Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of Hybrid fibre-added concrete strength using artificial neural networks

  • Demir, Ali (Department of Civil Engineering, Celal Bayar University)
  • Received : 2014.07.21
  • Accepted : 2015.02.25
  • Published : 2015.04.25
⟨ Previous Next ⟩

Abstract

Fibre-added concretes are frequently used in large site applications such as slab and airports as well as in bearing system elements or prefabricated elements. It is very difficult to determine the mechanical properties of the fibre-added concretes by experimental methods in situ. The purpose of this study is to develop an artificial neural network (ANN) model in order to predict the compressive and bending strengths of hybrid fibre-added and non-added concretes. The strengths have been predicted by means of the data that has been obtained from destructive (DT) and non-destructive tests (NDT) on the samples. NDTs are ultrasonic pulse velocity (UPV) and Rebound Hammer Tests (RH). 105 pieces of cylinder samples with a dimension of $150{\times}300mm$, 105 pieces of bending samples with a dimension of $100{\times}100{\times}400mm$ have been manufactured. The first set has been manufactured without fibre addition, the second set with the addition of %0.5 polypropylene and %0.5 steel fibre in terms of volume, and the third set with the addition of %0.5 polypropylene, %1 steel fibre. The water/cement (w/c) ratio of samples parametrically varies between 0.3-0.9. The experimentally measured compressive and bending strengths have been compared with predicted results by use of ANN method.

Keywords

Acknowledgement

Supported by : Celal Bayar University

References

  1. Altun, F., Kisi, O. and Aydin, K. (2008), "Predicting the compressive strength of steel fiber added lightweight concrete using neural network", Compos. Mater. Sci., 42, 259-265. https://doi.org/10.1016/j.commatsci.2007.07.011
  2. ASTM C 597-97 (1998), "Standard test method for pulse velocity through concrete", Easton, MD, USA: American Society for Testing and Materials International.
  3. ASTM C 805 (1997), Test for rebound number of hardened concrete", West Conshohocken, PA, USA: American Society for Testing and Materials International.
  4. ASTM C 39 (2001), Test for compressive strength of cylindrical concrete specimens. West Conshohocken, PA, USA: American Society for Testing and Materials International.
  5. Beutel, R., Reinhardt, H.W., Grosse, C.U., Glaubitt, A., Krause, M., Maierhofer, C., Algernon, D., Wiggenhauser, H. and Schickert, M. (2008), "Comparative performance tests and validation of NDT methods for concrete testing", J. Nondestruct. Eval., 27, 59-65. https://doi.org/10.1007/s10921-008-0037-1
  6. Bilgehan, M. and Turgut, P. (2010a), "The use of neural networks in concrete compressive strength estimation", Comput. Concr., 7(3), 271-283. https://doi.org/10.12989/cac.2010.7.3.271
  7. Bilgehan, M. and Turgut, P. (2010b), "Artificial neural network approach to predict compressive strength of concrete through ultrasonic pulse velocity", Res. Nondestruct. Eval., 21, 1-17. https://doi.org/10.1080/09349840903122042
  8. Demirboga, R., Turkmen, I. and Karakoc, M.B. (2004), "Relationship between ultrasonic velocity and compressive strength for high-volume mineral-admixtured concrete", Cement Concrete Res, 34, 2329-2336. https://doi.org/10.1016/j.cemconres.2004.04.017
  9. Ercikdi, B., Yilmaz, T. and Kulekci, G. (2014), "Strength and ultrasonic properties of cemented paste backfill", Ultrasonics, 54, 195-204. https://doi.org/10.1016/j.ultras.2013.04.013
  10. Hola, J. and Schabowicz, K. (2005), "New technique of nondestructive assessment of concrete strength using artificial intelligence", NDT&E Int., 38, 251-259. https://doi.org/10.1016/j.ndteint.2004.08.002
  11. Kewalramani, M.A. and Gupta, R. (2006), "Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks", Automat. Constr., 15, 374-379. https://doi.org/10.1016/j.autcon.2005.07.003
  12. Komlos, K., Popovics, S., Nurnbergerova, T., Babal, B. and Popovics, J.S. (1996), "Ultrasonic pulse velocity test of concrete properties as specified in national standards", Cement Concrete Compos, 18, 357-364. https://doi.org/10.1016/0958-9465(96)00026-1
  13. Liu, J.C., Sue, M.L. and Kou, C.H. (2009), "Estimating the strength of concrete using surface rebound value and design parameters of concrete material", Tamkang J. Sci. Eng., 12(1), 1-7.
  14. Mazloom, M. and Yoosefi, M.M. (2013), "Predicting the indirect tensile strength of self-compacting concrete using artificial neural networks", Comput. Concr., 12(3), 285-301. https://doi.org/10.12989/cac.2013.12.3.285
  15. Shariati, M., Sulong, H.R., Arabnejad, M.M.K.H., Shafigh, P. and Sinaei, H. (2011), "Assessing the strength of reinforced concrete structures through ultrasonic pulse velocity and schmidt rebound hammer tests", Sci. Res. Essays, 6(1), 213-220.
  16. Sheena, N.Y., Huangb, J.L. and Le, H.D. (2013), "Predicting strength development of RMSM using ultrasonic pulse velocity and artificial neural network", Comput. Concr., 12(6), 785-802. https://doi.org/10.12989/cac.2013.12.6.785
  17. Tanarslan, H.M., Secer, M. and Kumanlioglu, A. (2012), "An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks", Constr. Build. Mater., 30, 556-568. https://doi.org/10.1016/j.conbuildmat.2011.12.008
  18. Topcu, I.B. and Canbaz, M. (2007), "Effect of different fibers on the mechanical properties of concrete containing fly ash", Constr. Build. Mater., 21(7), 1486-1491. https://doi.org/10.1016/j.conbuildmat.2006.06.026
  19. Trtnik, G., Kavcic, F. and Turk, G. (2009), "Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks", Ultrasonics, 49, 53-60. https://doi.org/10.1016/j.ultras.2008.05.001
  20. Ulucan, Z.C ., Turk, K. and Karatas, M. (2008), "Effect of mineral admixtures on the correlation between ultrasonic velocity and compressive strength for self-compacting concrete", Russ. J. Nondestruct., 44(5), 367-374. https://doi.org/10.1134/S1061830908050100

