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Nano-delamination monitoring of BFRP nano-pipes of electrical potential change with ANNs

  • Altabey, Wael A. (International Institute for Urban Systems Engineering, Southeast University) ;
  • Noori, Mohammad (International Institute for Urban Systems Engineering, Southeast University) ;
  • Alarjani, Ali (Department of Mechanical Engineering, College of Engineering in Alkharj, Prince Sattam Bin Abdelaziz University) ;
  • Zhao, Ying (International Institute for Urban Systems Engineering, Southeast University)
  • Received : 2018.07.09
  • Accepted : 2019.12.24
  • Published : 2020.07.25
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Abstract

In this work, the electrical potential (EP) technique with an artificial neural networks (ANNs) for monitoring of nanostructures are used for the first time. This study employs an expert system to identify size and localize hidden nano-delamination (N.Del) inside layers of nano-pipe (N.P) manufactured from Basalt Fiber Reinforced Polymer (BFRP) laminate composite by using low-cost monitoring method of electrical potential (EP) technique with an artificial neural networks (ANNs), which are combined to decrease detection effort to discern N.Del location/size inside the N.P layers, with high accuracy, simple and low-cost. The dielectric properties of the N.P material are measured before and after N.Del introduced using arrays of electrical contacts and the variation in capacitance values, capacitance change and node potential distribution are analyzed. Using these changes in electrical potential due to N.Del, a finite element (FE) simulation model for N.Del location/size detection is generated by ANSYS and MATLAB, which are combined to simulate sensor characteristic, therefore, FE analyses are employed to make sets of data for the learning of the ANNs. The method is applied for the N.Del monitoring, to minimize the number of FE analysis in order to keep the cost and save the time of the assessment to a minimum. The FE results are in excellent agreement with an ANN and the experimental results available in the literature, thus validating the accuracy and reliability of the proposed technique.

Keywords

References

  1. Al-Tabey, W.A. (2010), "Effect of pipeline filling material on electrical capacitance tomography", Proceedings of the International Postgraduate Conference on Engineering (IPCE 2010), Perlis, Malaysia, October.
  2. Al-Tabey, W.A. (2012), Finite Element Analysis in Mechanical Design Using ANSYS: Finite Element Analysis (FEA) HandBook For Mechanical Engineers With ANSYS Tutorials, LAP Lambert Academic Publishing, Germany (ISBN 978-3-8454-0479-0)
  3. Al-tabey, W.A. (2014), "Vibration analysis of laminated composite variable thickness plate using finite strip transition matrix technique", In: MATLAB Verifications MATLAB-Particular for Engineer (Kelly, B., Ed.), InTech, USA, Volume 21, pp. 583-620. (ISBN 980-953-307-1128-8) https://doi.org/10.5772/57384
  4. Altabey, W.A. (2016a), "Detecting and predicting the crude oil type inside composite pipes using ECS and ANN", Struct. Monit. Maint., Int. J., 3(4), 377-393. http://dx.doi.org/10.12989/smm.2016.3.4.377
  5. Altabey W.A. (2016b), "FE and ANN model of ECS to simulate the pipelines suffer from internal corrosion", Struct. Monit. Maint., 3(3), 297-314. http://dx.doi.org/10.12989/smm.2016.3.3.297
  6. Altabey, W.A. (2016c), "The thermal effect on electrical capacitance sensor for two-phase flow monitoring", Struct. Monit. Maint., Int. J., 3(4), 335-347. http://dx.doi.org/10.12989/smm.2016.3.4.335
  7. Altabey, W.A. (2017a), "An exact solution for mechanical behavior of BFRP Nano-thin films embedded in NEMS", J. Adv. Nano Res., Int. J., 5(4), 337-357. https://doi.org/10.12989/anr.2017.5.4.337
