Computers and Concrete

  • 제14권5호
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  • Pages.563-575
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  • 2014
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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 of acceleration and impact force values of a reinforced concrete slab

  • Erdem, R. Tugrul (Department of Civil Engineering, Celal Bayar University)
  • 투고 : 2014.03.26
  • 심사 : 2014.07.31
  • 발행 : 2014.11.28
⟨ 이전 논문 다음 논문 ⟩

초록

Concrete which is a composite material is frequently used in construction works. Properties and behavior of concrete are significant under the effect of different loading cases. Impact loading which is a sudden dynamic one may have destructive effects on structures. Testing apparatuses are designed to investigate the impact effect on test members. Artificial Neural Network (ANN) is a computational model that is inspired by the structure or functional aspects of biological neural networks. It can be defined as an emulation of biological neural system. In this study, impact parameters as acceleration and impact force values of a reinforced concrete slab are obtained by using a testing apparatus and essential test devices. Afterwards, ANN analysis which is used to model different physical dynamic processes depending on several variables is performed in the numerical part of the study. Finally, test and predicted results are compared and it's seen that ANN analysis is an alternative way to predict the results successfully.

키워드

참고문헌

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피인용 문헌

  1. Impact dynamics and energy dissipation capacity of fibre-reinforced self-compacting concrete plates vol.138, 2017, https://doi.org/10.1016/j.conbuildmat.2017.02.011
  2. Investigation of lateral impact behavior of RC columns vol.22, pp.1, 2014, https://doi.org/10.12989/cac.2018.22.1.123
  3. Experimental Investigation of Impact Behaviour of RC Slab with Different Reinforcement Ratios vol.24, pp.1, 2014, https://doi.org/10.1007/s12205-020-1168-x
  4. Experimental and numerical investigation of impact behavior of reinforced concrete slab with different support conditions vol.21, pp.6, 2014, https://doi.org/10.1002/suco.202000216