• International Journal of Concrete Structures and Materials
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• 제7권3호
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• pp.193-202
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• 2013

Geopolymer mortar compressed blocks were prepared using fly ash, ground granulated blast furnace slag, silica fume and metakaolin as binders and sand/quarry dust/pond ash as fine aggregate. Alkaline solution was used to activate the source materials for synthesizing the geopolymer mortar. Fresh mortar was used to obtain the compressed blocks. The strength development with reference to different parameters was studied. The different parameters considered were fineness of fly ash, binder components, type of fine aggregate, molarity of alkaline solution, age of specimen, fluid-to-binder ratio, binder-to-aggregate ratio, degree of saturation, etc. The compressed blocks were tested for compression at different ages. It was observed that some of the blocks attained considerable strength within 24 h under ambient conditions. The cardinal aim was to analyze the experimental data generated to formulate a phenomenological model to arrive at the combinations of the ingredients to produce geopolymer blocks to meet the strength development desired at the specified age. The strength data was analyzed within the framework of generalized Abrams' law. It was interesting to note that the law was applicable to the analysis of strength development of partially saturated compressed blocks when the degree of saturation was maintained constant. The validity of phenomenological model was examined with an independent set of experimental data. The blocks can replace the traditional masonry blocks with many advantages.

• 한국구조물진단유지관리공학회 논문집
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• 제18권6호
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• pp.82-89
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• 2014

이 연구의 목적은 다양한 혼화재의 치환과 양생온도를 고려한 콘크리트의 압축강도 발현을 평가할 수 있는 단순모델의 제시이다. 이를 위해 ACI 209의 포물선 식을 성숙도 함수를 기반으로 하여 수정하였으며, 압축강도 발현 상수 A, B 그리고 재령 28일 압축강도는 264개의 기존 실험결과들의 회귀분석으로부터 결정하였다. 제시된 모델의 검증을 위하여 혼화재 치환과 양생온도를 변수로 3그룹의 실험을 수행하였다. 콘크리트의 28일 압축강도는 양생온도가 표준양생온도(20도시)보다 높을수록 또는 낮을수록 감소하였다. 초기 재령3일동안 표준온도에서 양생을 한 콘크리트의 압축강도 발현은 그 이후 양생온도 변화에 영향을 거의 받지 않았다. 제안된 모델의 예측값과 실험값의 비의 평균과 표준편차는 각각 1.00와 0.08로서 실험결과와 잘 일치하였다.

• 한국해양공학회지
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• 제26권2호
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• pp.39-47
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• 2012

The objective of this study was to develop a strength prediction model using a polynomial regression analysis based on the experimental results obtained from ninety samples. As the results of a correlation analysis between various mixing factors and unconfined compressive strength using SPSS (statistical package for the social sciences), the governing factors in the strength of lightweight soil were found to be the crumb rubber content, bottom ash content,and water-cement ratio. After selecting the governing factors affecting the strength through the correlation analysis, a strength prediction model, which consisted of the selected governing factors, was developed using the polynomial regression analysis. The strengths calculated from the proposed model were similar to those resulting from laboratory tests (R2=87.5%). Therefore, the proposed model can be used to predict the strength of lightweight mixtures with various mixing ratios without time-consuming experimental tests.

• International Journal of Aeronautical and Space Sciences
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• 제16권4호
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• pp.537-547
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• 2015

A Monte Carlo (MC) simulation was conducted to predict the reliability of a newly developed pyrotechnic pin puller. The reliability model is based on the stress-strength interference model that states that failure occurs if the stress exceeds the strength. In this study, the stress is considered to be the energy consumed by movement of a pin shaft, and the strength is considered to be the energy generated by pyrotechnic combustion for driving the pin shaft. Failure of the pin puller can thus be defined as the consumed energy being greater than the generated energy. These energies were calculated using a performance model formulated in the previous study of the present authors. The MC method was used to synthesize the probability densities of the two energies and evaluate the reliability of the pin puller. From a probabilistic perspective, the calculated reliability was compared to a deterministic safety factor. A sensitivity analysis was also conducted to determine which design parameters most affect the reliability.

