• International Journal of Concrete Structures and Materials
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• v.7 no.3
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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.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.6
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• pp.82-89
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• 2014

The purpose of this study is to propose a simple model to evaluate the compressive strength development of concrete with various supplementary cementitious materials (SCMs) and cured under different temperatures. For the generalization of the model, the ACI 209 parabola equation was modified based on the maturity function and then experimental constants A and B and 28-day compressive strength were determined from the regression analysis using a total of 265 data-sets compiled from the available literature. To verify the proposed model, concrete specimens classified into 3 Groups were prepared according to the SCM level as a partial replacement of cement and curing temperature. The analysis of existing data clearly revealed that the 28-day compressive strength decreases when the curing temperature is higher and/or lower than the reference curing temperature ($20^{\circ}C$). Furthermore, test results showed that the compressive strength development of concrete cured under $20^{\circ}C$ until an early age of 3 days was marginally affected by the curing temperature afterward. The proposed model accurately predicts the compressive strength development of concrete tested, indicating that the mean and standard deviation of the ratios between predictions and experiments are 1.00 and 0.08, respectively.

• Journal of Ocean Engineering and Technology
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• v.26 no.2
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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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• v.16 no.4
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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.

• Journal of Ocean Engineering and Technology
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• v.22 no.3
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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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• v.16 no.1
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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).

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.6
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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 $900^{\circ}C{\cdot}h^{0.4}$.

• Structural Engineering and Mechanics
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• v.68 no.6
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2005.11a
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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.

• Architectural research
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• v.15 no.1
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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.