  8. Altabey, W.A. (2017b), "A study on thermo-mechanical behavior of MCD through bulge test analysis", Adv. Computat. Des., Int. J., 2(2), 107-119. https://doi.org/10.12989/acd.2017.2.2.107
  9. Altabey, W.A. (2017c), "Free vibration of basalt fiber reinforced polymer (FRP) laminated variable thickness plates with intermediate elastic support using finite strip transition matrix (FSTM) method", J. Vibroeng., 19(4), 2873-2885. https://doi.org/10.21595/jve.2017.18154
  10. Altabey, W.A. (2017d), "Prediction of natural frequency of basalt fiber reinforced polymer (FRP) laminated variable thickness plates with intermediate elastic support using artificial neural networks (ANNs) method", J. Vibroeng., 19(5), 3668-3678. https://doi.org/10.21595/jve.2017.18209
  11. Altabey, W.A. (2017e), "Delamination evaluation on basalt FRP composite pipe of electrical potential change", Adv. Aircr. Spacecr. Sci., Int. J., 4(5), 515-528. http://dx.doi.org/10.12989/aas.2017.4.5.515
  12. Altabey, W.A. (2017f), "EPC method for delamination assessment of basalt FRP pipe: Electrodes number effect", Struct. Monit. Maint., Int. J., 4(1), 69-84. https://doi.org/10.12989/smm.2017.4.1.069
  13. Altabey, W.A. (2018), "High performance estimations of natural frequency of basalt FRP laminated plates with intermediate elastic support using response surfaces method", J. Vibroeng., 20(2), 1099-1107. https://doi.org/10.21595/jve.2017.18456
  14. Altabey, W.A. and Noori, M. (2017), "Detection of fatigue crack in basalt FRP laminate composite pipe using electrical potential change method", Proceedings of the 12th International Conference on Damage Assessment of Structures, IOP Conf. Series: Journal of Physics, 842, 012079, Kitakyushu, Japan, July.
  15. Altabey, W.A. and Noori, M. (2018a), "Fatigue life prediction for carbon fibre/epoxy laminate composites under spectrum loading using two different neural network architectures", Int. J. Sustain. Mater. Struct. Syst., 3(1), 53-78. http://dx.doi.org/10.1504/IJSMSS.2017.10013394
  16. Altabey, W.A. and Noori, M. (2018b), "Monitoring the water absorption in GFRE pipes via an electrical capacitance sensors", Adv. Aircr. Spacecr. Sci., Int. J., 5(4), 411-434. https://doi.org/10.12989/aas.2018.5.4.411
  17. Altabey, W.A., Noori, M. and Wang, L. (2018a), Using ANSYS for Finite Element Analysis: A Tutorial for Engineers, Volume I, Momentum Press, USA (ISBN 978-1-94708-321-9).
  18. Altabey, W.A., Noori, M. and Wang, L. (2018b), Using ANSYS for Finite Element Analysis: Dynamic, Probabilistic, Design and Heat, Transfer Analysis, Volume II, Momentum Press, USA (ISBN 978-1-94708-323-3)
  19. Altabey, W.A., Noori, M., Zhao, Y. and Wu, Z. (2019), "Tensile creep monitoring of BFRP plates via electrical potential change and ANNs", Sci. Iran. Trans B: Mech. Eng. [In press].
  20. ANSYS Low-Frequency Electromagnetic analysis Guide, The Electrostatic Module in the Electromagnetic subsection of ANSYS (2015), ANSYS, inc. Southpointe 275 Technology Drive Canonsburg, PA 15317, USA.
  21. Asencio, K., Bramer-Escamilla, W., Gutierrez, G. and Sanchez, I. (2015), "Electrical capacitance sensor array to measure density profiles of a vibrated granular bed", J. Powder Technol., 270, 10-19. https://doi.org/10.1016/j.powtec.2014.10.003
  22. Buhmann, M.D. (2003), Radial Basis Functions: Theory and Implementations, Cambridge University Press, Cambridge, UK.
  23. Daoye, Y., Bin, Z., Chuanlong, X., Guanghua, T. and Shimin, W. (2009), "Effect of pipeline thickness on electrical capacitance tomography", Proceedings of the 6th International Symposium on Measurement Techniques for Multiphase Flows, Journal of Physics: Conference Series, 147, 1-13, Okinawa, Japan, December.
