• Computers and Concrete
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• v.10 no.5
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• pp.523-539
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• 2012

Silica fume has long been used as a mineral admixture to improve the durability and produce high strength and high performance concrete. And in marine and coastal environments, penetration of chloride ions is one of the main mechanisms causing concrete reinforcement corrosion. In this paper, we proposed a numerical procedure to predict the chloride diffusion in a hydrating silica fume blended concrete. This numerical procedure includes two parts: a hydration model and a chloride diffusion model. The hydration model starts with mix proportions of silica fume blended concrete and considers Portland cement hydration and silica fume reaction respectively. By using the hydration model, the evolution of properties of silica fume blended concrete is predicted as a function of curing age and these properties are adopted as input parameters for the chloride penetration model. Furthermore, based on the modeling of physicochemical processes of diffusion of chloride ion into concrete, the chloride distribution in silica fume blended concrete is evaluated. The prediction results agree well with experiment results of chloride ion concentrations in the hydrating concrete incorporating silica fume.

• 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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1992.03a
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• pp.177-200
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• 1992

A thermo-viscoplastic finite element program was developed to analyze the compression molding of SMC process. Deformation of the material was modelled by using the flow-rule. Heat balance during the process was coupled to the deformation. In the cure study, a kinetic model was adopted to describe the cure behavior. The numerical kinetic model was integrated with the thermo-viscoplastic numerical analysis by adding heat generation due to the chemical reaction of the workpiece in the heat transfer analysis. The integrated finite element program can simulate a whole sequential molding process including deformation, heat transfer, and chemical reaction. A practical SMC molding process with T-shaped substructure was simulated. The simulated results showed good agreements with experiments.

• Tribology and Lubricants
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• v.16 no.4
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• pp.266-273
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• 2000

The friction characteristics of phenolic resin and model friction materials were investigated with the content of hexamethylenetetamine (HEXA). At 10 wt.% of HEXA, the phenolic resin and model friction materials showed the most stable friction coeffcient in constant temperature test at various test conditions because of its good thermal stability and proper curing reaction. It was found from constant interval test in mild condition that the friction coeffcients of friction materials cured with 10 wt.% of HEXA was the highest and stable values in the whole range of braking operations. However, at the severe condition in constant interval test, the friction coefficient of friction materials cured with 10 wt.% to of HEXA was lowered and as the number of braking operation increased, the values became stable. In order to obtain the thormal stable friction materials, the content of HEXA from 5 to 10 wt.% could be recommended.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2008.11a
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• pp.93-96
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• 2008

This research performed strength improvement analysis after evaluating strength characteristics by estimated temperatures to evaluate the real time strength performance of 50 to 80 MPa high performance concrete equipped with heat resistance, and the results are as follows. The lesser W/B and the lesser target slump flow value difference, compression strength was shown to increase, and the more curing temperature becomes, the strength increased accordingly. According to the correlation review result of strength improvement analysis by estimated temperature change performed using logistic analysis model, the compression strength value predicted with logistic curve expression and the compression strength value measured in experiment were shown to have similar correlation, and the strength improvement analysis value by logistic model was shown to be estimated good when W/B is high.

• Elastomers and Composites
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• v.53 no.1
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• pp.1-5
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• 2018

In the current research, the curing kinetics of a mixture system consisting of a Bisphenol-A type vinyl ester resin and styrene monomer was studied. Methylethylketone peroxide and cobalt octoate were used as the polymerization initiator and accelerator respectively. Thermograms with several different heating rates were obtained using non-isothermal differential scanning calorimetry. Activation energy values analyzed by the Flynn-Wall-Ozawa isoconversional method showed a three-step change with conversion ${\alpha}$: a slight decrease initially for ${\alpha}$ < 0.1, a constant value of 47.9 kJ/mol in the range 0.1 < ${\alpha}$ < 0.7, and a slow increase for 0.7 < ${\alpha}$. When assuming a constant activation energy of 47.9 kJ/mol, an autocatalytic model of the Sestak-Berggren equation was considered as the proper mathematical model of the conversion function, indicating an overall order of 1.2.

• 한국가시화정보학회:학술대회논문집
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• 2001.12a
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• pp.138-150
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• 2001

Researchers have investigated nasal flow both numerically and experimentally for centuries. Experimental studies most have suffered from various limitations necessary to allow the measurements to be obtained with available equipment. Nasal airflow can be subdivided into two interrelated categories; nasal airflow resistance and heat and mass transfer between the air stream and the walls of the nasal cavity. In this study, thanks to a new method for model casting by a combination of Rapid prototyping and curing of clear silicone, a transparent rectangular box containing the complex nasal cavity is made for PIV experiments. The CBC PIV algorithm is used for analysis. Average and RMS distributions are obtained for inspirational and expiration nasal airflows. Comparison between western and Korean nasal air flows are appreciated. Flow fields for Korean model shows some differences from western's. Flow resistances for breathing are measured with varying flow rates.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.251-252
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• 2012

The exiting studies on the strength prediction by maturity method is mainly focused on concrete using OPC, meanwhile the study on the concrete mixing blast furnace slag powder (BFSP) is insufficient. The purpose of this study is to investigate the relationships between compressive strength and equivalent age by existing Maturity functions, i.e., Nurse-saul function Arrhenius function. This study also compared and examined the strength prediction of concrete mixing BGSP using ACI model and Logistic Curve prediction equation. Therefore, it is intended that fundamental data are presented for quality management and process management of concrete mixing BFSP.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.4
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• pp.97-105
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• 2001

The ultrasonic pulse velocity test has a strong potential to be developed into a very useful and relatively inexpensive in-place test for assuring the quality of concrete placed in structure. The main problem in realizing this potential is that the relationship between compressive strength ad ultrasonic pulse velocity is uncertain and concrete is an inherently variable material. The objective of this study is to improve the reliability of in-place concrete strength predictions by ultrasonic pulse velocity method. Experimental cement content, s/a rate, and curing condition of concrete. Accuracy of the prediction expressed in empirical formula are examined by multiple regression analysis and linear regression analysis and practical equation for estimation the concrete strength are proposed. Multiple regression model uses water-cement ratio cement content s/a rate, and pulse velocity as dependent variables and the compressive strength as an independent variable. Also linear regression model is used to only pulse velocity as dependent variables. Comparing the results of the analysis the proposed equation expressed highest reliability than other previous proposed equations.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.130-131
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• 2018

The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were learned using the Deep Learning Algorithm to derive an estimated function model. The purpose of this study is to investigate the effect of the number of hidden layers on the prediction performance in the process of estimating the compressive strength for an arbitrary combination.