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Dynamic performance using artificial intelligence techniques and educational assessment of nanocomposite structures

  • Han Zengxia (Department of Education, Xinzhou Teachers (Normal) University) ;
  • M. Nasihatgozar (Department of Mechanical Engineering, Kashan Branch, Islamic Azad University) ;
  • X. Shen (Department of Education, Xinzhou Teachers (Normal) University)
  • Received : 2024.09.01
  • Accepted : 2024.10.04
  • Published : 2024.10.10

Abstract

The present paper deals with a comprehensive study about dynamic performance and educational economic assessment of nanocomposite structures, while it focuses on truncated conical shells. Advanced structure dynamic behavior has been analyzed by means of AI techniques, which allow one to predict and optimize their performances with good accuracy for different loading and environmental conditions. The incorporation of the AI method significantly enhances the computational efficiency and is a powerful tool in designing nanocomposites and for their structural analysis. Further, an educational assessment is provided in the context of cost and practicality related to such structures in engineering education. This study showcases the capabilities of AI-enabled methods with regard to cost reduction, improvement of structural efficiency, and enhancement of learning engagement for students through certain practical examples on state-of-the-art nanocomposite technology. The results also confirm a remarkable capability of artificial intelligence regarding the optimization of both dynamic and economic aspects, which could be highly valued for further development of nanocomposite structures.

Keywords

Acknowledgement

Shanxi Province Higher Education General Teaching Reform and Innovation Project: Practical Research on the 6C Teaching Model under the C-STEAM Concept in Teaching Methods Courses of Early Childhood Teacher Education. No J20241258.

