• 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.

• Magazine of the Korea Concrete Institute
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• v.8 no.4
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• pp.141-148
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• 1996

The pulished works on Accelerated Curing Effect were generally performed around from 1960 to 1970th century for 18 to 24 hours - total curing periods. It is not possible to define the effect of temperature rise because those results were obtaine mainly by using the manually operated steam-curing tank. Thus, it may not be available to apply those data immediately on the domestic PC wall production line. The testing specimens were made from the standard mix proportion according to those of domestic PC factories to establish a basic data for the Accelerated Curing Effect. The experimental tests were conducted according to the conditions of each sub-curing periods. By comparing the results of compression tests on de-molded and 28-day water-curing specimens, we find that the most effective curing condition to obtain more than the required design strength after 28 day of water curing may be as follows: the presteaming period does not affect seriously and less than$30^{circ}C/hr$- the rate of temperature rise andless than $82^{circ}C$ - maximum temperature are necessary. It seems that post-curing procedure is very important factor to increase the effect of accelerated curing.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.5
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• pp.92-103
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• 2015

Box-Wilson experimental design method, known as central composite design, is the design of any information-gathering exercises where variation is present. This method was devised to gather as much data as possible in spite of the low design cost. This method was employed to model the effect of mixing factors on several performances of 60 MPa high strength self compacting concrete and to numerically calculate the optimal mix proportion. The nonlinear relations between factors and responses of HSSCC were approximated in the form of second order polynomial equation. In order to characterize five performances like compressive strength, passing ability, segregation resistance, manufacturing cost and density depending on five factors like water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content, the experiments were made at the total 52 experimental points composed of 32 factorial points, 10 axial points and 10 center points. The study results showed that Box-Wilson experimental design was really effective in designing the experiments and analyzing the relation between factor and response.