• Computers and Concrete
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• v.32 no.2
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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.

• Journal of the Korea Concrete Institute
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• v.23 no.1
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• pp.67-75
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• 2011

The effect of steel-fiber contents on the compressive behavior of ultra high performance cementitious composites (UHPCC) was studied to propose a compressive behavior model for UHPCC. The experiments considered fiber contents of 0~5 vol.% and the results indicated that compressive strength and corresponding strain as well as elastic modulus were improved as the fiber contents increased. Compared to the previous study results obtained from concrete with compressive strength of 100MPa or less, the reinforcement effect on strength showed similar tendency, while the effect on the strain and elastic modulus were much less. Strength, strain, and elastic modulus according to the fiber contents were presented as a linear function of fiber reinforcement index (RI). Fiber reinforcement in UHPCC had no influence on the shape of compressive behavioral curve. Considering its effect on compressive strength, strain, and elastic modulus, a compressive stress-strain relation for UHPCC was proposed.

• Computers and Concrete
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• v.10 no.4
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• pp.409-417
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• 2012

The quality of high performance concrete largely depends on water cement ratio, porosity, material composition and mix methods. The uniformity of color, texture and compressive strengths are quality indicators commonly used to assess the overall characteristics of concrete mixes. The homogeneity and share of coarse aggregates play a key role in concrete quality and must be analyzed in a microscopic point of view. This research studies the quality of high performance concrete by taking drilled cores in both horizontal and vertical directions from a 1.0 $m^3$ specimen. The coarse aggregate, expressed in digitized $100{\times}116$ dpi resolution images are processed based on brightness in colors through commercial software converted into text files. With the image converting to text format, the share of coarse aggregate is quantified leading to a satisfactory assessment of homogeneity - a quality index of high performance concrete. The compressive strengths of concrete and the shares of coarse aggregate of the samples are also compared in this research study to illustrate its correlation in concrete quality. It is concluded that a higher homogeneity of aggregate exists in the vertical plane than that of the horizontal planes of the high performance concrete. In addition, the concrete specimen showing denser particle packing has relatively higher compressive strengths. The research methodology provides an easy-to-use, direct measurement of high performance concrete when conducting quality assessment in the construction site.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.5
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• pp.501-509
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• 2010

In this paper, the interference avoidance performance of the RFID reader with the compressive receiver was analyzed. In the environment of the multiple dense readers operating, the recognition rates of the conventional RFID reader and the proposed RFID reader with the compressive receiver were measured and compared. The recognition rate of the proposed reader was improved more than 2.7 times compared to the conventional reader. Especially, in the environment of two proposed RFID readers operating simultaneously, little degradation of the recognition rate was observed.

• Structural Engineering and Mechanics
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• v.36 no.2
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• pp.225-241
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• 2010

In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.

• Computers and Concrete
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• v.5 no.5
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• pp.461-473
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• 2008

The paper explores the potential of Support Vector Machines (SVM) approach in predicting 28-day compressive strength and slump flow of self-compacting concrete. Total of 80 data collected from the exiting literature were used in present work. To compare the performance of the technique, prediction was also done using a back propagation neural network model. For this data-set, RBF kernel worked well in comparison to polynomial kernel based support vector machines and provide a root mean square error of 4.688 (MPa) (correlation coefficient=0.942) for 28-day compressive strength prediction and a root mean square error of 7.825 cm (correlation coefficient=0.931) for slump flow. Results obtained for RMSE and correlation coefficient suggested a comparable performance by Support Vector Machine approach to neural network approach for both 28-day compressive strength and slump flow prediction.

• Journal of the Korea Concrete Institute
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• v.12 no.5
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• pp.47-57
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• 2000

Experimental and numerical studies were done to investigate seismic performance of slender shear walls with no boundary confinement that are principal structural members of high0rise bearing wall buildings. 1/3 scale specimens that model the plastic region of long slender shear walls subjected to combined axial load and bending moment were tested to investigate strength, ductility, capacity of energy dissipation, and strain distribution, The experimental results show that the slender shear walls fail due to early crushing in the compressive boundary, and then have very low ductility. The measured maximum compressive strain is 0.0021, much less than 0.004 being commonly used for estimation of ductility. This result indicates that the maximum compressive strain is not a fixed value but is affected by moment gradient along the shear wall height and distance from the neutral axis to the extreme compressive fiber.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2016.05a
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• pp.107-108
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• 2016

When the construction in a low temperature, the concrete performance is deteriorated by frost damage at early age. In this study, the form utilizing heating sheet and insulation is measured the performance to prevent frost damage at 10 below zero. It produced five types of the form and to measure the temperature history and compressive strength. At first, form attached heating sheet showed the highest temperature. But the form attached vacuum insulation showed the highest temperature ever since 12hours. In the case of compressive strength, the form attached heating sheet + isopink(polystyrene foam board) showed the highest compressive strength. It was followed by vacuum insulation. As a result, the form utilizing insulation and heating sheet helped to prevent frost damage.

• Computers and Concrete
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• v.13 no.5
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• pp.621-632
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• 2014

This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the compressive strength of High Performance Concrete (HPC) containing binary and quaternary blends. The investigations were done on 23 HPC mixes, and specimens were cast and tested after 7, 28 and 56 days curing. The obtained experimental datas of 7, 28 and 56 days are trained using ANN which consists of eight input parameters like cement, metakaolin, blast furnace slag and fly ash, fine aggregate, coarse aggregate, superplasticizer and water binder ratio. The corresponding output parameters are 7, 28 and 56 days compressive strengths. The predicted values obtained using ANN show a good correlation between the Experimental data. The performance of the 8-9-3-3 architecture was better than other architectures. It concluded that ANN tool is convenient and time saving for predicting compressive strength at different ages.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.176-177
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• 2019

Recently, efforts are made to apply 200MPa levels of ultra-high strength concrete to structures exceeding 40MPa.. Ultra-high strength concrete has been steadily researched in Korea as well as abroad, and now it is equipped with 200MPa ultra-high strength concrete mixing technology. Because ultra-high strength concrete has a higher range of compressive strength than ordinary concrete, it is difficult to accurately measure the compressive strength of UHPC concrete with existing compressive strength measuring equipment and can be less reliable. In this study, the compressive strength of the SC80 was measured according to the test method to compare the compressive strength of the SC80 by applying various methods of measurement of compressive strength. The compressive strength test method measured the compressive strength according to the size of the specimen, the grinding method, and the capacity of the UTM equipment.