1 A mini review on machine learning technique for bending and buckling behaviors of different composite structures 1 This paper examines recent developments in machine learning (ML) techniques for optimizing and predicting the flexural and buckling behavior of composite structures, including those made from concrete, fiber-reinforced polymers (FRP), wood, and metals. To enhance the understanding of structural system performance and data-driven modeling, various ML techniques are demonstrated and reviewed throughout the paper, including artificial neural networks (ANN), deep learning, and support vector machines (SVM). The paper also provides examples of how ML applications can reduce testing costs while improving design accuracy and fostering innovation in civil, materials, and mechanical engineering. 1 5 Sogand Jalili Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran Iran Sogandjalili1994@aut.ac.ir Iman Jalili Faculty of Fine Arts, Department of Architecture, University of Tehran, Tehran Iran Imanjalili@ut.ac.ir Machine learning prediction Composite behavior Buckling and bending Computational efficiency modeling [1] D. Chukov, S. Nematulloev, M. Zadorozhnyy, V. Tcherdyntsev, A. Stepashkin, D. Zherebtsov, Structure, mechanical and thermal properties of polyphenylene sulfide and polysulfone impregnated carbon fiber composites, Polymers 11(4) (2019) 684.##[2] D.K. Rajak, D.D. Pagar, P.L. Menezes, E. Linul, Fiber-reinforced polymer composites: Manufacturing, properties, and applications, Polymers 11(10) (2019) 1667.##[3] R.K. Behera, D. Pinisetty, D.D. Luong, Modeling and Simulation of Composite Materials, JOM 71(11) (2019) 3949-3950.##[4] R. Gupta, D. Srivastava, M. Sahu, S. Tiwari, R.K. Ambasta, P. Kumar, Artificial intelligence to deep learning: machine intelligence approach for drug discovery, Molecular Diversity 25(3) (2021) 1315-1360.##[5] I.D. Viotti, G.F. Gomes, Delamination identification in sandwich composite structures using machine learning techniques, Computers and Structures 280 (2023) 106990.##[6] R.F. Ribeiro Junior, G.F. Gomes, On the Use of Machine Learning for Damage Assessment in Composite Structures: A Review, Applied Composite Materials 31(1) (2024) 1-37.##[7] N. Nawafleh, F.M. AL-Oqla, Evaluation of mechanical properties of fiber-reinforced syntactic foam thermoset composites: A robust artificial intelligence modeling approach for improved accuracy with little datasets, Journal of the Mechanical Behavior of Materials 32(1) (2023).##[8] A.A. Griño, M.K. M. Daly, J.M.C. Ongpeng, Bio-Influenced Self-Healing Mechanism in Concrete and Its Testing: A Review, Applied Sciences 10(15) (2020) 5161.##[9] S. Huang, Q. Fu, L. Yan, B. Kasal, Characterization of interfacial properties between fibre and polymer matrix in composite materials – A critical review, Journal of Materials Research and Technology 13 (2021) 1441-1484.##[10] D.K. Rajak, D.D. Pagar, P.L. Menezes, E. Linul, Fiber-Reinforced Polymer Composites: Manufacturing, Properties, and Applications, Polymers, 2019.##[11] J. Kumar, D.R. Yadav, Engineered Wood Products as a Sustainable Construction Material: A Review, in: M. Gong (Ed.), Engineered Wood Products for Construction, IntechOpen, London, 2021.##[12] L. Singh, B. Singh, K.K. Saxena, Manufacturing techniques for metal matrix composites (MMC): an overview, Advances in Materials and Processing Technologies 6(2) (2020) 441-457.##[13] P.K. Rohatgi, A.D. M, B.F. Schultz, J.B. Ferguson, Synthesis and Properties of Metal Matrix Nanocomposites (MMNCS), Syntactic Foams, Self Lubricating and Self-Healing Metals, in: F. Marquis (Ed.) Proceedings of the 8th Pacific Rim International Congress on Advanced Materials and Processing, Springer International Publishing, Cham, 2016, pp. 1515-1524.##[14] J.M. Mistry, P.P. Gohil, Research review of diversified reinforcement on aluminum metal matrix composites: fabrication processes and mechanical characterization, 25(4) (2018) 633-647.##[15] J.B. Deb, J. Gou, H. Song, C. Maiti, Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites, Journal of Composites Science 8(3) (2024) 96.##[16] A. Kausar, I. Ahmad, S.A. Rakha, M.H. Eisa, A. Diallo, State-Of-The-Art of Sandwich Composite Structures: Manufacturing—to—High Performance Applications, Journal of Composites Science 7(3) (2023) 102.##[17] N. Tiwari, N. Baldo, N. Satyam, M. Miani, Mechanical Characterization of Industrial Waste Materials as Mineral Fillers in Asphalt Mixes: Integrated Experimental and Machine Learning Analysis, Sustainability 14(10) (2022) 5946.##[18] M. Alhusban, M. Alhusban, A.A. Alkhawaldeh, The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in Structural Engineering, Sustainability 16(1) (2024) 11.##[19] D.J. Cruz, M.R. Barbosa, A.D. Santos, S.S. Miranda, R.L. Amaral, Application of Machine Learning to Bending Processes and Material Identification, Metals 11(9) (2021) 1418.##[20] K. Falkowicz, M. Kulisz, Prediction of buckling behaviour of composite plate element using artificial neural networks, Advances in Science and Technology. Research Journal 18(1) (2024).##[21] O.S. Ahmed, A. Aabid, J.S. Mohamed Ali, M. Hrairi, N.M. Yatim, Progresses and Challenges of Composite Laminates in Thin-Walled Structures: A Systematic Review, ACS Omega 8(34) (2023) 30824-30837.##[22] N.R. Kapoor, A. Kumar, A. Kumar, A. Kumar, H.C. Arora, 1 - Artificial intelligence in civil engineering: An immersive view, in: M.L. Nehdi, H.C. Arora, K. Kumar, R. Damaševičius, A. Kumar (Eds.), Artificial Intelligence Applications for Sustainable Construction, Woodhead Publishing2024, pp. 1-74.##[23] M. Mishra, P.B. Lourenço, G.V. Ramana, Structural health monitoring of civil engineering structures by using the internet of things: A review, Journal of Building Engineering 48 (2022) 103954.##[24] D. Chen, K. Gao, J. Yang, L. Zhang, Functionally graded porous structures: Analyses, performances, and applications – A Review, Thin-Walled Structures 191 (2023) 111046.##[25] G. Manickam, A. Bharath, A.N. Das, A. Chandra, P. Barua, Thermoelastic Stability Behavior of Curvilinear Fiber‐Reinforced Composite Laminates With Different Boundary Conditions, Polymer Composites 40(7) (2019) 2876-2890.##[26] F. Ebrahimi, H. Ezzati, A Machine-Learning-Based Model for Buckling Analysis of Thermally Affected Covalently Functionalized Graphene/Epoxy Nanocomposite Beams, Mathematics 11(6) (2023) 1496.##[27] M.H. Yas, N. Samadi, Free vibrations and buckling analysis of carbon nanotube-reinforced composite Timoshenko beams on elastic foundation, International Journal of Pressure Vessels and Piping 98 (2012) 119-128.