DOI QR코드

DOI QR Code

Artificial neural network model using ultrasonic test results to predict compressive stress in concrete

  • Ongpeng, Jason (Department of Civil Engineering, De La Salle University) ;
  • Soberano, Marcus (Department of Civil Engineering, De La Salle University) ;
  • Oreta, Andres (Department of Civil Engineering, De La Salle University) ;
  • Hirose, Sohichi (Department of Civil Engineering, Tokyo Institute of Technology)
  • 투고 : 2016.06.17
  • 심사 : 2016.10.19
  • 발행 : 2017.01.25

초록

This study focused on modeling the behavior of the compressive stress using the average strain and ultrasonic test results in concrete. Feed-forward backpropagation artificial neural network (ANN) models were used to compare four types of concrete mixtures with varying water cement ratio (WC), ordinary concrete (ORC) and concrete with short steel fiber-reinforcement (FRC). Sixteen (16) $150mm{\times}150mm{\times}150mm$ concrete cubes were used; each contained eighteen (18) data sets. Ultrasonic test with pitch-catch configuration was conducted at each loading state to record linear and nonlinear test response with multiple step loads. Statistical Spearman's rank correlation was used to reduce the input parameters. Different types of concrete produced similar top five input parameters that had high correlation to compressive stress: average strain (${\varepsilon}$), fundamental harmonic amplitude (A1), $2^{nd}$ harmonic amplitude (A2), $3^{rd}$ harmonic amplitude (A3), and peak to peak amplitude (PPA). Twenty-eight ANN models were trained, validated and tested. A model was chosen for each WC with the highest Pearson correlation coefficient (R) in testing, and the soundness of the behavior for the input parameters in relation to the compressive stress. The ANN model showed increasing WC produced delayed response to stress at initial stages, abruptly responding after 40%. This was due to the presence of more voids for high water cement ratio that activated Contact Acoustic Nonlinearity (CAN) at the latter stage of the loading path. FRC showed slow response to stress than ORC, indicating the resistance of short steel fiber that delayed stress increase against the loading path.

