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Optimal Prediction for Patch Design Using YUKI-RANDOM-FOREST in a Cracked Pipeline Repaired with CFRP

  • Research Article-Mechanical Engineering
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Abstract

This paper presents the effectiveness of a hybrid YUKI-RANDOM-FOREST, Particle Swarm Optimization-YUKI (PSO-YUKI), and balancing composite motion optimization algorithm (BCMO) based on artificial neural networks (ANN) for the best prediction of patch design considering the maximum principal stress. The study compares the maximum principal stress in a damaged pipe under different composite patch designs. Robust models have been developed and utilized in various applications. The research investigates the influence of cracks on the mechanical characteristics of API X70 steel in a test pipe under critical pressure. The numerical model employs the extended finite element method (XFEM) to simulate notches. Extending the optimization technique, the study examines the effect of crack presence in a pipeline section under internal pressure without and with composite repairs on the maximum principal stress. The sensitivity of stress is analyzed with respect to the design parameters of the composite patch. Finally, YUKI-RANDOM-FOREST, NN-PSO-YUKI, and NN-BCMO, with different parameters and hidden layer sizes are employed to predict the maximum principal stress under different composite patch designs, and yielding minimal error. Once the database was built, our model was prepared to predict various situations at the composite patch level. Compared to other methods, the obtained results with hybrid YUKI-RANDOM-FOREST are effective. The investigation technique is relevant to real-world engineering applications, structural safety control, and design processes.

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References

  1. Mohtadi-Bonab, M.A., et al.: Evaluation of hydrogen induced cracking behavior of API X70 pipeline steel at different heat treatments. Int. J. Hydrogen Energy 39(11), 6076–6088 (2014)

    Article  Google Scholar 

  2. Oulad Brahim, A., et al.: Strength prediction of a steel pipe having a hemi-ellipsoidal corrosion defect repaired by GFRP composite patch using artificial neural network. Compos. Struct. 304, 116299 (2023)

    Article  Google Scholar 

  3. Shin, S.Y., et al.: Fracture toughness analysis in transition temperature region of API X70 pipeline steels. Mater. Sci. Eng. A 429(1), 196–204 (2006)

    Article  Google Scholar 

  4. Mohtadi-Bonab, M.A., et al.: The mechanism of failure by hydrogen induced cracking in an acidic environment for API 5L X70 pipeline steel. Int. J. Hydrogen Energy 40(2), 1096–1107 (2015)

    Article  Google Scholar 

  5. Ghandourah, E., et al.: Enhanced ANN predictive model for composite pipes subjected to low-velocity impact loads. Buildings 13(4), 973 (2023)

    Article  Google Scholar 

  6. Benaissa, B., et al.: Optimal axial-probe design for foucault-current tomography: a global optimization approach based on linear sampling method. Energies 16(5), 2448 (2023)

    Article  Google Scholar 

  7. Slimani, M., et al.: Experimental sensitivity analysis of sensor placement based on virtual springs and damage quantification in CFRP composite. J. Mater. Eng. Struct. JMES 9(2), 207–220 (2022)

    MathSciNet  Google Scholar 

  8. Nateche, T., et al.: Residual harmfulness of a defect after repairing by a composite patch. Eng. Fail. Anal. 48, 166–173 (2015)

    Article  Google Scholar 

  9. Reis, J.M.L.; Costa, A.R.; da Costa Mattos, H.S.: Repair of damage in pipes using bonded GFRP patches. Composite Struct. 296, 115875 (2022)

    Article  Google Scholar 

  10. Saffar, A., et al.: Prediction of failure pressure in pipelines with localized defects repaired by composite patches. J. Fail. Anal. Prev. 19(6), 1801–1814 (2019)

    Article  Google Scholar 

  11. Zarrinzadeh, H.; Kabir, M.Z.; Deylami, A.: Experimental and numerical fatigue crack growth of an aluminium pipe repaired by composite patch. Eng. Struct. 133, 24–32 (2017)

    Article  Google Scholar 

  12. Ali Ghaffari, M.; Hosseini-Toudeshky, H.: Fatigue crack propagation analysis of repaired pipes with composite patch under cyclic pressure. J. Pressure Vessel Technol. 135(3), 031402 (2013)

    Article  Google Scholar 

  13. Ganesh, N., et al.: Robust metamodels for accurate quantitative estimation of turbulent flow in pipe bends. Eng. Comput. 36(3), 1041–1058 (2020)

    Article  Google Scholar 

  14. Jain, P.; Choudhury, A.; Dutta, P.; Kalita, K.; Barsocchi, P., et al.: Random forest regression-based machine learning model for accurate estimation of fluid flow in curved pipes. Processes (2021). https://doi.org/10.3390/pr9112095

    Article  Google Scholar 

  15. Narayanan, G., et al.: PSO-tuned support vector machine metamodels for assessment of turbulent flows in pipe bends. Eng. Comput. 37(3), 981–1001 (2020)