Cited by

  1. Non-destructive assessment of the three-point-bending strength of mortar beams using radial basis function neural networks vol.16, pp.6, 2015, https://doi.org/10.12989/cac.2015.16.6.919
  2. Prediction of behavior of fresh concrete exposed to vibration using artificial neural networks and regression model vol.60, pp.4, 2016, https://doi.org/10.12989/sem.2016.60.4.655
  3. Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network 2018, https://doi.org/10.1007/s11709-017-0445-3
  4. Investigation on the Sensitivity of Ultrasonic Test Applied to Reinforced Concrete Beams Using Neural Network vol.8, pp.3, 2018, https://doi.org/10.3390/app8030405
  5. Study on vertical and batter piles subjected to lateral loads in different non-cohesive sub-soil conditions pp.1939-7879, 2020, https://doi.org/10.1080/19386362.2018.1564181
  6. Strength prediction of similar materials to ionic rare earth ores based on orthogonal test and back propagation neural network pp.1433-7479, 2019, https://doi.org/10.1007/s00500-019-03833-7
  7. Artificial neural network model using ultrasonic test results to predict compressive stress in concrete vol.19, pp.1, 2017, https://doi.org/10.12989/cac.2017.19.1.059
  8. Modeling of mechanical properties of roller compacted concrete containing RHA using ANFIS vol.19, pp.4, 2015, https://doi.org/10.12989/cac.2017.19.4.435
  9. Mechanical behavior of stud shear connectors embedded in HFRC vol.24, pp.2, 2015, https://doi.org/10.12989/scs.2017.24.2.177
  10. Evaluation of concrete compressive strength based on an improved PSO-LSSVM model vol.21, pp.5, 2015, https://doi.org/10.12989/cac.2018.21.5.505
  11. Neuro-fuzzy based approach for estimation of concrete compressive strength vol.21, pp.6, 2015, https://doi.org/10.12989/cac.2018.21.6.697
  12. Behavior of vertical and batter piles under lateral, uplift and combined loads in non-cohesive soil vol.4, pp.1, 2015, https://doi.org/10.1007/s41062-019-0242-z
  13. Displacement prediction of precast concrete under vibration using artificial neural networks vol.74, pp.4, 2015, https://doi.org/10.12989/sem.2020.74.4.559
  14. Compressive strength modeling of blended concrete based on empirical and artificial neural network techniques vol.5, pp.4, 2015, https://doi.org/10.1080/24705314.2020.1783120
  15. Nano-materials on the strength of ultra-high performance concrete vol.578, pp.1, 2015, https://doi.org/10.1080/00150193.2021.1902780
  16. Numerical assessment of a slender structure damaged during October 30, 2020, İzmir earthquake in Turkey vol.19, pp.14, 2021, https://doi.org/10.1007/s10518-021-01197-8