DOI QR코드

DOI QR Code

Displacement prediction of precast concrete under vibration using artificial neural networks

  • 투고 : 2018.08.14
  • 심사 : 2020.01.04
  • 발행 : 2020.05.25

초록

This paper intends to progress models to accurately estimate the behavior of fresh concrete under vibration using artificial neural networks (ANNs). To this end, behavior of a full scale precast concrete mold was investigated numerically. Experimental study was carried out under vibration with the use of a computer-based data acquisition system. In this study measurements were taken at three points using two vibrators. Transducers were used to measure time-dependent lateral displacements at these points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using ANNs. Benefiting ANNs used in this study for modeling fresh concrete, mold design can be performed. For the modeling of ANNs: Experimental data were divided randomly into two parts such as training set and testing set. Training set was used for ANN's learning stage. And the remaining part was used for testing the ANNs. Finally, ANN modeling was compared with measured data. The comparisons show that the experimental data and ANN results are compatible.

키워드

참고문헌

  1. Aktas, G., Tanrikulu, A.K. and Baran, T. (2014), "Computer-aided mold design algorithm for precast concrete elements", ACI Mat. J., 111(1), 77-87.
  2. Aktas, G. and Karasin, A. (2014), "Experimental confirmation for the validity of Ritz method in structural dynamic analysis", J. Theor. App. Mech., 52(4), 981-993. https://doi.org/10.15632/jtam-pl.52.4.981
  3. Aktas, G. (2016), "Investigation of fresh concrete behavior under vibration using mass-spring model", Struct. Eng. Mech, 57(3),425-439. http://dx.doi.org/10.12989/sem.2016.57.3.425.
  4. Aktas, G. and Ozerdem, M.S. (2016), "Prediction of behavior of fresh concrete exposed to vibration using artificial neural networks and regression model", Struct. Eng. Mech, 60(4),655-665. https://doi.org/10.12989/sem.2016.60.4.655.
  5. Alexsandridis, A. and Gardner, N.J. (1981), "Mechanical behaviour of fresh concrete", Cement Concrete Res., 11(3), 323-339. https://doi.org/10.1016/0008-8846(81)90105-8.
  6. Ashteyat, A.M. and Ismeik, M. (2018), "Predicting residual compressive strength of self-compacted concrete under various temperatures and relative humidity conditions by artificial neural networks", Comp. Concrete, 21(1), 47-54. https://doi.org/10.12989/cac.2018.21.1.047.
  7. Beale, M.H., Hagan, M.T. and Demuth, H.B. (2014), Neural Network Toolbox User's Guide, The MathWorks, Inc., Natick, MA, USA.
  8. Cascardi, A., Micelli, F. and Aiello, M.A. (2017), "An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns", Eng. Struct., 140, 199-208. https://doi.org/10.1016/j.engstruct.2017.02.047.
  9. Chithra, S., Senthil, S.R.R., Kumar, K., Chinnaraju, F. and Ashmita, A. (2016), "A comparative study on the compressive strength prediction models for High Performance Concrete containing nano silica and copper slag using regression analysis and Artificial Neural Networks", Const. Build. Mat.,114, 528-553. https://doi.org/10.1016/j.conbuildmat.2016.03.214.
  10. Demir, A. (2015), "Prediction of Hybrid fibre-added concrete strength using artificial neural networks", Comp. Concrete, 15(4), 503-514. http://dx.doi.org/10.12989/cac.2015.15.4.503.
  11. Duan, Z.H., Kou, S.C. and Poon, C.S. (2013), "Prediction of compressive strength of recycled aggregate concrete using artificial neural networks", Const. Build. Mat.40, 1200-1206. https://doi.org/10.1016/j.conbuildmat.2012.04.063.
  12. Erdem, H. (2010), "Prediction of the moment capacity of reinforced concrete slabs in fire using artificial neural networks", Adv. Eng. Soft., 41, 270-276. https://doi.org/10.1016/j.advengsoft.2009.07.006.
  13. Kao, C.S. and Yeh, I.C. (2014), "Optimal design of plane frame structures using artificial neural networks and ratio variables", Struct. Eng. Mech, 52(4), 739-753. https://doi.org/10.12989/sem.2014.52.4.739.
  14. Khademi, F. and Jamal, S.M. (2016), "Predicting the 28 days compressive strength of concrete using artificial neural network", i-Manager's J. Civ. Eng. 6(2), 1.
  15. Khademi, F., Jamal, S.M., Deshpande, N. and Londhe, S. (2016), "Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression", Inter. J. Sust.Built Env.,5, 355-369. https://doi.org/10.1016/j.ijsbe.2016.09.003.
  16. Larrard, F.D., Hu, C., Sedran. T., Szitkar. J.C., Jolt. M., Claux. F. and Derkx, F. (1997), "New Rheometer for Soft-to-Fluid Fresh Concrete", ACI Mat. J., 94(3), 234-243. https://doi.org/10.1007/BF02485970.
  17. Onat, O. and Yon, B. (2019), "Elimination of a measurement problem: A robust prediction model for missing eigenvector value to assess earthquake induced out-of-plane failure of infill wall", Measurement, 144, 88-104. https://doi.org/10.1016/j.measurement.2019.05.001.
  18. Tattersall, G.H. and Baker, P.H. (1988), "Effect of Vibration on the Rheological Properties of Fresh Concrete", Mag. Concrete Res., 40(143), 79-89. https://doi.org/10.1680/macr.1988.40.143.79
  19. Thomas, J. and Harilal, B. (2014), "Fresh and hardened properties of concrete containing cold bonded aggregates", Adv. Concrete Const., 2(2), 77-89. https://doi.org/10.12989/acc.2014.2.2.077.
  20. U.S. Department of Transportation (2003), "Poission's Ratio and Temperature Gradient Adjustments", HIPERPAV Validation Model Summary, Federal Highway Administration Research, Technology, and Development Turner-Fairbank Highway Research Center; Virginia, USA. 1-4.
  21. Wenzel, D. (1986), "Compaction of Concrete-Principles, Practice, Special Problems", Beton. Fert. Tech., 52(3), 153-158.
  22. Zhou, Q., Wang, F. and Zhu, F. (2016), "Estimation of compressive strength of hollow concrete masonry prisms using artificial neural network sand adaptive neuro-fuzzy inference systems", Const. Build. Mat.,125, 417-426. https://doi.org/10.1016/j.conbuildmat.2016.08.064.