    Article  Google Scholar 

  16. Turkyilmazoglu, M.: Exact solutions concerning momentum and thermal fields induced by a long circular cylinder. Eur. Phys. J. Plus 136(5), 1–10 (2021)

    Article  Google Scholar 

  17. Mustafa, T.: Eyring-Powell fluid flow through a circular pipe and heat transfer: full solutions. Int. J. Numer. Meth. Heat Fluid Flow 30(11), 4765–4774 (2020)

    Article  Google Scholar 

  18. Turkyilmazoglu, M.; Duraihem, F.Z.: Full solutions to flow and heat transfer from slip-induced microtube shapes. Micromachines (2023). https://doi.org/10.3390/mi14040894

    Article  Google Scholar 

  19. Dorigo, M.; Stützle, T.: The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances, in Handbook of Metaheuristics. In: Glover, F.; Kochenberger, G.A. (Eds.) Springer, pp. 250–285. US, Boston, MA (2003)

    Google Scholar 

  20. Agrawal, P., et al.: Metaheuristic algorithms on feature selection: a survey of one decade of research (2009–2019). IEEE Access 9, 26766–26791 (2021)

    Article  Google Scholar 

  21. Vu-Huu, T., et al.: An improved bat algorithms for optimization design of truss structures. Structures 47, 2240–2258 (2023)

    Article  Google Scholar 

  22. Ghandourah, E., et al.: Novel approach-based sparsity for damage localization in functionally graded material. Buildings 13(7), 1768 (2023)

    Article  Google Scholar 

  23. Abdel-Basset, M.; Abdel-Fatah, L.; Sangaiah, A.K.: Chapter 10 - Metaheuristic Algorithms: A Comprehensive Review. In: Sangaiah, A.K.; Sheng, M.; Zhang, Z. (Eds.) Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications, pp. 185–231. Academic Press, London (2018)

    Chapter  Google Scholar 

  24. Gogna, A.; Tayal, A.: Metaheuristics: review and application. J. Exp. Theor. Artif. Intell. 25(4), 503–526 (2013)

    Article  Google Scholar 

  25. Amoura, N., et al. Deep Neural Network and YUKI Algorithm for Inner Damage Characterization Based on Elastic Boundary Displacement. In: International Conference of Steel and Composite for Engineering Structures. 2022. Springer.

  26. Benaissa, B., et al.: YUKI algorithm and POD-RBF for elastostatic and dynamic crack identification. J. Comput. Sci. 55, 101451 (2021)

    Article  Google Scholar 

  27. Khatir, S., et al.: An improved Artificial Neural Network using Arithmetic Optimization Algorithm for damage assessment in FGM composite plates. Compos. Struct. 273, 114287 (2021)

    Article  Google Scholar 

  28. Baxt, W.G.: Application of artificial neural networks to clinical medicine. The Lancet 346(8983), 1135–1138 (1995)

    Article  Google Scholar 

  29. Garcı́a-Gimeno, R.M.a., C. Hervás-Martı́nez, and M.I. de Silóniz, 2002 Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food. International Journal of Food Microbiology, 72(1): p. 19–30.

  30. Fahem, N., et al.: Prediction of resisting force and tensile load reduction in GFRP composite materials using Artificial Neural Network-Enhanced Jaya Algorithm. Compos. Struct. 304, 116326 (2023)

    Article  Google Scholar 

  31. Kahouadji, A., et al. Vibration-Based Damage Assessment in Truss Structures Using Local Frequency Change Ratio Indicator Combined with Metaheuristic Optimization Algorithms. In International Conference of Steel and Composite for Engineering Structures. 2022. Springer.

  32. Slimani, M., et al. Improved ANN for Damage Identification in Laminated Composite Plate. In International Conference of Steel and Composite for Engineering Structures. 2022. Springer.

  33. Cuong-Le, T., et al.: An efficient approach for damage identification based on improved machine learning using PSO-SVM. Eng. Comput. 38, 1–16 (2021)

    Google Scholar 

  34. Cuong-Le, T., et al.: A novel version of grey wolf optimizer based on a balance function and its application for hyperparameters optimization in deep neural network (DNN) for structural damage identification. Eng. Failure Anal. 142, 106829 (2022)

    Article  Google Scholar 

  35. LeCun, Y.; Bengio, Y.; Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  36. Krizhevsky, A.; Sutskever, I.; Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  37. Hochreiter, S.; Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  38. Cho, K., et al., Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014.

  39. Tarancón, J.E., et al.: Enhanced blending elements for XFEM applied to linear elastic fracture mechanics. Int. J. Numer. Meth. Eng. 77(1), 126–148 (2009)

    Article  MathSciNet  Google Scholar 

  40. Agathos, K.; Bordas, S.P.A.; Chatzi, E.: Improving the conditioning of XFEM/GFEM for fracture mechanics problems through enrichment quasi-orthogonalization. Comput. Methods Appl. Mech. Eng. 346, 1051–1073 (2019)

    Article  MathSciNet  Google Scholar 

  41. Khatir, S., et al. Crack identification using eXtended IsoGeometric analysis and particle swarm optimization. In: Proceedings of the 7th International Conference on Fracture Fatigue and Wear: FFW 2018, 9–10 July 2018, Ghent University, Belgium. 2019. Springer.

