Aerospace Mechanics

Aerospace Mechanics

Prediction of the Critical Buckling Load in a Composite Lattice Conical Structure Using Artificial Neural Network

Document Type : Solid Mechanics

Authors
Aliasghar Naderi 1
Farid Bagherpoor 2
Seyed Mostafa Mirtabaei 2
1 Associate Professor, Imam Ali University, Tehran, Iran.
2 Assistant Professor, Imam Ali University, Tehran, Iran.
10.47176/MAJ.2026.1541
Abstract
Composite lattice shells are widely employed across various industries due to their exceptional strength-to-weight ratio and ability to sustain significant loads while remaining lightweight. Buckling analysis and the accurate prediction of the critical buckling load are among the most essential design considerations for these structures. In this study, the influence of incorporating carbon nanoparticles (1, 1.5, 3, 4, and 5 wt%) on the elastic properties of the resin was investigated using the Modified Halpin–Tsai model. The effective properties of the composite material in the conical lattice structure were then estimated through the rule of mixtures. Based on these properties, finite element models were developed in Abaqus to predict the buckling load, and the numerical results were validated against experimental data. Furthermore, to enable neural network–based prediction of the critical buckling load, 75 parametric models were generated in Abaqus with varying nanoparticle concentrations and aspect ratios (0.5, 1, and 1.5) for the cross-sections of helical and circumferential ribs. The corresponding critical buckling loads were calculated and used to train neural networks. Networks with different neuron counts and training epochs were systematically evaluated, and the optimal architecture for accurate prediction of the critical buckling load was identified.

Graphical Abstract

Prediction of the Critical Buckling Load in a Composite Lattice Conical Structure Using Artificial Neural Network
Keywords
Composite Lattice Structures
Buckling Analysis
Finite Element Method
Artificial Neural Network (ANNs
Subjects


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[1]     Wang D, Abdalla MM. Global and local buckling analysis of grid-stiffened composite panels. Composite Structures. 2015;119:767–776. DOI 10.1016/j.compstruct.2014.09.050
[2]     Kim TD. Fabrication and testing of thin composite isogrid stiffened panel. Composite Structures. 2000;49(1):21–25. DOI 10.1016/S0263-8223(99)00122-1
[3]     Hedayatian M, Liaghat Gh, Rahimi Gh, Pol MH, Khoshsorour M. Experimental study on dynamic response of grid cylindrical composite structures under high velocity impact. Modares Mechanical Engineering. 2014;14(2):41–46.
[4]     Alirezvani H, Davar A, Heydari Beni M, Eskandari Jam J, Eskandari Shahraki M. A dynamic stability analysis of composite grid-stiffened cylindrical shells under harmonic loads using the Donnelly theory. Aerospace Mechanics. 2022;17(4):55–79. DOI 20.1001.1.26455323.1400.14.4.5.3
[5]     Rahnama M, Sheikhi MM, Hamzeloo SR. Free vibration behavior of three-skin conical shells with composite lattice core based on semi-analytical and finite element methods. Aerospace Mechanics.(Persian) 2024;20(3):75–86. DOI 20.1001.1.26455323.1403.20.3.5.1
[6]     Morozov EV, Lopatin AV, Nesterov VA. Buckling analysis and design of anisogrid composite lattice conical shells. Composite Structures. 2011;93(12):3150–3162. DOI 10.1016/j.compstruct.2011.06.015
[7]     Latifi Rostami SA, Kolahdooz A, Lim HJ. Structural stability and design optimization of anisogrid conical lattice composite shells. Structures. 2025;79:109504. DOI 10.1016/j.istruc.2025.109504
[8]     Naderi AA, Rahimi Gh. Simple method for buckling load of composite conical lattice structures under axial load. Modares Mechanical Engineering. 2015;14(15):290–298. DOI  20.1001.1.10275940.1393.14.15.28.5
[9]     Naderi AA, Rahimi Gh, Arefi M. Influence of fiber paths on buckling load of tailored conical shells. Steel and Composite Structures. 2014;16(4):375–387. DOI 10.12989/scs.2014.16.4.375
[10]  Shan L, Tan CY, Shen X, Ramesh S, Zarei MS, Kolahchi R, Hajmohammad MH. The effects of nano-additives on the mechanical, impact, vibration, and buckling/post-buckling properties of composites: A review. Journal of Materials Research and Technology. 2023;24:7570–7598.DOI  10.1016/j.jmrt.2023.04.267
[11]  Ahmadifar A, Zamani MR, Davar A, Jam JE, Beni MH. Experimental and numerical buckling analysis of carbon fiber composite lattice conical structure before and after lateral impact. Journal of Applied and Computational Mechanics. 2020;6(4):813–822. DOI: 10.22055/JACM.2019.29929.1664
[12]  Yakhchian B, Shaterzadeh A. Thermal buckling and postbuckling analysis of plates reinforced with graphene platelets using differential quadrature method. Aerospace Mechanics. (Persian)2023;19(4):103–117. DOI: 20.1001.1.26455323.1402.19.4.8.9
[13]  Eskandary K, Shishesaz M, Moradi S. Buckling analysis of composite conical shells reinforced by agglomerated functionally graded carbon nanotube. Archives of Civil and Mechanical Engineering. 2022;22(3):132. DOI 10.1007/s43452-022-00440-6
[14]  Zamani R, Rahimi GhH, Pol MH, Hedayatian M. Reinforcing effect of nanoclay on buckling behavior of nanocomposite grid shells: Experimental investigation. Modares Mechanical Engineering. 2015;15(3):411–418. DOI  20.1001.1.10275940.1394.15.3.9.9
[15]  Naderi AA, Rahimi Gh. Buckling analysis of glass fiber reinforced epoxy/clay conical shells subjected to axial loading. Proceedings of the 12th Iranian Aerospace Society Conference. 2013.
[16]  Tahir ZUR, Mandal P, Adil MT, Naz F. Application of artificial neural network to predict buckling load of thin cylindrical shells under axial compression. Engineering Structures. 2021;248:113221. DOI 10.1016/j.engstruct.2021.113221
[17]  Azizi S, Fattahi AM, Nel W. Investigation of elastic properties of PE/CNT injected composites. International Journal of Engineering and Technology. 2018;7:3330–3334. DOI 10.14419/ijet.v7i4.10745
[18]  Tao F, Liu X, Du H, Yu W. Physics-informed artificial neural network approach for axial compression buckling analysis of thin-walled cylinder. AIAA Journal. 2020. DOI 10.2514/1.J058765
[19]  Falcone R, Lima C, Martinelli E. Soft computing techniques in structural and earthquake engineering: a literature review. Engineering Structures. 2020;207:110269. DOI 10.1016/j.engstruct.2020.110269
[20]  Haykin S. Neural networks and learning machines. 3rd ed. Pearson Education India; 2009.
[21]  Tran VL, Kim SE. Efficiency of three advanced data-driven models for predicting axial compression capacity of CFDST columns. Thin-Walled Structures. 2020;152:106744. DOI 10.1016/j.tws.2020.106744
[22]  Tahir ZR, Mandal P. Artificial neural network prediction of buckling load of thin cylindrical shells under axial compression. Engineering Structures. 2017;152:843–855. DOI 10.1016/j.engstruct.2017.09.016
 
 
 
 
Aerospace Mechanics
Volume 22, Issue 1 - Serial Number 83
Spring
Spring 2026
Pages 63-72

  • Receive Date 11 November 1404
  • Revise Date 07 January 1405
  • Accept Date 28 January 1405
  • Publish Date 24 August 2026