DOI QR코드

DOI QR Code

The use of neural networks in concrete compressive strength estimation

  • Bilgehan, M. (Harran University, Engineering Faculty, Civil Engineering Department) ;
  • Turgut, P. (Harran University, Engineering Faculty, Civil Engineering Department)
  • 투고 : 2009.11.14
  • 심사 : 2010.03.02
  • 발행 : 2010.06.25

초록

Testing of ultrasonic pulse velocity (UPV) is one of the most popular and actual non-destructive techniques used in the estimation of the concrete properties in structures. In this paper, artificial neural network (ANN) approach has been proposed for the evaluation of relationship between concrete compressive strength, UPV, and density values by using the experimental data obtained from many cores taken from different reinforced concrete structures with different ages and unknown ratios of concrete mixtures. The presented approach enables to find practically concrete strengths in the reinforced concrete structures, whose records of concrete mixture ratios are not yet available. Thus, researchers can easily evaluate the compressive strength of concrete specimens by using UPV values. The method can be used in conditions including too many numbers of the structures and examinations to be done in restricted time duration. This method also contributes to a remarkable reduction of the computational time without any significant loss of accuracy. Statistic measures are used to evaluate the performance of the models. The comparison of the results clearly shows that the ANN approach can be used effectively to predict the compressive strength of concrete by using UPV and density data. In addition, the model architecture can be used as a non-destructive procedure for health monitoring of structural elements.