키워드

참고문헌

  1. Alexandridis, A., Chondrodima, E., Giannopoulos, N. and Sarimveis, H. (2016), A Fast and Efficient Method for Training Categorical Radial Basis Function Networks, IEEE Trans. Neural Networks Learn. Sys., In Press.
  2. Alexandridis, A., Stravrakas, I., Stergiopoulos, C., Hloupis, G., Ninos, K. and Triantis, D. (2015), "Non-destructive assessment of the three-point-bending strength of mortar beams using radial basis function neural networks", Comput. Concrete, 16(6), 919-932. https://doi.org/10.12989/cac.2015.16.6.919
  3. Atici, U. (2011), "Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network", Expert Sys. Appl., 38(8), 9609-9618. https://doi.org/10.1016/j.eswa.2011.01.156
  4. Bilgehan, M. and Turgut, P. (2010), "The use of artificial neural network in concrete compressive strength estimation", Comput. Concrete, 7(3), 271-283. https://doi.org/10.12989/cac.2010.7.3.271
  5. Breysse, D. (2012), "Nondestructive evaluation of concrete strength: An historical review and a new perspective by combining NDT methods", Constr. Build. Mater., 33, 139-163. https://doi.org/10.1016/j.conbuildmat.2011.12.103
  6. Daponte, P., Maceri, F. and Olivito, R.S. (1995), "Ultrasonic signal-processing techniques for the measurement of damage growth in structural materials", IEEE Trans. Instrument. Measure., 44(6), 1003-1008. https://doi.org/10.1109/19.475146
  7. Demir, A. (2015), "Prediction of hybrid fibre-added concrete strength using artifical neural networks", Comput. Concrete, 15(4), 503-514. https://doi.org/10.12989/cac.2015.15.4.503
  8. Grippo, L., Manno, A. and Sciandrone, M. (2016), Decomposition Techniques for Multilayer Perceptron Training, IEEE Trans. Neural Networks Learn. Sys., In Press.
  9. Hecht-Nielsen, R. (1998), "A theory of the cerebral cortex", ICONIP, 1459-1464.
  10. Hirose, S. and Achenbach, J.D. (1993), "Higher harmonics in the far field due to dynamic crack-face contacting", J. Acoust. Soc. Am., 93(1), 142-147. https://doi.org/10.1121/1.405651
  11. Johnson, P.A. (2006), Nonequilibrium Nonlinear-dynamics in Solids: State of the Art, Universality of Nonclassical Nonlinearity, 49-69, Springer, New York, U.S.A.
  12. Johnson, P. and Sutin, A. (2005), "Slow dynamics in diverse solids", J. Acoust. Soc. Am., 117(1), 24-130.
  13. Komlos, K., Popovics, S., Nurnbergerova, T., Babal, B. and Popovics, J.S. (1996), "Ultrasonic pulse velocity test of concrete properties as specified in various standards", Cement Concrete Compos., 18(5), 357-364. https://doi.org/10.1016/0958-9465(96)00026-1
  14. Korshak, B.A., Solodov, I.Y. and Ballad, E.M. (2002), "DC effects, sub-harmonics, stochasticity and "memory" for contact acoustic nonlinearity", Ultras., 40(1), 707-713. https://doi.org/10.1016/S0041-624X(02)00241-X
  15. Kumar, N. and Anamika, Y. (2016), "Solar resource estimation based on correlation matrix response for Indian geographical cities", J. Renew. Energy Res., 6(2), 695-701.
  16. Liang, M.T. and Wu, J. (2002), "Theoretical elucidation on the empirical formulae for the ultrasonic testing method for concrete structures", Cement Concrete Res., 32(11), 1763-1769. https://doi.org/10.1016/S0008-8846(02)00866-9
  17. Martin, O., Lopez, M. and Martin, F. (2007), "Artificial neural network for quality control by ultrasonic testing in resistance spot welding", J. Mater. Process. Technol., 183(2), 226-233. https://doi.org/10.1016/j.jmatprotec.2006.10.011
  18. Oh, P.E.T., Kee, S.H., Arndt, R.W., Popovics, J.S. and Zhu, J. (2013), "Comparison of NDT methods for assessment of a concrete bridge deck", J. Eng. Mech., 139(3), 305-314. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000441
  19. Ongpeng, J.M.C., Oreta, A.W.C. and Hirose, S. (2016a), "Effect of load pattern in the generation of higher harmonic amplitude in concrete using nonlinear ultrasonic test", J. Adv. Concrete Technol., 14(5), 205-214. https://doi.org/10.3151/jact.14.205
  20. Ongpeng, J.M.C., Oreta, A.W.C., Hirose, S. and Nakahata, K. (2016b), "Nonlinear ultrasonic investigation of concrete with varying aggregate size under uniaxial compression loading and unloading", J. Mater. Civil Eng., 04016210.
  21. Oreta, A. and Ongpeng, J. (2011), "Modeling the confined compressive strength of hybrid circular concrete columns using neural networks", Comput. Concrete, 8(5), 597-616. https://doi.org/10.12989/cac.2011.8.5.597
  22. Oreta, A.W.C. and Kawashima, K. (2003), "Neural network modeling of confined compressive strength and strain of circular concrete columns", J. Struct. Eng., 129(4), 554-561. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:4(554)
  23. Osman, N.Y., Ng, A.M. and McManus, K.J. (2006), "Selection of important input parameters using neural network trained with genetic algorithm for damage to light structures", Proceedings of the Fifth International Conference on Engineering Computational Technology: Las Palmas de Gran Canaria, Spain, September.
  24. Rossello, J.L., Canals, V., Oliver, A. and Morro, A. (2014), "Studying the role of synchronized and chaotic spiking neural ensembles in neural information processing", J. Neural Syst., 24(5), 1430003. https://doi.org/10.1142/S0129065714300034
  25. Shah, A.A. and Hirose, S. (2010a), "Nonlinear ultrasonic investigation of concrete damaged under uniaxial compression step loading", J. Mater. Civil Eng., 22(5), 476-484. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000050
  26. Shah, A.A, Alsayed, S., Abbas, H. and Al-Salloum, Y. (2012), "Predicting residual strength of non-linear ultrasonically evaluated damaged concrete using artificial neural network", Constr. Build. Mater., 20, 42-50.
  27. Shah, A.A. and Ribakov, Y. (2008), "Non-linear non-destructive evaluation of concrete", Constr. Build. Technol. J., 2, 111-115. https://doi.org/10.2174/1874836800802010111
  28. Shah, A.A. and Ribakov, Y. (2009), "Non-linear ultrasonic evaluation of damaged concrete based on higher order harmonic generation", Mater. Des., 30(10), 4095-4102. https://doi.org/10.1016/j.matdes.2009.05.009
  29. Shah, A.A. and Ribakov, Y. (2010b), "Effectiveness of nonlinear ultrasonic and acoustic emission evaluation of concrete with distributed damages", Mater. Des., 31(8), 3777-3784. https://doi.org/10.1016/j.matdes.2010.03.020
  30. Shah, A.A., Ribakov, Y. and Zhang, C. (2013), "Efficiency and sensitivity of linear and non-linear ultrasonics to identifying micro-and macro-scale defects in concrete", Mater. Des., 50, 905-916. https://doi.org/10.1016/j.matdes.2013.03.079
  31. Shah, A.A., Ribakov, Y. and Hirose, S. (2009), "Nondestructive evaluation of damaged concrete using non-linear ultrasonics", Mater. Des., 30(3), 775-782. https://doi.org/10.1016/j.matdes.2008.05.069
  32. Solodov, I.Y. and Chin, A.W. (1993), "Popping nonlinearity and chaos in vibrations of contact interface between solids", Acoust. Phys., 39(5), 476-479.
  33. Solodov, I.Y., Krohn, N. and Busse, G. (2002), "CAN: An example of nonclassical acoustic nonlinearity in solids", Ultras., 40(1), 621-625. https://doi.org/10.1016/S0041-624X(02)00186-5
  34. Solodov, I.Y. (1998), "Ultrasonics of nonlinear contacts: propagations, reflection and NDE-applications", Ultras., 36(1), 383-390. https://doi.org/10.1016/S0041-624X(97)00041-3
  35. Solodov, I.Y., Doring, D. and Busse, G. (2011), "New opportunities for NDT using non-linear interaction of elastic waves with defects", J. Mech. Eng., 57(3), 169-182.
  36. Ursino, M., Cuppini, C. and Magosso, E. (2015), "A neural network for learning the meaning of objects and words from a featural representation", Neur. Network., 63, 234-253. https://doi.org/10.1016/j.neunet.2014.11.009
  37. Van Den Abeele, K.E.A., Johnson, P.A. and Sutin, A. (2000), "Nonlinear elastic wave spectroscopy (NEWS) technique to discern material damage, Part I: Nonlinear wave modulation spectroscopy (NWMS)", Res. Nondestr. Eval., 12, 17-30. https://doi.org/10.1080/09349840009409646
  38. Yim, H.J., Kim, J.H., Park, S.J., Kwak, H.G. (2012), "Characterization of thermally damaged concrete using nonlinear ultrasonic method", Cement Concrete Res., 42(11), 1438-1446. https://doi.org/10.1016/j.cemconres.2012.08.006
  39. Zheng, Y., Maev, R.G. and Solodov, I.Y. (1999), "Nonlinear acoustic applications for material characterization: A review", Can. J. Phys., 77(12), 927-967. https://doi.org/10.1139/cjp-77-12-927