References

  1. Allah, M.J., Hassouna, S., Aitbelale, R. and Timesli, A. (2023), "Free vibration analysis of FGM plates using an optimization methodology combining artificial neural networks and thirdorder shear deformation theory", Steel Compos. Struct., 49(6), 633-646. https://doi.org/10.12989/scs.2023.49.6.633.
  2. Allahyari, S.M.R., Shokravi, M. and Murmy, T.T. (2024), "Modeling of truncated nanocompositeconical shell structures for dynamic stability response", Struct. Eng. Mech., 91(3), 325-334. https://doi.org/10.12989/sem.2024.91.3.325.
  3. Almakaeel, H., Albalawi, A. and Desai, S. (2018), "Artificial neural network based framework for cyber nano manufacturing", Manufact. Lett., 15, 151-154. https://doi.org/10.1016/j.mfglet.2017.12.013.
  4. Bahcelerli, N.M. and Altinay, M. (2023), "Tourism education programme adoption to learning organization and human resources industry for service quality", J. Chin. Hum. Resour. Manag., 14(3), 59-69. https://doi.org/10.47297/wspchrmWSP2040-800505 .20231403.
  5. Bahiraei, M., Heshmatian, S. and Moayedi, H. (2019), "Artificial intelligence in the field of nanofluids: A review on applications and potential future directions", Powder Technol., 353, 276-301. https://doi.org/10.1016/j.powtec.2019.05.034.
  6. Bakhshandeh Amnieh, H., Zamzam, M.S. and Kolahchi, R. (2018), "Dynamic analysis of non-homogeneous concrete blocks mixed by SiO2 nanoparticles subjected to blast load experimentally and theoretically", Construct. Build. Mat., 174, 633-644.
  7. Baseri, V., Jafari, G.S. and Kolahchi, R. (2016), "Analytical solution for buckling of embedded laminated plates based on higher order shear deformation plate theory", Steel Compos. Struct., 21(4), 883-919.
  8. Bilouei, B.S., Kolahchi, R. and Bidgoli, M.R. (2018), "Buckling of beams retrofitted with Nano-Fiber Reinforced Polymer (NFRP)", Comput., 18, 1053-106, https://doi.org/10.12989/cac.2016.18.6.1053.
  9. Boztas, G.D., Berigel, M., Altinay, Z., Altinay, F., Shadiev, R. and Dagli, G. (2023), "Readiness for inclusion: Analysis of information society indicator with educational attainment of people with disabilities in European union countries", J. Chin. Hum. Resour. Manag., 14(3), 47-58. https://doi.org/10.47297/wspchrmWSP2040-800504.20231403.
  10. Chen, Z., Liang, Q., Wei, Z., Chen, X., Shi, Q., Yu, Z. and Sun, T. (2023), "An overview of in vitro biological neural networks for robot intelligence", Cyborg Bionic Syst., 4. https://doi.org/10.34133/cbsystems.0001.
  11. Elhoone, H., Zhang, T., Anwar, M. and Desai, S. (2020), "Cyberbased design for additive manufacturing using artificial neural networks for Industry 4.0", Int. J. Product. Res., 58, 2841-2861. https://doi.org/10.1080/00207543.2019.1671627.
  12. Hajmohammad, M.H., Farrokhian, A. and Kolahchi, R. (2021), "Dynamic analysis in beam element of wave-piercing Catamarans undergoing slamming load based on mathematical modelling", Ocean Eng., 234, 109269. https://doi.org/10.1016/j.oceaneng.2021.109269.
  13. Kolahchi, R., Safari, M. and Esmailpour, M. (2016), "Dynamic stability analysis of temperature-dependent functionally graded CNT-reinforced visco-plates resting on orthotropic elastomeric medium", Compos. Struct., 150, 255-265, https://doi.org/10.1016/j.compstruct.2016.05.023.
  14. Kolahchi, R., Zarei, M.Sh., Hajmohammad, M.H. and Naddaf Oskouei, A. (2017), "Visco-nonlocal-refined Zigzag theories for dynamic buckling of laminated nanoplates using differential cubature-Bolotin methods", Thin-Wall. Struct., 113, 162-169. https://doi.org/10.1016/j.tws.2017.01.016.
  15. Li, N., Asteris, P.G., Tran, T.T., Pradhan, B. and Nguyen, H. (2022), "Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization", Steel Compos. Struct., 42(6), 733-749. https://doi.org/10.12989/scs.2022.42.6.733.
  16. Liu, Z., Tang, Q., Ouyang, F., Long, T. and Liu, S. (2024), "Profiling students' learning engagement in MOOC discussions to identify learning achievement: An automated configurational approach", Comput. Educ., 219, 105109. https://doi.org/10.1016/j.compedu.2024.105109.
  17. Motezaker, M,. Jamali, M. and Kolahchi, R. (2021a), "Application of differential cubature method for nonlocal vibration, buckling and bending response of annular nanoplates integrated by piezoelectric layers based on surface-higher order nonlocalpiezoelasticity theory, Comput. Appl. Math. Comput. Appl. Math., 369, 112625. https://doi.org/10.1016/j.cam.2019.112625.
  18. Motezaker, M., Kolahchi, R., Rajak, D.K. and Mahmoud, S.R. (2021b), "Influences of fiber reinforced polymer layer on the dynamic deflection of concrete pipes containing nanoparticle subjected to earthquake load", Polym. Compos., 42(8), 4073-4081.
  19. Noureldin, M., Gharagoz, M.M. and Kim, J. (2023), "Seismic retrofit of steel structures with re-centering friction devices using genetic algorithm and artificial neural network", Steel Compos. Struct., 47(2), 167-180. https://doi.org/10.12989/scs.2023.47.2.167.
  20. Patrick, E.I., Makhatha, M.E. and Jen, T.-C. (2024), "Artificial Intelligence prediction and optimization of the mechanical strength of modified natural fibre/MWCNT polymer nanocomposite", J. Sci. Adv. Mater. Dev., 9(2), 100705. https://doi.org/10.1016/j.jsamd.2024.100705.
  21. So, K.P., Stapelberg, M., Zhou, Y.R., Li, M., Short, M.P. and Yip, S. (2022), "Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machinelearning algorithm", Mater. Res. Lett., 10, 14-20. https://doi.org/10.1080/21663831.2021.2005700.
  22. Tang, N., Zhang, C., Yuan, Z. and Yvaz, A. (2024), "Artificial intelligence design for dependence of size surface effects on advanced nanoplates through theoretical framework", Steel Compos. Struct., 52(6), 621-635. https://doi.org/10.12989/scs.2024.52.6.621.
  23. Wang, B., Wang, Z., Song, Y., Zong, W., Zhang, L., Ji, K. and Dai, Z. (2023), "A neural coordination strategy for attachment and detachment of a climbing robot inspired by Gecko locomotion", Cyborg Bionic Syst., 4. https://doi.org/10.34133/cbsystems.0008.
  24. Wang, Y. and Sigmund, O. (2024), "Topology optimization of multi-material active structures to reduce energy consumption and carbon footprint", Struct. Multidiscip. Optim., 67(1), 5. https://doi.org/10.1007/s00158-023-03698-3.
  25. Xu, T., Gao, Q., Ge, X., and Lu, J. (2024a), "The relationship between social media and professional learning from the perspective of pre-service teachers: A survey", Educ. Inf. Technol., 29(2), 2067-2092. https://doi.org/10.1007/s10639-023-11861-y.
  26. Xu, W., Xing, Q., Yu, Y. and Zhao, L. (2024b), "Exploring the influence of gamified learning on museum visitors' knowledge and career awareness with a mixed research approach", Humanit. Soc. Xci. Commun., 11(1), 1055. https://doi.org/10.1057/s41599-024-03583-4.
  27. Yang, X. and Li, M. (2024), "Supply chain management and artificial intelligence improve the microstructure and economic evaluation of composite materials", Steel Compos. Struct., 51(1), 43-56. https://doi.org/10.12989/scs.2024.51.1.043.
  28. Zamanian, M., Kolahchi, R. and Bidgoli, M.R. (2017), "Agglomeration effects on the buckling behaviour of embedded beams reinforced with SiO2 nano-particles", Wind. Struct., 24, 43-57. https://doi.org/10.12989/was.2017.24.1.043.
  29. Zhang, C., Khorshidi, H., Najafi, E. and Ghasemi, M. (2023), "Fresh, mechanical and microstructural properties of alkaliactivated composites incorporating nanomaterials: A comprehensive review", J. Clean. Prod., 384, 135390. https://doi.org/10.1016/j.jclepro.2022.135390.