• Computers and Concrete
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• v.6 no.1
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• pp.19-40
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• 2009

This paper describes the development of modified heat of hydration and maturity-strength models for concrete containing fly ash and slag. The modified models are developed based on laboratory and literature test results, which include different types of cement, fly ash, and slag. The new models consider cement type, water-to-cementitious material ratio (w/cm), mineral admixture, air content, and curing conditions. The results show that the modified models well predict heat evolution and compressive strength development of concrete made with different cementitious materials. Using the newly developed models, the sensitivity analysis was also performed to study the effect of each parameter on the hydration and strength development. The results illustrate that comparing with other parameters studied, w/cm, air content, fly ash, and slag replacement level have more significantly influence on concrete strength at both early and later age.

• Computers and Concrete
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• v.20 no.1
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• pp.111-118
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• 2017

The present study established prediction models based on multiple nonlinear regressions (MNLRs) and backpropagation neural networks (BPNs) for the expansion of cement mortar caused by oxidization slag that was used as a replacement of the aggregate. The data used for the models were obtained from actual laboratory tests on specimens that were produced with water/cement ratios of 0.485 or 1.5, within which 0%, 10%, 20%, 30%, 40%, or 50% of the cement had been replaced by oxidization slag from electric-arc furnaces; the samples underwent high-temperature curing at either $80^{\circ}C$ or $100^{\circ}C$ for 1-4 days. The varied mixing ratios, curing conditions, and water/cement ratios were all used as input parameters for the expansion prediction models, which were subsequently evaluated based on their performance levels. Models of both the MNLR and BPN groups exhibited $R^2$ values greater than 0.8, indicating the effectiveness of both models. However, the BPN models were found to be the most accurate models.

• Computers and Concrete
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• v.18 no.1
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• pp.1-16
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• 2016

In this study, an Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) prediction models for flexural strength of the cement mortars have been developed. For purpose of constructing this models, 12 different mixes with 144 specimens of the 2, 7, 28 and 90 days flexural strength experimental results of cement mortars containing pure Portland cement (PC), blast furnace slag (BFS), waste tire rubber powder (WTRP) and BFS+WTRP used in training and testing for ANN and ANFIS were gathered from the standard cement tests. The data used in the ANN and ANFIS models are arranged in a format of four input parameters that cover the Portland cement, BFS, WTRP and age of samples and an output parameter which is flexural strength of cement mortars. The ANN and ANFIS models have produced notable excellent outputs with higher coefficients of determination of $R^2$, RMS and MAPE. For the testing of dataset, the $R^2$, RMS and MAPE values for the ANN model were 0.9892, 0.1715 and 0.0212, respectively. Furthermore, the $R^2$, RMS and MAPE values for the ANFIS model were 0.9831, 0.1947 and 0.0270, respectively. As a result, in the models, the training and testing results indicated that experimental data can be estimated to a superior close extent by the ANN and ANFIS models.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.1
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• pp.125-132
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• 2020

The causes of concrete shrinkage are very diverse, in particular, aggregates impact the characteristics of shrinkage in concrete by constraining the shrinkage of cement paste. Meanwhile, owing to the lack of natural aggregate, various alternative aggregates are being developed, and their application in concrete also becomes more diverse. This study aimed to experimentally evaluate the drying and autogenous shrinkage in concrete that was composed of electric arc furnace slag as coarse aggregates. And, the results were compared with prediction models. From the results, the application of electric arc furnace slag can reduce the drying and autogenous shrinkage. In particular, autogenous shrinkage is greatly decreased. The predictions using GL2000 for drying shrinkage and Tazawa model for autogenous shrinkage were similar to the experimental results. However, the most prediction models do not consider the impact of aggregates, hence, the new prediction model should be developed or improved.

• International Journal of Concrete Structures and Materials
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• v.5 no.2
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• pp.125-131
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• 2011

Five all-lightweight alkali-activated (AA) slag concrete mixes were tested according to the variation of water content to examine the significance and limitation on the development of cementless structural concrete using lightweight aggregates. The compressive strength development rate and shrinkage strain measured from the concrete specimens were compared with empirical models proposed by ACI 209 and EC 2 for portland cement normal weight concrete. Splitting tensile strength, and moduli of elasticity and rupture were recorded and compared with design equations specified in ACI 318-08 or EC 2, and a database compiled from the present study for ordinary portland cement (OPC) lightweight concrete, wherever possible. Test results showed that the slump loss of lightweight AA slag concrete decreased with the increase of water content. In addition, the compressive strength development and different mechanical properties of lightweight AA slag concrete were comparable with those of OPC lightweight concrete and conservative comparing with predictions obtained from code provisions. Therefore, it can be proposed that the lightweight AA slag concrete is practically applicable as an environmental-friendly structural concrete.