  24. Fasching, G.E. and Smith, N.S. (1988), "High resolution capacitance imaging system", US Dept. Energy, 37, DOE/METC-88/4083.
  25. Fasching, G.E. and Smith, N.S. (1991), "A capacitive system for 3-dimensional imaging of fluidized-beds", Rev. Sci. Instr., 62(9), 2243-2251. https://doi.org/10.1063/1.1142343
  26. Ghiasi, R., Ghasemi, M.R., Noori, M. and Altabey, W. (2019), "A non-parametric approach toward structural health monitoring for processing big data collected from the sensor network", Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT) - Proceedings of the 12th International Workshop on Structural Health Monitoring (IWSHM 2019), Stanford, CA, USA, September.
  27. Huang, S.M., Plaskowski, A.B., Xie, C.G. and Beck, M.S. (1989), "Tomographic imaging of two-flow component flow using capacitance sensor", J. Phys. E: Sci. Instrum., 22,173-177. https://doi.org/10.1088/0022-3735/22/3/009
  28. Jaworski, A.J. and Bolton, G.T. (2000), "The design of an electrical capacitance tomography sensor for use with media of high dielectric permittivity", Meas. Sci. Technol., 11(6), 743-757. https://doi.org/10.1088/0957-0233/11/6/318
  29. Jiang, S., Li, D., Zhou, C. and Zhang, L. (2014), "Capabilities of stochastic response surface method and response surface method in reliability analysis", Struct. Eng. Mech., Int. J., 49(1), 111-128. http://dx.doi.org/10.12989/sem.2014.49.1.111
  30. Kost, A., Altabey W.A., Noori, M. and Awad, T. (2019), "Applying neural networks for tire pressure monitoring systems", Struct. Durab. Health Monit., 13(3), 247-266. http://dx.doi.org/10.32604/sdhm.2019.07025
  31. Li, H. and Huang, Z. (2000), Special Measurement Technology and Application, Zhejiang University Press, Hangzhou, China.
  32. Mohamad, E.J., Rahim, R.A., Leow, P.L., Rahiman, M.H.F., Marwah, O.M.F., Nor Ayob, N.M., Rahim, H.A. and Mohd Yunus, F.R. (2012), "An introduction of two differential excitation potentials technique in electrical capacitance tomography", Sensor. Actuat. A, 180, 1-10. https://doi.org/10.1016/j.sna.2012.03.025
  33. Mohamad, E.J., Rahim, R.A., Rahiman, M.H.F., Ameran, H.L.M., Muji, S.Z.M. and Marwah, O.M.F. (2016), "Measurement and analysis of water/oil multiphase flow using electrical capacitance tomography sensor", J. Flow Meas. Instrum., 47, 62-70. https://doi.org/10.1016/j.flowmeasinst.2015.12.004
  34. Mouritz, A.P. (2003), "Non-destructive evaluation of damage accumulation", Chapter 8 - Fatigue in Composites, (B. Harris), A volume in Woodhead Publishing Series in Composites Science and Engineering. (ISBN: 978-185573-608-5)
  35. Myers, R. and Montgomery, D.C. (2002), Response surface methodology process and product optimization using designed experiments, (2nd Ed.), Wiley-Interscience, New York, USA
  36. Noori, M., Wang, H., Altabey, W.A. and Silik, A.I.H. (2018), "A modified wavelet energy rate-based damage identification method for steel bridges", Sci. Iran. Trans B: Mech. Eng., 25(6), 3210-3230. https://doi.org/10.24200/sci.2018.20736
  37. Pei, T. and Wang, W. (2009), "Simulation analysis of sensitivity for electrical capacitance tomography", Proceedings of 9th International Conference on Electronic Measurement & Instruments (ICEMI 2009), Beijing, China, August.