• 한국해양공학회지
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• 제22권3호
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• pp.64-70
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• 2008

A full-scale field experiment is an important part in the design of ships and offshore structures. Full-scale tests in the ice-covered sea, however, are usually very expensive and difficult tasks. Model tests in a refrigerated ice tank may substitute this difficulty of full-scale field tests. One of the major tasks to perform proper model tests in an ice towing tank is to select a realistic material for model ice which shows correct similitude with natural sea ice. This study focuses on the testing material properties and the selection of model ice material which will be used in an ice model basin. The first Korean ice model basin will be constructed at the Maritime & Ocean Engineering Research Institute (MOERI) in 2009. With an application to the MOERI ice model basin, in this study the material properties of EG/AD/S model ice of IOT (Institute for Ocean Technology) Canada, were tested. Through comprehensive bending tests, the elastic modulus and the flexural strength of EG/AD/S model ice were evaluated and the results were compared with published test results from Canada. Instead of using an ice model basin, a cold room facility was used for making a model ice specimen. Since the cold room adopts a different freezing procedure to make model ice, the strength of the model ice specimen differs from the published test results. The reason for this difference is discussed and the future development for a making model ice is recommended.

• Structural Engineering and Mechanics
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• 제16권1호
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• pp.83-99
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• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

• 한국농공학회논문집
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• 제59권6호
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• pp.19-27
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• 2017

This study investigated to predict the compressive strength of unsaturated polyester resin based polymer concrete using the maturity method. The test results show that the development of the compressive strength increased exponentially until an age of 24 hours. After 24 hours, the development of the compressive strength just increased gradually. This test result shows that the strength of unsaturated polyester resin based polymer concrete was developed mainly at the early age. Estimated datum temperature of unsaturated polyester resin based polymer concrete was $-20.67^{\circ}C$ which was much lower than of datum temperature ($-10^{\circ}C$) of Portland cement concrete. Also, this study result shows that the existing maturity index associated with Portland cement concrete was not applicable for polymer concrete because curing time of Portland cement concrete is different clearly with curing time of polymer concrete. The cause of different curing time was that there were different curing mechanisms between Portland cement concrete and polymer concrete. In order to best apply the experimental data to a model, CurveExpert Professional, the commercial software, was used to determine the predictive model regarding the compressive strength of unsaturated polyester resin based polymer concrete. As a result, Gompertz Relation or Weibull Model was an appropriate model as a predictive model. The proposed model can be used to predict the compressive strength, especially, it is more useful when the maturity is in the range between $40^{\circ}C{\cdot}h^{0.4}$ and $900^{\circ}C{\cdot}h^{0.4}$.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2005년도 추계 학술논문 발표대회
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• pp.75-78
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• 2005

This raper investigates the effect of curing temperature on strength development of concrete incorporating cement kiln dust(CKD) and blast furnace slag (BS) quantitatively. Estimation of compressive strength of concrete was conducted using equivalent age equation and rate constant model. An increasing curing temperature results in an increase in strength at early age, but with the elapse of age, strength development at high curing temperature decreases compared with that at low curing temperature. Especially, the use of 35 has a remarkable strength development at early age and even at later age, high strength is maintained due to accelerated pozzolanic activity resulting from high temperature. Whereas, at low curing temperature, the use of BS leads to a decrease in compressive strength. Accordingly, much attention should be paid to prevent strength loss at low temperature. Based on the strength development estimation using equivalent age equation, good agreements between measured strength and calculated strength are obtained.

• Structural Engineering and Mechanics
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• 제68권6호
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• Architectural research
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• 제15권1호
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• pp.53-58
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• 2013

High strength concrete is being used increasingly in mass structure projects. The purpose of this study is to investigate the influence of temperature during mixing, placing and curing on the strength development, hydration products and pore structures of high strength concrete in mass structures. The experiments were conducted with two different model walls, viz.: 1.5 m and 0.3 m under typical summer and winter weather conditions. The final part of this study deal with the clarification of the relationship between the long-term strength loss and the microstructure of the high strength concrete at high temperatures. Test results indicated that high elevated temperatures in mass concrete structures significantly accelerate the strength development of concrete at the early ages, while the long-term strength development is decreased. The long-term strength loss is caused by the decomposition of ettringite and increased the total porosity and amount of small pores.