• Journal of the Korea Concrete Institute
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• v.14 no.4
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• pp.457-466
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• 2002

An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.307-315
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• 2001

The purpose of this study was to provide the evaluation and prediction of strengths of SB latex modified concrete(LMC) using a statistical method and factorial experimental design method. The main experimental variables were as follows ; W/C ( 4 levels ; 31, 33, 35, 42%), S/a( 2 levels ; 55, 58%) and L/C(2 levels ; 5, 15%). The compressive strength and flexural strength of LMC were selected as a factor of response. The statistical method was carried out to analyze the results, together with factorial experimental design method and response surface method. The analysis showed that if L/C had been 15%, W/C appeared to be around 33% to achieve the design strength of $350kgf/cm^2$. In this case, the flexural strength and the slump came to around $68kgf/cm^2$ and 18cm, respectively. Eventhough the L/C varied, the design strength and W/C could be predictable together with slump value and flexural strength. As a result of series of experiments in this study, W/C and L/C were proved to be the main factors influencing on the compressive and flexural strength of LMC. Both of strength and slump values could be predictable from the mixing proportion of LMC.

• Computers and Concrete
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• v.16 no.3
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• pp.343-356
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• 2015

1078 sets of mixtures in total that include fly ash, slag, and/or silica fume have been collected for prediction on concrete properties. A new database platform (Compos) has been developed, by which the stepwise multiple linear regression (SMLR) and BP artificial neural networks (BP ANNs) programs have been applied respectively to identify correlations between the concrete properties (strength, workability, and durability) and the dosage and/or quality of raw materials'. The results showed obvious nonlinear relations so that forecasting by using nonlinear method has clearly higher accuracy than using linear method. The forecasting accuracy rises along with the increasing of age and the prediction on cubic compressive strength have the best results, because the minimum average relative error (MARE) for 60-day cubic compressive strength was less than 8%. The precision for forecasting of concrete workability takes the second place in which the MARE is less than 15%. Forecasting on concrete durability has the lowest accuracy as its MARE has even reached 30%. These conclusions have been certified in a ready-mixed concrete plant that the synthesized MARE of 7-day/28-day strength and initial slump is less than 8%. The parameters of BP ANNs and its conformation have been discussed as well in this study.

• Computers and Concrete
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• v.10 no.6
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• pp.557-573
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• 2012

This paper discusses the combined application of two different techniques, Neural Networks (NN) and Principal Component Analysis (PCA), for improved prediction of concrete properties. The combination of these approaches allowed the development of six neural networks models for predicting slump and compressive strength of concrete with mineral additives such as blast furnace slag, fly ash and silica fume. The Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian regularization was used in all these models. They are produced to implement the complex nonlinear relationship between the inputs and the output of the network. They are also established through the incorporation of a huge experimental database on concrete organized in the form Mix-Property. Thus, the data comprising the concrete mixtures are much correlated to each others. The PCA is proposed for the compression and the elimination of the correlation between these data. After applying the PCA, the uncorrelated data were used to train the six models. The predictive results of these models were compared with the actual experimental trials. The results showed that the elimination of the correlation between the input parameters using PCA improved the predictive generalisation performance models with smaller architectures and dimensionality reduction. This study showed also that using the developed models for numerical investigations on the parameters affecting the properties of concrete is promising.

• Journal of the Korean Society of Hazard Mitigation
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• v.3 no.4 s.11
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• pp.119-129
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• 2003

Concrete and steel are essential structural materials in the construction. But, concrete, different from steel, consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructors. Concrete have two kinds of properties, immediately knowing properties such as slump, air contents and time dependent one like strength. Therefore, concrete mixes depend on experiences of experts. However, at point of time using High Performance Concrete, new method is wanted because of more ingredients like mineral and chemical admixtures and lack of data. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network ate used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength, slump, and air contents are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• Journal of the Korea Concrete Institute
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• v.26 no.6
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• pp.707-714
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• 2014

CFT (Concrete-Filled Tube) is a type of steel column comprised of steel tube and concrete. Steel tube holds concrete and the concrete inside tube takes charge of compressive load. This study presents structural performance of the CFT column which has 73~100 MPa high strength concrete inside. Fluidity, mechanical compression, pump pressure test in flexible pipe were conducted for understanding properties of the high strength concrete. Material properties were achieved by various experimental tests, such as slump, slump flow, air content, U-box, O-Lot, L-flow. In addition, mock-up tests were conducted to monitor concrete filling, hydration heat, compressive strength. From construction sites in Sang-am dong and University of Seo-kang, long-term behaviors could be effectively predicted in terms of ACI 209 material model considering elastic deformation, shrinkage and creep.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.10a
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• pp.769-774
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• 2002

In this study, methyl methacrylate (MMA)- modified polyester polymer concrete, in which the MMA was added to the unsaturated polyester resin, was developed for improving the early-age strength and the workability of the conventional polymer concrete, binder of which was the unsaturated polyester resin. Then the fundamental properties of the polymer concrete such as workability and strength were surveyed. The experimental results showed that the workability was remarkably improved as the MMA contents increased, and the filler-binder ratio was turned out to be important factor for the workability. Slump prediction equation was derived by the regression analysis based on MMA content and filler-binder ratio. Furthermore, early-age strength was greater when the MMA content were increased in the range of 20-40 % but the strength rather showed a tendency of decrease when the MMA content was 50 %.

• Computers and Concrete
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• v.22 no.4
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• pp.355-363
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• 2018

In the following paper, a socio-political heuristic search approach, named the imperialist competitive algorithm (ICA) has been used to improve the efficiency of the multi-layer perceptron artificial neural network (ANN) for predicting the compressive strength of concrete. 173 concrete samples have been investigated. For this purpose the values of slump flow, the weight of aggregate and cement, the maximum size of aggregate and the water-cement ratio have been used as the inputs. The compressive strength of concrete has been used as the output in the hybrid ICA-ANN model. Results have been compared with the multiple-linear regression model (MLR), the genetic algorithm (GA) and particle swarm optimization (PSO). The results indicate the superiority and high accuracy of the hybrid ICA-ANN model in predicting the compressive strength of concrete when compared to the other methods.

• Proceedings of the Korea Concrete Institute Conference
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• 2002.05a
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• pp.545-550
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• 2002

Concrete is one of the essential structural materials in the construction. But, concrete consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructor. Therefore, concrete mixes depend on experiences of experts. However, it is more and more difficult to determine concrete mixes design by empirical means because more ingredients like mineral and chemical admixtures are included. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network are used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength and slump are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.1
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• pp.163-169
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• 1999

The object of this study is to compare properties of massive fly-ash concrete with plain concrete. Two concrete mixtures comprising two batch each $1.0m^3$ in volume, were made from ready mixed concrete batch plant. The water-to-cementitious materials ratio was kept constant at 51.4%. Therefore, massive concrete specimen($W800{\times}D800{\times}H800mm$) was cast from ready mixed concrete to analyze history of temperature and core strength properties. Bleeding, time of slump loss and time of setting of the fresh concrete were measured. In order to estimate the properties of massive fly-ash concrete in hardened concrete, non-destructive tests such as rebound hardness, ultrasonic pulse velocity and maturity were performed and analyzed.