Computers and Concrete

Techno-Press (테크노프레스)
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)
  • Received : 2014.03.26
  • Accepted : 2014.07.31
  • Published : 2014.11.28
⟨ Previous Next ⟩

Abstract

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.

Keywords

References

  1. Zineddin, M. and Krauthammer, T. (2007), "Dynamic response and behavior of reinforced concrete slabs under impact loading", Int. J. Imp. Eng., 34, 1517-1534. https://doi.org/10.1016/j.ijimpeng.2006.10.012
  2. Suaris, W. and Shah, S.P. (1983), "Properties of concrete subjected to impact", J. Struct. Eng., 109, 1727-1741. https://doi.org/10.1061/(ASCE)0733-9445(1983)109:7(1727)
  3. Yankelevsky, D.Z. (1997), "Local response of concrete slabs to low velocity missile impact", Int. J. Imp. Eng., 19(4), 331-343. https://doi.org/10.1016/S0734-743X(96)00041-3
  4. Tai, Y.S. and Tang, C.C. (2006), "Numerical simulation: The dynamic behavior of reinforced concrete plates under normal impact", Theo. Appl. Fract. Mech., 45, 117-127. https://doi.org/10.1016/j.tafmec.2006.02.007
  5. Nataraja, M.C, Dhang, N. and Gupta, A.P. (1999), "Statistical variations in impact resistance of steel fiber-reinforced concrete subjected to drop weight test", Cement Concrete Res., 29, 989-995. https://doi.org/10.1016/S0008-8846(99)00052-6
  6. Siewert, T.A. and Manahan, M.P. (2000), Pendulum Impact Testing: A Century of Progress, ASTM International, West Conshohocken, PA, USA.
  7. Guang, N.H. and Zong, W.J. (2000), "Prediction of compressive strength of concrete by neural networks", Cement Concrete Res., 30, 1245-1250. https://doi.org/10.1016/S0008-8846(00)00345-8
  8. Siddique, R., Aggarwal, P., Aggarwal, Y. (2011), "Prediction of compressive strength of self-compacting concrete containing bottom ash using artificial neural networks", Adv. Eng. Softw., 42, 780-786. https://doi.org/10.1016/j.advengsoft.2011.05.016
  9. Slonski, M. (2010), "A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks", Comput. Struct., 88, 1248-1253. https://doi.org/10.1016/j.compstruc.2010.07.003
  10. Lee, S.C. (2003), "Prediction of concrete strength using artificial neural networks", Eng. Struct., 25, 849-857. https://doi.org/10.1016/S0141-0296(03)00004-X
  11. Kim, J.I. and Kim, D.K. (2002), "Application of neural networks for estimation of concrete strength", KSCE J. Civ. Eng., 6(4), 429-438 https://doi.org/10.1007/BF02841997
  12. Oztas, A., Pala, M., Ozbay, E., Kanca, E., Caglar, N. and Bhatti, M.A. (2006), "Predicting the compressive strength and slump of high strength concrete using neural network", Construct. Build. Mat., 20(9), 769-775. https://doi.org/10.1016/j.conbuildmat.2005.01.054
  13. Yeh, I.C. (1998), "Modeling of strength of high performance concrete using artificial neural networks", Cement Concrete Res., 28(2), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3
  14. Kim, J.I., Kim, D.K., Feng, M.Q. and Yazdani, F. (2004), "Application of neural networks for estimation of concrete strength", J. Civ. Eng., 16(4), 257-264. https://doi.org/10.1061/(ASCE)0899-1561(2004)16:3(257)
  15. Caglar, N. and Garip, S.G. (2013), "Neural network based model for seismic assessment of existing RC buildings", Comput. Concr., 12(2), 229-241. https://doi.org/10.12989/cac.2013.12.2.229
  16. Erdem, R.T., Kantar, E., Gucuyen, E. and Anil, O. (2013), "Estimation of compression strength of polypropylene fibre reinforced concrete using artificial neural networks", Comput. Concrete, 12(5), 613-625. https://doi.org/10.12989/cac.2013.12.5.613
  17. Demir, F. (2008), "Prediction of elastic modulus of normal and high strength concrete by artificial neural networks", Construct. Build. Mat., 22(7), 1428-1435. https://doi.org/10.1016/j.conbuildmat.2007.04.004
  18. Uysal, M. and Tanyildizi, H. (2013), "Estimation of compressive strength of self compacting concrete containing polypropylene fiber and mineral additives exposed to high temperature using artificial neural networks", Construct. Build. Mat., 27(1), 404-414.

Cited by

  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