키워드

참고문헌

  1. ACI 318-95 (1995), Building code requirements for structural concrete, (ACI 318-95) and commentary-ACI 318R-95, ACI, USA.
  2. Ashour, A.F. and Alqedra, M.A. (2005), "Concrete breakout strength of single anchors in tension using neural networks", Adv. Eng. Softw., 36, 87-97. https://doi.org/10.1016/j.advengsoft.2004.08.001
  3. ASTM C 42-90 (1992), Standard test method for obtaining and testing drilled cores and sawed beams of concrete, ASTM, USA.
  4. ASTM C 597-83 (1991), Test for pulse velocity through concrete, ASTM, USA.
  5. Bilgehan, M. and Turgut, P. (2010), "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
  6. BS 1881: Part 120 (1983), Method for determination of compressive strength of concrete cores, BSI, UK.
  7. BS 1881-203 (1986), Recommendations for measurement of velocity of ultrasonic pulses in concrete, BSI, UK.
  8. Bungey, J.H. and Millard S.G. (2004), Testing of concretes in structures, Taylor&Francis e-Library.
  9. Bungey, J.H. and Soutsos, M.N. (2001), "Reliability of partially-destructive tests to assess the strength of concrete on site", Constr. Build. Mater., 15, 81-92. https://doi.org/10.1016/S0950-0618(00)00057-X
  10. Castro, P.F. (2000), "Assessing concrete strength by Insitu Tests", 15th World Conference on Non Destructive Testing, Rome, Italy.
  11. Davis, S.G. (1977), "The effect of variations in the aggregate content of concrete columns upon the estimation of strength by the pulse-velocity method", Mag. Concrete Res., 29(98), 7-12. https://doi.org/10.1680/macr.1977.29.98.7
  12. Del Rio, L.M., Jimenez, A., Lopez, F., Rosa, F.J., Rufo, M.M. and Paniagua, J.M. (2004), "Characterization and hardening of concrete with ultrasonic testing", Ultrasonics, 42, 527-530. https://doi.org/10.1016/j.ultras.2004.01.053
  13. Ferreira, A.P. and Castro, P.F. (2000), "NDT for assessing concrete strength", Non Destructive Testing In Civil Engineering 2000-Seiken Symposium No. 26, April, 25-27, Tokyo, Japan.
  14. Flood, I. and Kartam, N. (1994a), "Neural network in civil engineering I: principles and understandings", J. Comput. Civil Eng. - ASCE, 8(2), 131-148. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(131)
  15. Flood, I. and Kartam, N. (1994b), "Neural network in civil engineering II: systems and applications", J. Comput. Civil Eng. - ASCE, 8(2), 149-162. https://doi.org/10.1061/(ASCE)0887-3801(1994)8:2(149)
  16. Hola, J. and Schabowicz, K. (2005a), "Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests", J. Civil Eng. Manage., 11(1), 23-32.
  17. Hola, J. and Schabowicz, K. (2005b), "New technique of nondestructive assessment of concrete strength using artificial intelligence", NDT&E Inter., 38(4), 251-259. https://doi.org/10.1016/j.ndteint.2004.08.002
  18. Kartam, N., Flood, I. and Garrett, J.H. (1997), Artificial neural networks for civil engineers: fundamentals and applications, ASCE, New York.
  19. 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
  20. Kisi, O. (2005), "Suspended sediment estimation using neuro-fuzzy and neural network approaches", Hydrolog. Sci. J., 50(4), 683-695.
  21. Leshchinsky A.M. (1991), "Non-destructive methods instead of specimens and cores", In: Quality Control of Concrete Structures, Proceedings of the International Symposium Held by RILEM, CEB, RUG, BBG/GBB and NFWO/FNRS and organized by the Magnel Laboratory for Reinforced Concrete, State University of Ghent, ed. by L. Taerwe and H. Lambotte, Belgium, June 12-14, 377-386.
  22. Lippman, R. (1987), "An introduction to computing with neural nets", IEEE ASSP Mag., 4, 4-22. https://doi.org/10.1109/MASSP.1987.1165575
  23. Malhotra, V.M. (1976), Testing hardened concrete: non-destructive methods, ACI, Monograph no. 9, Detroit, USA.