피인용 문헌

  1. Feed-Forward Neural Network Prediction of the Mechanical Properties of Sandcrete Materials vol.17, pp.6, 2017, https://doi.org/10.3390/s17061344
  2. Monitoring Damage Using Acoustic Emission Source Location and Computational Geometry in Reinforced Concrete Beams vol.8, pp.2, 2018, https://doi.org/10.3390/app8020189
  3. 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
  4. Contact and Noncontact Ultrasonic Nondestructive Test in Reinforced Concrete Beam vol.2018, pp.1687-8094, 2018, https://doi.org/10.1155/2018/5783175
  5. Guided wave analysis of air-coupled impact-echo in concrete slab vol.20, pp.3, 2017, https://doi.org/10.12989/cac.2017.20.3.257
  6. Ultrasonic velocity as a tool for mechanical and physical parameters prediction within carbonate rocks vol.13, pp.3, 2017, https://doi.org/10.12989/gae.2017.13.3.371
  7. Estimation of Wave Velocity for Ultrasonic Imaging of Concrete Structures Based on Dispersion Analysis vol.48, pp.2, 2017, https://doi.org/10.1520/jte20180343
  8. Knowledge-based learning for modeling concrete compressive strength using genetic programming vol.23, pp.4, 2017, https://doi.org/10.12989/cac.2019.23.4.255
  9. Evaluation of the Adhesion between Overlays and Substrates in Concrete Floors: Literature Survey, Recent Non-Destructive and Semi-Destructive Testing Methods, and Research Gaps vol.9, pp.9, 2019, https://doi.org/10.3390/buildings9090203
  10. Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars vol.24, pp.4, 2017, https://doi.org/10.12989/cac.2019.24.4.329
  11. Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network vol.24, pp.6, 2017, https://doi.org/10.12989/cac.2019.24.6.541
  12. Artificial neural network calculations for a receding contact problem vol.25, pp.6, 2017, https://doi.org/10.12989/cac.2020.25.6.551
  13. Assessing the load carrying capacity of concrete anchor bolts using non-destructive tests and artificial multilayer neural network vol.30, pp.None, 2020, https://doi.org/10.1016/j.jobe.2020.101260
  14. Investigation of the effects of corrosion on bond strength of steel in concrete using neural network vol.28, pp.1, 2021, https://doi.org/10.12989/cac.2021.28.1.077
  15. Using artificial neural network and non‐destructive test for crack detection in concrete surrounding the embedded steel reinforcement vol.22, pp.5, 2017, https://doi.org/10.1002/suco.202000767
  16. Correlation analysis and statistical assessment of early hydration characteristics and compressive strength for multi-composite cement paste vol.310, pp.None, 2021, https://doi.org/10.1016/j.conbuildmat.2021.125260
  17. Development and testing of cellulose nanocrystal-based concrete vol.15, pp.None, 2017, https://doi.org/10.1016/j.cscm.2021.e00761