maintained due to accelerated pozzolanic activity resulting from high temperature. Whereas, at low curing temperature, the use of BS leads to a decrease in compressive strength. Accordingly, much attention should be paid to prevent strength loss at low temperature. Based on the strength development estimation using equivalent age equation, good agreements between measured strength and calculated strength are obtained.

• 한국농공학회지
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• 제42권2호
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• pp.99-107
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• 2000

The objective of this study are to compare the development of compressive strength of epoxy mortars used as repairing materials with respect to maturity , and to propose a model predicting strength development of epoxy mortars. A series of tests are carried out for the hardener contents of 30, 40 and 50 percentage of epoxy resin and compressive strengths are measured at the age of 6, 12, 24, 72, 120 and 168 hours respectively under the cure temperature of 0, 10, 20 and 3$0^{\circ}C$. The datum temperature is estimated by measured strengths, and the maturity is calculated with the estimated datum temperature. The compressive strength of epoxy mortars can be predicted by regression analysis from the maturity-compressive strength relationship.

• Computers and Concrete
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• 제27권5호
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• pp.489-512
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• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

• 한국해양공학회지
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• 제26권2호
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• pp.39-47
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• 2012

The objective of this study was to develop a strength prediction model using a polynomial regression analysis based on the experimental results obtained from ninety samples. As the results of a correlation analysis between various mixing factors and unconfined compressive strength using SPSS (statistical package for the social sciences), the governing factors in the strength of lightweight soil were found to be the crumb rubber content, bottom ash content,and water-cement ratio. After selecting the governing factors affecting the strength through the correlation analysis, a strength prediction model, which consisted of the selected governing factors, was developed using the polynomial regression analysis. The strengths calculated from the proposed model were similar to those resulting from laboratory tests (R2=87.5%). Therefore, the proposed model can be used to predict the strength of lightweight mixtures with various mixing ratios without time-consuming experimental tests.

• Computers and Concrete
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• 제32권2호
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• Advanced Composite Materials
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• 제18권3호
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• pp.251-264
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• 2009

Nonlinear mechanical behaviors of unidirectional carbon fiber-reinforced plastic (CFRP) laminates using cup-stacked carbon nanotubes (CSCNTs) dispersed epoxy are evaluated and compared with those of CFRP laminates without CSCNTs. Off-axis compression tests are performed to obtain the stress-strain relations. One-parameter plasticity model is applied to characterize the nonlinear response of unidirectional laminates, and nonlinear behaviors of laminates with and without CSCNTs are compared. Clear improvement in stiffness of off-axis specimens by using CSCNTs is demonstrated, which is considered to contribute the enhancement of the longitudinal compressive strength of unidirectional laminates and compressive strength of multidirectional laminates. Finally, longitudinal compressive strengths are predicted based on a kink band model including the nonlinear responses in order to demonstrate the improvement in longitudinal strength of CFRP by dispersing CSCNTs.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 1999년도 학회창립 10주년 기념 1999년도 가을 학술발표회 논문집
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• pp.155-158
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• 1999

This paper discusses the validity of models to predict the compressive strength of concrete subjected to various temperature histories and the shortcomings of existing rate constant model and apparent activation energy concept. Based on the discussion, a modified rate constant model is proposed. The modified rate constant model, in which apparent activation energy is a nonlinear function of curing temperature and age, accurately estimates the development of the experimental compressive strengths by a few researches. Also, the apparent activation energy of concrete cured with high temperature decreases rapidly with age, but that cured with low temperature decreases gradually with age. Finally a generalized model to predict apparent activation energy and compressive strength is proposed, which is based on the regression results.

• International Journal of Concrete Structures and Materials
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• 제10권3호
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• pp.257-269
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• 2016

Early-strength-concrete (ESC) made of Type I cement with a high Blaine value of $500m^2/kg$ reaches approximately 60 % of its compressive strength in 1 day at ambient temperature. Based on the 210 compressive test results, a generalized rateconstant material model was presented to predict the development of compressive strengths of ESC at different equivalent ages (9, 12, 18, 24, 36, 100 and 168 h) and maximum temperatures (20, 30, 40, 50 and $60^{\circ}C$) for design compressive strengths of 30, 40 and 50 MPa. The developed material model was used to find optimum curing regimes for precast prestressed members with ESC. The results indicated that depending on design compressive strength, conservatively 25-40 % savings could be realized for a total curing duration of 18 h with the maximum temperature of $60^{\circ}C$, compared with those observed in a typical curing regime for concrete with Type I cement.

• 한국농공학회논문집
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• 제48권3호
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• pp.85-94
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• 2006

Practially useful method of steel fiber for construction work is presented in this study. The most important purpose of this study is to develop a model which can predict mechanical behavior of the structure according to aspect ratio and volume fraction of steel fiber. Experiments on compressive strength, elastic modulus, and splitting strength were performed with self-made cylindrical specimens of variable aspect ratios and volume fractions. The experiment showed that compressive strength was not in direct proportion to volume fraction which doesn't seem to have great influence over compressive strength. However, splitting strength showed almost direct proportion to aspect ratio and volume fraction. Improvement of optimal efficiency was confirmed when the aspect ratio was 70. Experiments on flexural strength, fracture energy, and characteristic length were carried out with self-manufactured beams with notch. As a result, increases of flexural strength, fracture energy, and characteristic length according to increase of volume fraction tend to be prominent when aspect ratio is 70. The steel fiber improves concrete to be more ductile and tough. Moreover, regression analysis was the performed and predictable model was developed after determining variables. With comparison and analysis of suggested estimated values and measured data, reliance of the model was verified.

• Advances in Computational Design
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• 제2권3호
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• pp.225-240
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• 2017

This paper presents the application of multiple linear regression (MLR) and artificial neural network (ANN) techniques for developing the models to predict the unconfined compressive strength (UCS) and Brazilian tensile strength (BTS) of the fiber reinforced cement stabilized fly ash mixes. UCS and BTS is a highly nonlinear function of its constituents, thereby, making its modeling and prediction a difficult task. To establish relationship between the independent and dependent variables, a computational technique like ANN is employed which provides an efficient and easy approach to model the complex and nonlinear relationship. The data generated in the laboratory through systematic experimental programme for evaluating UCS and BTS of fiber reinforced cement fly ash mixes with respect to 7, 14 and 28 days' curing is used for development of the MLR and ANN model. The data used in the models is arranged in the format of four input parameters that cover the contents of cement and fibers along with maximum dry density (MDD) and optimum moisture contents (OMC), respectively and one dependent variable as unconfined compressive as well as Brazilian tensile strength. ANN models are trained and tested for various combinations of input and output data sets. Performance of networks is checked with the statistical error criteria of correlation coefficient (R), mean square error (MSE) and mean absolute error (MAE). It is observed that the ANN model predicts both, the unconfined compressive and Brazilian tensile, strength quite well in the form of R, RMSE and MAE. This study shows that as an alternative to classical modeling techniques, ANN approach can be used accurately for predicting the unconfined compressive strength and Brazilian tensile strength of fiber reinforced cement stabilized fly ash mixes.