  24. MathWorks Inc. (1999), MatLab the Language of Technical Computing, Version 6, Natick, M.A., USA.
  25. Mehta, P.K. and Monteiro, P.J.M. (2006), Concrete microstructure, properties, and materials, McGraw-Hill, New York.
  26. Neville, A.M. (1995), Properties of concrete, Addison-Wesley Longman, Essex, UK.
  27. Nilsen, A. and Aitcin, P. (1992), "Static modulus of elasticity of high strength concrete from pulse velocity tests", Cement Concrete Aggr., 14(1), 64-66. https://doi.org/10.1520/CCA10577J
  28. Ohdaira, E. and Masuzawa, N. (2000), "Water content and its effect on ultrasound propagation in concrete-the possibility of NDE", Ultrasonics, 38, 546-552. https://doi.org/10.1016/S0041-624X(99)00158-4
  29. Philleo, R. (1995), "Comparison of results of three methods for determining Young's modulus of elasticity of concrete", J. Am. Concr. Inst., 51, 461-469.
  30. Popovics, S. (1998), Strength and related properties of concrete: a quantitative approach, New York, John Wiley Sons Inc.
  31. Popovics, S. (2001), "Analysis of the concrete strength versus ultrasonic pulse velocity relationship", Mater. Eval., 59(2), 123-130.
  32. Popovics, S. and Popovics, J.S. (1992), "A critique of the ultrasonic pulse velocity method for testing concrete", In: Ansari, F. and Sture, S. (Eds.), Nondestructive Testing of Concrete Elements and Structures, Symp. on Nondestructive Testing of Concrete Elements and Structures/Structures Congress, Engineering Division of ASCE, April 13-15, San Antonio, Texas, 94-103.
  33. Rafiq, M.Y., Bugmann, G. and Easterbrook D.J. (2001), "Neural network design for engineering application", Comput. Struct., 79, 1541-1552. https://doi.org/10.1016/S0045-7949(01)00039-6
  34. Rajagopalan, P.R., Prakash, J. and Naramimhan, V. (1973), "Correlation between ultrasonic pulse velocity and strength of concrete", Indian Concrete J., 47(11), 416-418.
  35. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986), "Learning internal representation by error propagation", Inf: Parallel Distributed Processing, ed. by Rumelhart, D.E. and McClelland, J.L., vol. 1: Foundations, MIT Press, Cambridge, Massachusetts, USA.
  36. Sharma, M. and Gupta, B. (1960), "Sonic modulus as related to strength and static modulus of high strength concrete", Indian Concrete. J., 34(4), 139-141.
  37. Tapkn, S., Cevik, A. and Uar, U. (2010), "Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks", Expert Systems with Applications, doi:10.1016/j.eswa. 2009. 12.042.
  38. Tapkin, S., Tuncan, M., Arioz, O., Tuncan, A. and Ramyar, K. (2006), "Estimation of concrete compressive strength by using ultrasonic pulse velocities and artificial neural networks", Conference for Computer Aided Engineering and System Modeling, 11th FIGES User's Conference, Turkey.
  39. Tharmaratnam, K. and Tan, B.S. (1990), "Attenuation of ultrasonic pulse in cement mortar", Cement Concrete Res., 20, 335-345. https://doi.org/10.1016/0008-8846(90)90022-P
  40. Todd, C.P.D. and Challis, R.E. (1999), "Quantitative classification of adhesive bondlines using Lamb waves and artificial neural networks", IEEE Trans. UFFC, 46(1), 167-181. https://doi.org/10.1109/58.741528
  41. Trtnik, G., Franci, K. 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
  42. Turgut, P. (2004), "Research into the correlation between concrete strength and UPV values", J. Nondestruct. Test., 12(12).
  43. Whitehurst, E. (1951), "Soniscope tests concrete structures", J. Am. Concrete Inst., 47, 433-444.
  44. Yaman, I.O., Akbay, Z. and Aktan, H. (2006), "Numerical modelling and finite element analysis of stress wave propagation for ultrasonic pulse velocity testing of concrete", Comput. Concrete, 3(6), 423-437. https://doi.org/10.12989/cac.2006.3.6.423

