• International Journal of Concrete Structures and Materials
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• v.7 no.4
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• pp.295-301
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• 2013

This paper deals with the interfacial effects of silica fume (SF) and styrene-butadiene rubber (SBR) on compressive strength of concrete. Analyzing the compressive strength results of 32 concrete mixes performed over two water-binder ratios (0.35, 0.45), four percentages replacement of SF (0, 5, 7.5, and 10 %) and four percentages of SBR (0, 5, 10, and 15 %) were investigated. The results of the experiments were showed that in 5 % of SBR, compressive strength rises slightly, but when the polymer/binder materials ratio increases, compressive strength of concrete decreases. A mathematical model based on Abrams' law has been proposed for evaluation strength of SF-SBR concretes. The proposed model provides the opportunity to predict the compressive strength based on time of curing in water (t), and water, SF and SBR to binder materials ratios that they are shown with (w/b), (s) and (p).This understanding model might serve as useful guides for commixture concrete admixtures containing of SF and SBR. The accuracy of the proposed model is investigated. Good agreements between them are observed.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Journal of the Korean Ceramic Society
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• v.34 no.11
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• pp.1139-1150
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• 1997

A model for predicting the relative density and the compressive strength of open-cell ceramics with three-dimensional network structure was proposed through the interpretation of their macrostructure and fracture mechanics. The equation predicting the relative density was derived under the assumption that the open-cell structure was a periodic array of the tetrakaidecahedron unit cell consisting of cylindrical struts containing the internal hollow with the shape of a triangular prism. The model for compressive strength of open-cell ceramics with the hollow strut was also developed by modifying conventional model which based on fracture behavior of them subjected to the compressive stress. Both the relative density and the compressive strength were expressed in terms of the ratio of the strut diameter to the length together with the ratio of the hollow size to the strut diameter. The proposed model for the relative density and the compressive strength of the alumina-zirconia composite with open-cell structure were accorded well with the experimental values, whereas Gibson-Ashby and Zhang's model did not show such a good agreement.

• Computers and Concrete
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• v.13 no.4
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• pp.531-545
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• 2014

The purpose of this paper is to develop a prediction model for the compressive strength of waste LCD glass applied in concrete by analyzing a series of laboratory test results, which were obtained in our previous study. The hyperbolic function was used to perform the nonlinear-multivariate regression analysis of the compressive strength prediction model with the following parameters: water-binder ratio w/b, curing age t, and waste glass content G. According to the relative regression analysis, the compressive strength prediction model is developed. The calculated results are in accord with the laboratory measured data, which are the concrete compressive strengths of different mix proportions. In addition, a coefficient of determination $R^2$ value between 0.93 and 0.96 and a mean absolute percentage error MAPE between 5.4% and 8.4% were obtained by regression analysis using the predicted compressive analysis value, and the test results are also excellent. Therefore, the predicted results for compressive strength are highly accurate for waste LCD glass applied in concrete. Additionally, this predicted model exhibits a good predictive capacity when employed to calculate the compressive strength of washed glass sand concrete.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.189-194
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• 2001

In this study, ultimate strength of concrete deep beams with an opening is predicted by using Strut-and-Tie Model with a new effective compressive strength. First crack occurs around an opening by stress concentration due to geometric discontinuity. This results in decreasing ultimate strength of deep beams with an opening compared with general deep beams. With fundamental notion that ultimate strength of deep beam with an opening decreases as a result of reduction in effective compressive strength of a concrete strut, an equivalent effective compressive strength formula is proposed in order to reflect ultimate strength reduction due to an opening located in a concrete strut. An equivalent effective compressive strength formula which can reflect opening size and position is added to a testified algorithm of predicting ultimate strength of concrete deep beams. Therefore, ultimate strength of concrete deep beam with an opening is predicted by using a simple and rational STM algorithm including an equivalent effective compressive strength formula, not by finite element analysis or a former complex Strut-and-Tie Model

• Computers and Concrete
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• v.15 no.5
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• pp.759-770
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• 2015

In this study, a multi-layer perceptron neural network (MLPNN) prediction model for compressive strength of the cement mortars has been developed. For purpose of constructing this model, 8 different mixes with 240 specimens of the 2, 7, 28, 56 and 90 days compressive strength experimental results of cement mortars containing fly ash (FA), silica fume (SF) and FA+SF used in training and testing for MLPNN system was gathered from the standard cement tests. The data used in the MLPNN model are arranged in a format of four input parameters that cover the FA, SF, FA+SF and age of samples and an output parameter which is compressive strength of cement mortars. In the model, the training and testing results have shown that MLPNN system has strong potential as a feasible tool for predicting 2, 7, 28, 56 and 90 days compressive strength of cement mortars.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.90-91
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• 2020

Utilization of upgraded fine fly ash in cement-based materials has been proved by many researchers as an effective method to improve compressive strength of cement based materials at early ages. The addition of fine fly ash has introduced dilution effect, enhanced pozzolanic reaction effect, nucleation effect and physical filling effect into cement-fly ash system. In this study, an integrated reaction model is adpoted to quantify the contributions from cement hydration and pozzolanic reaction to compressive strength. A modified model related to the physical filling effect is utilized to calculate the compressive strength increment considering the gradual dissolution of fly ash particles. Via combination of these two parts, a numerical model has been proposed to predict the compressive strength development of fine fly ash mortar considering fly ash fineness. The reliability of the model is validated through good agreement with the experimental results from previous articles.

• Computers and Concrete
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• v.21 no.5
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• pp.505-511
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• 2018

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSO-LSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.05a
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• pp.947-952
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• 2001

The prediction model is proposed to estimate the variation of compressive strength of fly ash concrete with aging. After analyzing the experimental result with the model, the regression results are presented according to fly ash replacement content and water/cement ratio. Based on the regression results, the influence of fly ash replacement content and water/cement ratio on apparent activation energy was investigated. According to the analysis, the model provides a good estimate of compressive strength development of fly ash concrete with aging. As the fly ash replacement content increases, the limiting relative compressive strength and initial apparent activation energy become greater. The concrete with water/cement ratio smaller than 0.40 shows that the limiting relative compressive strength and apparent activation energy are nearly constant according to water/cement ratio. But, the concrete with water/cement ratio greater than 0.40 has the increasing limiting relative compressive strength and apparent activation energy with increasing water/cement ratio.