Advances in concrete construction

  • 제10권3호
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  • Pages.247-256
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  • 2020
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  • 2287-5301(pISSN)
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  • 2287-531X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of mechanical properties of limestone concrete after high temperature exposure with artificial neural networks

  • Blumauer, Urska (Faculty of Civil and Geodetic Engineering, University of Ljubljana) ;
  • Hozjan, Tomaz (Faculty of Civil and Geodetic Engineering, University of Ljubljana) ;
  • Trtnik, Gregor (Building Materials Institute)
  • 투고 : 2020.02.20
  • Accepted : 2020.08.13
  • Published : 2020.09.25
⟨ 이전 논문 다음 논문 ⟩

Abstract

In this paper the possibility of using different regression models to predict the mechanical properties of limestone concrete after exposure to high temperatures, based on the results of non-destructive techniques, that could be easily used in-situ, is discussed. Extensive experimental work was carried out on limestone concrete mixtures, that differed in the water to cement (w/c) ratio, the type of cement and the quantity of superplasticizer added. After standard curing, the specimens were exposed to various high temperature levels, i.e., 200℃, 400℃, 600℃ or 800℃. Before heating, the reference mechanical properties of the concrete were determined at ambient temperature. After the heating process, the specimens were cooled naturally to ambient temperature and tested using non-destructive techniques. Among the mechanical properties of the specimens after heating, known also as the residual mechanical properties, the residual modulus of elasticity, compressive and flexural strengths were determined. The results show that residual modulus of elasticity, compressive and flexural strengths can be reliably predicted using an artificial neural network approach based on ultrasonic pulse velocity, residual surface strength, some mixture parameters and maximal temperature reached in concrete during heating.

Keywords

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