• Computers and Concrete
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• v.32 no.1
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• pp.45-60
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• 2023

Concrete in water environment is easily subjected to the attack of leaching, which causes its mechanical reduction and durability deterioration, and the key to improving the leaching resistance of concrete is to increase the compaction of its microstructure formed by the curing. This paper performs a numerical investigation on the intrinsic relationship between microstructures formed by the hydration of cement and slag and leaching resistance of concrete in water environment. Firstly, a shrinking-core hydration model of blended cement and slag is presented, in which the interaction of hydration process of cement and slag is considered and the microstructure composition is characterized by the hydration products, solution composition and pore structure. Secondly, based on Fick's law and mass conservation law, a leaching model of hardened paste is proposed, in which the multi-species ionic diffusion equation and modified Gérard model are established, and the model is numerically solved by applying the finite difference method. Finally, two models are combined by microstructure composition to form an integrated curing-leaching model, and it is used to investigate the relationship between microstructure composition and leaching resistance of slag-blended cement pastes.

• Computers and Concrete
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• v.21 no.5
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• pp.559-567
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• 2018

In this study alkali activation of Electric Arc Furnace Slag (EAFS) is studied with a comprehensive test program. Three different silicate moduli (1-1,5-2), three different sodium concentrations (4%-6%-8%) for each silicate module, two different curing conditions (45%-98% relative humidity) for each sodium concentration, two different curing temperatures ($400^{\circ}C-800^{\circ}C$) for each relative humidity condition and two different curing time (6h-12h) for each curing temperature variables are selected and their effects on compressive strength was evaluated then regression equations using multiple linear regressions methods are fitted. And then to select the best regression models confirm with using the variables, the regression models compared between itself. An Artificial Neural Network (ANN) models that use silicate moduli, sodium concentration, relative humidity, curing temperature and curing time variables, are formed. After the investigation of these ANN models' results, ANN and multiple linear regressions based models are compared with each other. After that, an explicit formula is developed with values of the ANN model. As a result of this study, the fluctuations of data set of the compressive strength were very well reflected using both of the methods, multiple linear regression with quadratic terms and ANN.

• Computers and Concrete
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• v.19 no.3
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• Structural Engineering and Mechanics
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• v.62 no.1
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• pp.21-31
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• 2017

Engineered Cementitious Composite (ECC) is a special class of the new generation of high performance fiber reinforced cementitious composites (HPFRCC) featuring high ductility with relatively low fiber content. In this research, the mechanical performance of ECC beams will be investigated with respect to the effect of slag and aggregate size and amount, by employing nonlinear finite element method. The validity of the models was verified with the experimental results of the ECC beams under monotonic loading. Based on the numerical analysis method, nonlinear parametric study was then conducted to evaluate the influence of the ECC aggregate content (AC), ECC compressive strength ($f_{ECC}$), maximum aggregate size ($D_{max}$) and slag amount (${\phi}$) parameters on the flexural stress, deflection, load and strain of ECC beams. The simulation results indicated that when increase the slag and aggregate size and content no definite trend in flexural strength is observed and the ductility of ECC is negatively influenced by the increase of slag and aggregate size and content. Also, the ECC beams revealed enhancement in terms of flexural stress, strain, and midspan deflection when compared with the reference beam (microsilica MSC), where, the average improvement percentage of the specimens were 61.55%, 725%, and 879%, respectively. These results are quite similar to that of the experimental results, which provides that the finite element model is in accordance with the desirable flexural behaviour of the ECC beams. Furthermore, the proposed models can be used to predict the flexural behaviour of ECC beams with great accuracy.

• 한국연소학회:학술대회논문집
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• 2006.04a
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• pp.167-171
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• 2006

The previous models for thermal behavior in the melting furnace were deterministic, composed of such a form that if the initial input conditions are determined, the results would have been come out by using the basic heat equilibrium equations. But making the experiment by trusting the analysis results, the melted slag is fortuitously set often, because temperature variation of the melted slag in the reaction process is not point function but path function. So in this study, a transient model was developed and verified by comparing with the experimental results.