피인용 문헌

  1. Artificial neural network for predicting drying shrinkage of concrete vol.38, 2013, https://doi.org/10.1016/j.conbuildmat.2012.08.043
  2. Comparison of ANFIS and NN models—With a study in critical buckling load estimation vol.11, pp.4, 2011, https://doi.org/10.1016/j.asoc.2011.02.011
  3. Prediction of the transfer length of prestressing strands with neural networks vol.12, pp.2, 2013, https://doi.org/10.12989/cac.2013.12.2.187
  4. Model for mix design of brick aggregate concrete based on neural network modelling vol.148, 2017, https://doi.org/10.1016/j.conbuildmat.2017.05.111
  5. Probabilistic Modelling of Compressive Strength of Concrete Using Response Surface Methodology and Neural Networks vol.39, pp.6, 2014, https://doi.org/10.1007/s13369-014-1139-y
  6. Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete vol.25, pp.8, 2011, https://doi.org/10.1016/j.conbuildmat.2011.03.040
  7. Predictive modeling of concrete compressive strength based on cement strength class vol.11, pp.6, 2013, https://doi.org/10.12989/cac.2013.11.6.587
  8. Prediction of Hybrid fibre-added concrete strength using artificial neural networks vol.15, pp.4, 2015, https://doi.org/10.12989/cac.2015.15.4.503
  9. Estimation of RC slab-column joints effective strength using neural networks vol.8, pp.4, 2011, https://doi.org/10.1590/S1679-78252011000400002
  10. Predicting strength development of RMSM using ultrasonic pulse velocity and artificial neural network vol.12, pp.6, 2013, https://doi.org/10.12989/cac.2013.12.6.785
  11. Prediction of compressive strength of concrete using neural networks vol.10, pp.2, 2012, https://doi.org/10.12989/cac.2012.10.2.197
  12. Combining non-invasive techniques for reliable prediction of soft stone strength in historic masonries vol.146, 2017, https://doi.org/10.1016/j.conbuildmat.2017.04.146
  13. Artificial neural network for predicting creep of concrete vol.25, pp.6, 2014, https://doi.org/10.1007/s00521-014-1623-z
  14. Application of artificial neural networks (ANNs) and linear regressions (LR) to predict the deflection of concrete deep beams vol.11, pp.3, 2013, https://doi.org/10.12989/cac.2013.11.3.237
  15. A comparative modeling study to estimate wear of concrete vol.24, pp.3-4, 2014, https://doi.org/10.1007/s00521-012-1277-7
  16. Prediction of compressive strength of self-compacting concrete by ANFIS models vol.280, 2018, https://doi.org/10.1016/j.neucom.2017.09.099
  17. Buckling load estimation of cracked columns using artificial neural network modeling technique vol.18, pp.4, 2012, https://doi.org/10.3846/13923730.2012.702988
  18. ANFIS-based prediction of moment capacity of reinforced concrete slabs exposed to fire vol.27, pp.4, 2016, https://doi.org/10.1007/s00521-015-1902-3
  19. Indirect estimation of compressive and shear strength from simple index tests vol.33, pp.1, 2017, https://doi.org/10.1007/s00366-016-0451-4
  20. Modeling of Thermal Conductivity of Concrete with Vermiculite by Using Artificial Neural Networks Approaches vol.26, pp.4, 2013, https://doi.org/10.1080/08916152.2012.669810
  21. Polynomial modeling of confined compressive strength and strain of circular concrete columns vol.11, pp.6, 2013, https://doi.org/10.12989/cac.2013.11.6.603
  22. Factors affecting the properties of recycled concrete by using neural networks vol.14, pp.5, 2014, https://doi.org/10.12989/cac.2014.14.5.547
  23. Proposing New Methods to Estimate the Safety Level in Different Parts of Freeway Interchanges vol.2018, pp.1687-8094, 2018, https://doi.org/10.1155/2018/8702854
  24. 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
  25. Influence of steel reinforcement on ultrasonic pulse velocity as a non-destructive evaluation of high-performance concrete strength pp.2116-7214, 2021, https://doi.org/10.1080/19648189.2018.1528890
  26. A graphical method for assessing the concrete strength class in existing RC structures vol.51, pp.3, 2018, https://doi.org/10.1617/s11527-018-1200-5
  27. 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
  28. Bond strength prediction of steel bars in low strength concrete by using ANN vol.22, pp.2, 2018, https://doi.org/10.12989/cac.2018.22.2.249
  29. Concrete compressive strength prediction using the imperialist competitive algorithm vol.22, pp.4, 2010, https://doi.org/10.12989/cac.2018.22.4.355
  30. An Artificial Intelligence Model for Computing Optimum Fly Ash Content for Structural-Grade Concrete vol.8, pp.1, 2010, https://doi.org/10.1520/acem20180079
  31. Knowledge-based learning for modeling concrete compressive strength using genetic programming vol.23, pp.4, 2010, https://doi.org/10.12989/cac.2019.23.4.255
  32. Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars vol.24, pp.4, 2010, https://doi.org/10.12989/cac.2019.24.4.329
  33. Actuating and sensing mechanism of embedded piezoelectric transducers in concrete vol.29, pp.8, 2020, https://doi.org/10.1088/1361-665x/ab9146
  34. Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey vol.31, pp.1, 2010, https://doi.org/10.1080/0954898x.2020.1759833
  35. Evaluation of concrete made with stone and brick aggregate using non-destructive testing vol.174, pp.1, 2010, https://doi.org/10.1680/jmuen.18.00030
  36. CompoNet with SFEL: A convolutional neural network for identifying low-emissivity coating damage vol.11, pp.5, 2021, https://doi.org/10.1063/5.0052154
  37. Artificial intelligence for the compressive strength prediction of novel ductile geopolymer composites vol.28, pp.1, 2010, https://doi.org/10.12989/cac.2021.28.1.055
  38. Emissivity measurement based on deep learning and surface roughness vol.11, pp.8, 2021, https://doi.org/10.1063/5.0055415
  39. Mechanical strength, mass loss and volumetric changes of drying adobe matrices combined with kaolin and fine soil particles vol.312, pp.None, 2021, https://doi.org/10.1016/j.conbuildmat.2021.125246