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논문 기본 정보

자료유형
학술저널
저자정보
Chih-Han Kao (National Quemoy University) Chien-Chih Wang (Cheng Shiu University) Her-Yung Wang (National Kaohsiung University of Applied Sciences)
저널정보
한국계산역학회 Computers and Concrete, An International Journal Computers and Concrete, An International Journal Vol.19 No.5
발행연도
2017.1
수록면
457 - 465 (9page)

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초록· 키워드

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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.

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참고문헌 (28)

참고문헌 신청
Adaway, M. / 2015 / Recycled glass as a partial replacement for fine aggregate in structural concrete-effects on compressive strength / J. Struct. Eng. 14(1):116~122 google schola Chopra, P. / 2015 / Artificial neural networks for the prediction of compressive strength of concrete / J. Appl. Sci. Eng. 13(3):187~204 google schola Chun, Y. M. / 2007 / Sustainable construction materials and technologies / Proceedings of the International Conference on Sustainable Construction Materials and Technology google schola Glantz, S. A. / 1990 / Primer of Applied Regression and Analysis of Variance / McGraw-Hill google schola Hanehara, S. / 2001 / Effect of water/powder ratio, mixing ratio of fly ash and curing temperature on pozzolanic reaction of fly ash in cement pastes / Cement Concrete Res. 31(1):31~39 google schola

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