• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.2
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• pp.101-111
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• 2012

In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

• Journal of the Korea Institute of Building Construction
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• v.2 no.4
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• pp.177-182
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• 2002

The purpose of this study is to develope the strength prediction model by Maturity Method. A maturity function is a mathematical expression to account for the combined effects of time and temperature on the strength development of a cementious mixture. The method of equivalent ages is to use Arrhenius equation which indicates the influence of curing temperature on the initial hydration ratio of cement. For the experimental factors of this study, we selected the concrete mixing of W/C ratio 45, 50, 55 and 60% and curing temperature 5, 10, 20 and $30^{\circ}C$. And we compare and evaluate with logistic model that is existing strength prediction model, because we have to verify adaption possibility of new strength prediction model which is proposed by maturity method. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor.

• Computers and Concrete
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• v.21 no.5
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• pp.513-523
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• 2018

Time dependent strain due to creep is a significant factor in structural design. Multi-gene genetic programming (MGGP) and artificial neural network (ANN) are used to develop two models for prediction of creep compliance in concrete. The first model was developed by MGGP technique and the second model by hybridized MGGP-ANN. In the MGGP-ANN, the ANN is working in parallel with MGGP to predict errors in MGGP model. A total of 187 experimental data sets that contain 4242 data points are filtered from the NU-ITI database. These data are used in developing the MGGP and MGGP-ANN models. These models contain six input variables which are: average compressive strength at 28 days, relative humidity, volume to surface ratio, cement type, age at start of loading and age at the creep measurement. Practical equation based on MGGP was developed. A parametric study carried out with a group of hypothetical data generated among the range of data used to check the generalization ability of MGGP and MGGP-ANN models. To confirm validity of MGGP and MGGP-ANN models; two creep prediction code models (ACI209 and CEB), two empirical models (B3 and GL 2000) are used to compare their results with NU-ITI database.

• Structural Engineering and Mechanics
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• v.59 no.5
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• pp.867-883
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• 2016

In this paper, the effects of steel-fiber and rebar reinforcements on the ultimate bearing strength of the local anchorage zone were investigated based on experiments and comparisons between test results and design-equation predictions (AASHTO 2012, NCHRP 1994). Eighteen specimens were fabricated using the same anchorage device, which is one of the conventional anchorage devices, and two transverse ribs were used to secure an additional bearing area for a compact anchorage-zone design. Eight of the specimens were reinforced with only steel fiber and are of two concrete strengths, while six were reinforced with only rebars for two concrete strengths. The other four specimens were reinforced with both rebars and steel fiber for one concrete strength. The test and the comparisons between the design-equation predictions and the test results showed that the ultimate bearing strength and the section efficiency are highly affected by the reinforcement details and the concrete strength; moreover, the NCHRP equation can be conservatively applied to various local anchorage zones for the prediction of the ultimate bearing strength, whereby conditions such as the consideration of the rib area and the calibration factor are changed.

• Journal of the Korean Society of Safety
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• v.30 no.6
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• pp.102-109
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• 2015

In this study, on the basis of the results of the field survey and the theoretical consideration for Korean Standard Specification for concrete durability and maintenance, the following conclusions are derived. From the survey, the prediction equation of carbonation depth for the southwest region in Korea is experimentally proposed, $y_p=5.865{\sqrt{t}}$, which predicts about 60mm of the carbonation depth for the concrete structures of 100 years, a 1st class of target endurance period, under a combined deterioration environment like a marine environment. Considering that the marginal value for a carbonation depth limitation under very severely marine environment is 25mm, in accordance with the Specification, it is found that the predicting carbonation depth for the concrete cover depths, 100mm and 60mm are 63mm and 29.4mm, respectively. In conclusion, according to the equation and the Specification, it is strongly required that the reinforced concrete structures with the cover depth under 100mm have to make a protection from combined deterioration factors by any methods like a surface coating, an increment of cover depth or an application of a special concrete.

• Journal of KSNVE
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• v.7 no.2
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• pp.293-300
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• 1997

Noise and vibration induced by subway operation is one of the major problem for the residents living nearby railway tracks. Many scientists and engineers have been working on proposing the more accurate prediction equation of noise and vibration to provide the better residential environment. Some predictiction equations were determined to compare the measurement value of a noise obtained from the inside of a residential area. It was observed that the condition of a soil type is one of the major parameter which should be considered to obtain the more accurate prediction value of a noise.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.3
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• pp.122-129
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• 2015

In this study, a prediction model for the effective compressive strength of corner columns with intervening normal strength concrete slabs was developed. A structural analogy between high-strength concrete column-normal strength concrete slab joint and brick masonry was used to develop the prediction model. In addition, the aspect ratio of slab thickness to column dimension was considered in the models. The reliability of the new prediction model was evaluated by comparison with experimental results and its superiority was demonstrated by comparison with previous models proposed by design codes and other researchers. As a result, with average test-to-predicted ratios of 1.09, a standard deviation of 0.15, the newly developed equation provided superior predictions in terms of accuracy and consistency over all of the existing effective strength prediction approaches including KCI structural concrete design code (2012).

• Structural Engineering and Mechanics
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• v.52 no.6
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• pp.1273-1287
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• 2014

Plates are most widely used in the hulls of floating concrete structures, bridge decks, walls of off-shore structures and liquid storage tanks. A method of analysis is presented for the determination of load-deflection response and ultimate strength of high-strength steel fiber reinforced concrete (HSSFRC) plates simply supported on all four edges and subjected to combined action of external compressive in-plane and transverse loads. The behavior of HSSFRC plate specimens subjected to combined uniaxial in-plane and transverse loads was investigated. The proposed analytical method is compared to the physical test results, and shows good agreement. To predict the constitutive behavior of HSSFRC in compression, a non-dimensional characteristic equation was proposed and found to give reasonable accuracy.

• Journal of the Korean Society for Railway
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• v.3 no.2
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• pp.51-61
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• 2000

In this study, field measurements of vibration on the structures supporting railroad track were performed. The vibration data obtained were analyzed to find out any correlation between its magnitude and several factors such as type of bridges, distance from the track, type of train, frequency characteristics, etc. As a result, the magnitude of vibration turned out to be the highest in the steel bridge, the concrete bridge and steel-concrete combined bridge were the next in descending order. It was also found that the dynamic characteristics of ground were the most important factors among several affecting vibration near by the railroad track. And the empirical ground vibration estimation equation for estimating ground vibration was developed. The proposed equation with respect to distances from the railroad could be easily used for the estimation of ground vibration at the residential areas nearby the track.