• Steel and Composite Structures
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• v.51 no.5
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• pp.529-543
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• 2024

The bonding efficacy of steel I-section embedded in metakaolin-fly ash-based geopolymer concrete (MK-FA-GC) was investigated in this study. Push-out tests were conducted on nine column specimens to evaluate the influence of compressive strength of concrete, embedded length of steel I-section, thickness of concrete cover, and stirrup ratio on the bond performance. Failure patterns, load-slip relationships, bond strength, and distribution of bond stress among the specimens were analyzed. The characteristic bond strength of geopolymer concrete (GC) increased with higher compressive strength, longer embedded steel section length, thicker concrete cover, and larger stirrup ratio. Empirical formulas for bond strength at the loading end were derived based on experimental data and a bond-slip constructive model for steel-reinforced MK-FA-GC was proposed. The calculated bond-slip curves showed good agreement with experimental results. Furthermore, numerical simulations using ABAQUS software were performed on column specimens by incorporating the suggested bond-slip relationship into connector elements to simulate the interface behavior between MK-FA-GC and the steel section. The simulation results showed a good correlation with the experimental findings.

• Computers and Concrete
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• v.5 no.1
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• pp.75-88
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• 2008

This paper presents a mathematical model for strength and porosity of mortars made with ternary blends of ordinary Portland cement (OPC), ground rice husk ash (RHA) and classified fly ash (FA). The mortar mixtures were made with Portland cement Type I containing 0-40% FA and RHA. FA and RHA with 1-3% by weight retained on a sieve No. 325 were used. Compressive strength and porosity of the blended cement mortar at the age of 7, 28 and 90 days were determined. The use of ternary blended cements of RHA and FA produced mixes with good strength and low porosity of mortar. A mathematical analysis and two-parameter polynomial model were presented for the strength and porosity estimation with FA and RHA contents as parameters. The computer graphics of strength and porosity of the ternary blend were also constructed to aid the understanding and the proportioning of the blended system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2007.11a
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• pp.79-82
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• 2007

Recently, the use of ALC has became increasingly popular. ALC is a unique building material. Because of its cellular nature, it is lightweight, self-insulating, sound and fireproof, as well as insect and mold resistant. Furthermore, ALC is free of VOCs and various fibers associated with wood and glass wool construction. However, ALC have high water absorption, low compressive strength and popout the origin of the low surface strength in its properties. These properties make troubles under construction such as cracking and popout. Thus, this study is to improve the fundamental strength by controls of increasing of admixtures, and grain size. Admixtures make use of metakaolin, silica fume, sodium silicate and sodium hydroxide. From the test result, the ALC using admixture have a good fundamental properties compared with plain specimen. Compressive strength, specific strength and abrasion's ratio were improved depending on increasing admixtures ratio's, and grain size.

• 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.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2003.11a
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• pp.124-129
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• 2003

In case that the mixing weight ratio of waste form between boric acid and paraffin was 3.3/l, which had been adopted in the concentrate waste drying system (CWDS) of domestic nuclear power plants. Using several specimens with different diameters and heights, 50/100mm specimens. compressive strength were measured. The experiment result showed that the small diameter specimens of compressive strength are increased more than large diameter specimens. (d=50＞75＞100mm) The average compressive strength of specimens showed that the range from 22.43 $\kg/textrm{cm}^2$ to 38.57$\kg/textrm{cm}^2$ (NRC standard$\geq$4.1 $\kg/textrm{cm}^2$). NRC standard is recommended that the compressive strength test specimens be right circular cylinders, 2 to 3 inches in diameter, with a height-to-diameter(H/D) ratio of approximately two. and compressive strength were increased more than large loading rate. As test result, this conditions are a good agreement, and estimated.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.11a
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• pp.89-92
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• 2001

Because the grading of aggregate is major factor affecting on compressive strength and durability of concrete, the standard specification of concrete has proposed that the standard grading should be used to make ordinary concrete having good quality. But, it is not suitable for making product having low W/C because of difference between them in manufacturing processes and demanded efficiencies. This study investigated if the grading of the fine aggregate affects on tamping efficiency and compressive strength of concrete-based product. The results of this study showed that the suitable grading for making concrete-based product ranged from C type(FM:2.77) to D type(FM:3.38).

• Proceedings of the Korea Concrete Institute Conference
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• 1995.04a
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• pp.1-7
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• 1995

The objective of this experimental investigation was to examine the fundamental properties of antiwashout underwater concrete. Expriments were conducted on the antiwashout property in underwater, the compressive strength in the air and in underwater, setting time, slump flow loss. As a result, a dosage of 2.0-2.5kg/$\textrm{m}^3$ antiwashout admixture was found to be appropriate not to cause water pollution and to provide a reliably good compressive strength in underwater concrete. Also, the experimental results showed that the amount of less than 50mg/$\ell$ suspended solid was required to obtain the underwater to air compressive strength ratio of more than 80%

• 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.

• Computers and Concrete
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• v.27 no.1
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• pp.21-28
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• 2021

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2007.04a
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• pp.59-62
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• 2007

Nowadays, polymer-cement mortars are widely used in construction field(floorings and pavements, water-proofings, adhesives, repair materials, deck coverings, anti-corrosive linings) Because of excellent performance such as high tensile and flexural strength, waterproofness, excellent adhesion, good durability, improved wear and chemical resistances. This article presents the results of experimental study that investigates the effect of ambient temperature on the strength properties of polymer-modified cement mortar. Results show that when increasing the polymer proportion in mortar on different ambient temperature, the compressive strength and flexural strength are decreased, and also alkali resistance is decreased.