  42. Alishahi, E., et al.: Effects of carbon nanoreinforcements of different shapes on the mechanical properties of epoxy-based nanocomposites. Macromol. Mater. Eng. 298(6), 670–678 (2013)

    Article  Google Scholar 

  43. Le-Duc, T.; Nguyen, Q.-H.; Nguyen-Xuan, H.: Balancing composite motion optimization. Inf. Sci. 520, 250–270 (2020)

    Article  MathSciNet  Google Scholar 

  44. Khatir, A., et al.: A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam. Compos. Struct. 311, 116803 (2023)

    Article  Google Scholar 

  45. Belytschko, T., et al.: Arbitrary discontinuities in finite elements. Int. J. Numer. Meth. Eng. 50(4), 993–1013 (2001)

    Article  Google Scholar 

  46. Melenk, J.M.; Babuška, I.: The partition of unity finite element method: Basic theory and applications. Comput. Methods Appl. Mech. Eng. 139(1), 289–314 (1996)

    Article  MathSciNet  Google Scholar 

  47. Bordas, S., et al.: An extended finite element library. Int. J. Numer. Meth. Eng. 71(6), 703–732 (2007)

    Article  Google Scholar 

  48. Nguyen-Thanh, N., et al.: Isogeometric analysis using polynomial splines over hierarchical T-meshes for two-dimensional elastic solids. Comput. Methods Appl. Mech. Eng. 200(21), 1892–1908 (2011)

    Article  MathSciNet  Google Scholar 

  49. Ye, C.; Shi, J.; Cheng, G.J.: An eXtended Finite Element Method (XFEM) study on the effect of reinforcing particles on the crack propagation behavior in a metal–matrix composite. Int. J. Fatigue 44, 151–156 (2012)

    Article  Google Scholar 

  50. Oulad Brahim, A., et al.: Prediction of the peak load and crack initiation energy of dynamic brittle fracture in X70 steel pipes using an improved artificial neural network and extended Finite Element Method. Theoret. Appl. Fract. Mech. 122, 103627 (2022)

    Article  Google Scholar 

  51. Abir, M.R., et al.: On the relationship between failure mechanism and compression after impact (CAI) strength in composites. Compos. Struct. 182, 242–250 (2017)

    Article  Google Scholar 

  52. Feld, N., et al.: Micro-mechanical prediction of UD laminates behavior under combined compression up to failure: influence of matrix degradation. J. Compos. Mater. 45(22), 2317–2333 (2011)

    Article  Google Scholar 

  53. Sheela, K.G.; Deepa, S.N.: Review on methods to fix number of hidden neurons in neural networks. Math. Probl. Eng. 2013, 425740 (2013)

    Article  Google Scholar 

  54. Guha, R.; Stanton, D.T.; Jurs, P.C.: Interpreting Computational Neural Network Quantitative Structure−Activity Relationship Models: A Detailed Interpretation of the Weights and Biases. J. Chem. Inf. Model. 45(4), 1109–1121 (2005)

    Article  Google Scholar 

  55. Ahmadianfar, I.; Bozorg-Haddad, O.; Chu, X.: Gradient-based optimizer: A new metaheuristic optimization algorithm. Inf. Sci. 540, 131–159 (2020)

    Article  MathSciNet  Google Scholar 

  56. Liu, Y.; Wang, Y.; Zhang, J.: New Machine Learning Algorithm: Random Forest. In Information Computing and Applications, Berlin, Heidelberg, Springer, Berlin Heidelberg (2012)

    Google Scholar 

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Acknowledgements

The authors would like to acknowledge the support from Ho Chi Minh City Open University under the basic research fund (No. E2023.03.1).

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Authors and Affiliations

  1. Structural Section DICEA, Polytechnic University of Marche, Ancona, Italy

    Abdelmoumin Oulad Brahim & Roberto Capozucca

  2. Department of Mechanical Engineering, LEMI Laboratory, University M’hamed Bougara Boumerdes, 35000, Boumerdes, Algeria

    Noureddine Fahem

  3. Center for Engineering Application & Technology Solutions, Ho Chi Minh City Open University, Ho Chi Minh, Vietnam

    Samir Khatir & Thanh Cuong-Le

  4. Department of Mechanical Systems Engineering, Design Engineering Lab, Toyota Technological Institute, 2-12-1 Hisakata, Tenpaku-ku, Nagoya, Aichi, 468-8511, Japan

    Brahim Benaissa

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  1. Abdelmoumin Oulad Brahim
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Correspondence to Thanh Cuong-Le.

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Oulad Brahim, A., Capozucca, R., Khatir, S. et al. Optimal Prediction for Patch Design Using YUKI-RANDOM-FOREST in a Cracked Pipeline Repaired with CFRP. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08777-1

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