  38. Sardeshpande, M.V., Harinarayan, S. and Ranade, V.V. (2015), "Void fraction measurement using electrical capacitance tomography and high speed photography", J. Chem. Eng. Res. Des., 9(4), 1-11. https://doi.org/10.1016/j.cherd.2014.11.013
  39. Todoroki, A., Tanaka, Y. and Shimamura, Y. (2004), "Multi-prove electric potential change method for delamination monitoring of graphite/epoxy composite plates using normalized response surfaces", Compos. Sci. Technol., 64(5), 749-758. https://doi.org/10.1016/j.compscitech.2003.08.004
  40. Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J. and Harley, R.G. (2008). "Particle swarm optimization: basic concepts, variants and applications in power dystems", IEEE Trans. Evol. Comput., 12(2), 171-195. https://doi.org/10.1109/TEVC.2007.896686
  41. Yang, W.Q., Stott, A.L., Beck, M.S. and Xie, C.G. (1995a), "Development of capacitance tomographic imaging systems for oil pipeline measurements", Rev. Sci. Instrum., 66(8), 4326. https://doi.org/10.1063/1.1145322
  42. Yang, W.Q., Beck, M.S. and Byars, M. (1995b), "Electrical capacitance tomography-from design to applications", Meas. Control, 28(9), 261-266. https://doi.org/10.1177/002029409502800901
  43. Zhang, W., Wang, C., Yang, W. and Wang, C. (2014), "Application of electrical capacitance tomography in particulate process measurement - A review", Adv. Powder Technol., 25(1), 174-188. https://doi.org/10.1016/j.apt.2013.12.003
  44. Zhao, Y., Noori, M. and Altabey, W.A. (2017a), "Damage detection for a beam under transient excitation via three different algorithms", J. Struct. Eng. Mech., 64(6), 803-817. https://doi.org/10.12989/sem.2017.64.6.803
  45. Zhao, Y., Noori, M., Altabey, W.A. and Naiwei, L. (2017b), "Reliability evaluation of a laminate composite plate under distributed pressure using a hybrid response surface method", Int. J. Reliabil. Quality Safe. Eng., 24(3), 1750013. http://dx.doi.org/10.1142/S0218539317500139
  46. Zhao, Y., Noori, M., Altabey W.A. and Wu, Z. (2018a), "Fatigue damage identification for composite pipeline systems using electrical capacitance sensors", J. Smart Mater. Struct., 27(8), 085023. https://doi.org/10.1088/1361-665X/aacc99
  47. Zhao, Y., Noori, M., Altabey, W.A., Ghiasi, R. and Zhishen, W., (2018b), "Deep learning-based damage, load and support identification for a composite pipeline by extracting modal macro strains from dynamic excitations", Appl. Sci., 8(12), 2564. https://doi.org/10.3390/app8122564
  48. Zhao, Y., Noori, M., Seyed, B.B. and Altabey, W.A. (2018c), "Mode shape-based damage identification for a reinforced concrete beam using wavelet coefficient differences and multiresolution analysis", J. Struct. Control Health Monit., 25(1), e2041. https://doi.org/10.1002/stc.2041
  49. Zhao, Y., Noori, M., Altabey, W.A., Ramin, G. and Wu, Z. (2019a), "Applying neural networks for tire pressure monitoring systems", Struct. Durab. Health Monit., 13(1), 85-103. http://dx.doi.org/10.32604/sdhm.2019.04695
  50. Zhao, Y., Noori, M., Altabey, W.A. and Awad, T. (2019b), "A comparison of three different methods for the identification of hysterically degrading structures using BWBN model", Front. Built Environ., 4(80), 1-19. http://dx.doi.org/10.3389/fbuil.2018.00080

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