• Journal of the Korea Concrete Institute
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• v.15 no.1
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• pp.43-51
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• 2003

Recently, to consider financial and constructive aspect usage of Admixture such as Blast-Furnace Slag and Fly-Ash, are increased. Also the use of cold-weather-concrete is increased. Blast-Furnace Slag, a by-product of steel industry, have many advantage to reduce the heat of hydration, increase in ultimate strength and etc. But it also reduces early-age strength, so it is prevented from using of Blast-Furnace Slag at cold-weather-concrete. In this study, for the purpose of increasing usage of Blast-Furnace Slag at cold-weather-concrete, it is investigated the strength properties of concrete subjected to frost damage for the cause of early age curing. The factors of this experience to give early frost damaged were Freezing temperature(-1, -10, $-15^{\circ}C$), Early curing age(0, 12, 24, 48hour), Freezing times(0, 12, 24, 48hour). According to this study, if early curing is carried out before haying frost damage, the strength of concrete used admixture, subjected to frost damage, is recovered. And that properties are considered, the effect of using admixture like blast-furnace-slag, is very high

• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.4
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• pp.91-98
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• 2009

Recently, production cost of ready mixed concrete has been increased due to the rising cost of raw materials such as cement and aggregate etc. cause by the upturn of oil price and increase of shipping charge. The delivery cost of ready mixed concrete companies, however, has been decreased owing to their excessive competition in sale. Consequently, ready mixed concrete companies began to manufacture the concrete by mixing ground granulated blast furnace slag(GGBF) and fly-ash in order to lower the production cost. Therefore, the objective of this study was to predict 28 days strength of GGBF slag concrete by early strength(warm and hot water curing method) for the sake of managing with ease the quality of ready mixed concrete. In experimental results, the prediction equation for 28 days compressive strength of GGBF slag concrete could be produced through the linear regression analysis of early strength and 28 days strength. In order to acquire the reliability, all mixture were repeated as 3 times and each mixture order was carried out by random sampling. The prediction equation for 28 days strength of GGBF slag concrete by 1 day compressive strength(accelerated testing) according to warm and hot water curing method won the good reliability.

• Journal of the Korean Recycled Construction Resources Institute
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• v.6 no.1
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• pp.63-71
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• 2011

Portland cement production is under critical review due to high amount of $CO_2$ gas released to the atmosphere. Attempts to increase the utilization of a by-products such as fly ash and ground granulated blast-furnace slag to partially replace the cement in concrete are gathering momentum. But most of by-products is currently dumped in landfills, thus creating a threat to the environment. Many researches on alkali-activated concrete that does not need the presence of cement as a binder have been carried out recently. In this study, we investigated the influence of alkali activator and superplasticizer on the flowability and compressive strength of the alkali-activated mortar in oder to develop cementless alkali-activated concrete using blast furnace slag. In view of the results, we found out that the type and mixture ratio of alkali activator, the type and adding order of superplasticizer results to be significant factors. When cementless alkali-activated mortar using blast furnace slag manufactured with 1:1 the mass ratio of 9M NaOH and sodium silicate, and added superplasticizer before alkali activator in the mixer, we can be secured workability with 180 mm of flow during 1 hours and compressive strength of about 50 MPa under $20^{\circ}C$ curing condition at age of 28days.

• 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.19 no.6
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• pp.717-724
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• 2017

In this paper, four formulas are proposed via gene expression programming (GEP)-based models and regression analysis (RA) to predict the flexural strength ($f_s$) values of mortars containing different mineral admixtures that are ground granulated blast-furnace slag (GGBFS), silica fume (SF) and fly ash (FA) at different ages. Three formulas obtained from the GEP-I, GEP-II and GEP-III models are constituted to predict the $f_s$ values from the age of specimen, water-binder ratio and compressive strength. Besides, one formula obtained from the RA is constituted to predict the $f_s$ values from the compressive strength. To achieve these formulas in the GEP and RA models, 972 data of the experimental studies presented with mortar mixtures were gathered from the literatures. 734 data of the experimental studies are divided without pre-planned for these formulas achieved from the training and testing sets of GEP and RA models. Beside, these formulas are validated with 238 data of experimental studies un-employed in training and testing sets. The $f_s$ results obtained from the training, testing and validation sets of these formulas are compared with the results obtained from the experimental studies and the formulas given in the literature for concrete. These comparisons show that the results of the formulas obtained from the GEP and RA models appear to well compatible with the experimental results and find to be very credible according to the results of other formulas.

• Smart Structures and Systems
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• v.26 no.2
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• pp.241-251
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• 2020

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.

• Journal of the Korea Institute of Building Construction
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• v.7 no.4
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• pp.145-151
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• 2007

In this paper, tests are carried out in order to investigate the strength development of concrete under various binder types, W/B and curing temperature ranged from $5{\sim}20^{\circ}C$. Fly ash and blast furnace slag were incorporated by as much as 30%, respectively. Strength development of concrete are estimated using Logistic model and strength ratio of concrete at 28days to that at early age are also investigated. According to experimental results, it is found that good agreements are obtained between measured values and calculated ones using logistic model below $20^{\circ}C$. Strength ratio of concrete at 28days to that at early age increases in case W/B decreases and curing temperature increases. Tables and graphs for strength ratio of concrete are provided in this paper. It is capable of obtaining and predicting the periods to attain design strength by considering increment factor of strength easily with the table and graphs presented in this paper. This paper presents the reference data to decide removal time of form, time to reach target strength and strength inspection of remicon whether the test specimens meet the specified criteria of compressive strength. Multi regression models with respect to the relationship between 7days compressive strength and 28 days compressive strength depending on W/B and admixture types are presented.

• Journal of the Korea Institute of Building Construction
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• v.12 no.4
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• pp.451-459
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• 2012

At the Korea Land and Housing Corporation(LH), concretes with high design strength of 50 MPa and 80 MPa that are composed only of ordinary Portland cement, blast furnace slag, and fly ash are developed. To determine whether the developed high strength concretes have the same properties when they are produced in batch plant(B/P) condition in the ready mixed concrete plant, and as existing high strength concretes, field tests are performed and material properties are evaluated. To investigate the material properties of the high strength concretes before and after pumping, compressive strength, flowability, air content, hydration temperature, pumping and compactability are evaluated. In field tests, before and after pumping, flowability satisfied the relevant criteria. In terms of air content, while it was slightly decreased after pumping, it satisfied the requirements. Hydration temperature criteria were satisfied, and compactability was excellent as well. The study found that the developed ternary high strength concretes have the same properties as existing high strength concretes. They can also be useful for the construction of high-rise buildings, as they are economical.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.321-322
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• 2013

Recently, the exhaustion of resource and environmental damage is serious due to the global warming because of the CO₂ exhaust and each type the natural aggregate picking described below. meanwhile, The rest is the actual condition gone to the dumping ground that there is nearly no use which the waste glass can recycle and it is recycled. This research applied the waste glass as the cement substitute material the inorganic binder and coares aggregate substitute material. It utilizes the substitute material of the cement according to it and natural aggregate and tries to develop the environment-friendly artificial stone. The inorganic binder used the blast furnace slag, red mud, and fly ash. The straight type steel fiber, PVA fiber, PA fiber, and cellulosic fiber were used with a kind of fiber. As to the experimental item according to it, the compressive strength is the flexural strength and compressive strength.