Computers and Concrete

  • 제19권5호
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  • Pages.467-475
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  • 2017
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction model of resistivity and compressive strength of waste LCD glass concrete

  • Wang, Chien-Chih (Department of Civil Engineering and Geomatics, Cheng Shiu University)
  • 투고 : 2015.09.12
  • 심사 : 2017.01.18
  • 발행 : 2017.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

The purpose of this study is to establish a prediction model for the electrical resistivity ($E_r$) of self-consolidating concrete by using waste LCD (liquid crystal display) glass as part of the fine aggregate and then, to analyze the results obtained from a series of laboratory tests. A hyperbolic function is used to perform nonlinear multivariate regression analysis of the electrical resistivity prediction model, with parameters such as water-binder ratio (w/b), curing age (t) and waste glass content (G). Furthermore, the relationship of compressive strength and electrical resistivity of waste LCD glass concrete is also found by a logarithm function, while compressive strength is evaluated by the electrical resistivity of non-destructive testing (NDT). According to relative regression analysis, the electrical resistivity and compressive strength prediction models are developed, and the results show that a good agreement is obtained using the proposed prediction models. From the comparison between the predicted analysis values and test results, the MAPE value of electrical resistivity is 17.0-18.2% and less than 20%, the MAPE value of compressive strength evaluated by $E_r$ is 5.9-10.6% and nearly less than 10%. Therefore, the prediction models established in this study have good predictive ability for electrical resistivity and compressive strength of waste LCD glass concrete. However, further study is needed in regard to applying the proposed prediction models to other ranges of mixture parameters.

키워드

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피인용 문헌

  1. Establishment of the Controlled Low-Strength Desulfurization Slag Prediction Model for Compressive Strength and Surface Resistivity vol.10, pp.16, 2017, https://doi.org/10.3390/app10165674