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2024-12-10
Model-Based Mechanical Property and Structural Failure Prediction of Pseudo Ductile Hybrid Composite
Authors
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Genetu Amare Dress
1. Department of Mechanical Engineering, College of Engineering, Addis Ababa Science and Technology University, P.O.Box 16417, Addis Ababa, Ethiopia; 2. Artificial Intelligence and Robotics Center of Excellence, Addis Ababa Science and Technology University, P.O.Box 16417, Addis Ababa, Ethiopia
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Yohannes Regassa
1. Department of Mechanical Engineering, College of Engineering, Addis Ababa Science and Technology University, P.O.Box 16417, Addis Ababa, Ethiopia; 2. Artificial Intelligence and Robotics Center of Excellence, Addis Ababa Science and Technology University, P.O.Box 16417, Addis Ababa, Ethiopia
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Ermias Gebrekidan Koricho
Department of Mechanical Engineering, University of Texas at Tyler, Houston Engineering Campus, Houston, TX 77082-2642, United States
DOI:
https://doi.org/10.30564/jbms.v7i2.8642Abstract
Lightweight fiber reinforced composites are widely used in engineering structures, which often fail catastrophically due to the uncertainty of external loads and their brittle nature. The development of pseudo ductile hybrid composites was the proposed solution to create minimal ductility in fiber reinforced composites so that equipment downtime, cost, and loss of lives can be minimized in their structural application. However, the development of pseudo ductile hybrid composites does not guarantee that pseudo ductile hybrid composite is prone to failure. As a result, different models, including Halpin-Tsai, Hashin and Shtrikman, Weibull, and log-normal models, were developed to predict degradation of mechanical properties and structural failure so that prior recognition of failure can be achieved. The current structural health monitoring research trend shows the development of hybrid mechanical property and structural failure prediction models spalling the drawback of data-driven and physics-based models. Physics-based models require detail understanding of the root cause of failure in terms of mathematical or physical model to predict failure progression whereas data-driven models rely on historical data or sensor data collected from machineries or structures. While hybrid models combine the strengths of both physics-based and data-driven models providing manageable uncertainty and more accurate prediction. This paper aims to review model-based mechanical property and structural failure prediction strategies with regard to pseudo ductile hybrid composites highlighting future research directions and challenges, and offering insights beneficial to the research and industrial communities.
Keywords:
Failure Prediction; Mechanical Property Prediction; Pseudo Ductile Hybrid Composite; Data-Driven Models; Physics-Based Models; Hybrid Models; Electric AircraftReferences
[1] Laurin, F., Carrere, N., Huchette, C., et al., 2013. A multiscale hybrid approach for damage and final failure predictions of composite structures. Journal of Composite Materials. 47(20–21), 1–15. DOI: https://doi.org/10.1177/0021998312470151
[2] Hinton, M.J., Kaddour, A.S., Soden, P.D., 2004. The worldwide failure exercise: Its origin, concept and content. In: Hinton, M.J., Kaddour, A.S., Soden, P.D. (eds.).Failure Criteria in Fibre-Reinforced-Polymer Composites. Elsevier: Amsterdam, the Netherlands. pp. 2–28.
[3] Nguyen, K.T.P., Medjaher, K., 2019. A new dynamic predictive maintenance framework using deep learning for failure prognostics. Reliability Engineering & System Safety. 188, 251–262. DOI: https://doi.org/10.1016/j.ress.2019.03.018
[4] Agyei, R.F., 2021. Investigating Damage in Short Fiber Reinforced Composites [Ph.D. Thesis]. West Lafayette, IN, USA: Purdue University.
[5] Marino, M., Sabatini, R., 2014. Advanced lightweight aircraft design configurations for green operations. Proceedings of the practical responses to climate change; 4–6 August 2014; Washington, DC, USA. pp. 1–9.
[6] Casadei, A., Broda, R., Ricardo Inc., 2008. Impact of Vehicle Weight Reduction on Fuel Economy for Various Vehicle Architectures. Aluminum Association Inc.: Arlington, VA, USA. pp. 1–60.
[7] Nastos, C., Komninos, P., Zarouchas, D., 2023. Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methods. Composite Structures. 311, 116815. DOI: https://doi.org/10.1016/j.compstruct.2023.116815
[8] Guo, R., Li, C., Niu, Y., et al., 2022. The fatigue performances of carbon fiber reinforced polymer composites – A review. Journal of Materials Research and Technology. 21, 4773–4789. DOI: https://doi.org/10.1016/j.jmrt.2022.11.053
[9] Chen, Y., Zhang, J., Li, Z., et al., 2023. Intelligent methods for optimization design of lightweight fiber-reinforced composite structures: A review and the state-of-the-art. Frontiers in Materials. 10, 1125328. DOI: https://doi.org/10.3389/fmats.2023.1125328
[10] Wong, J., Ryan, L., Kim, I.Y., 2018. Design optimization of aircraft landing gear assembly under dynamic loading. Structural and Multidisciplinary Optimization. 57(3), 1357–1375. DOI: https://doi.org/10.1007/s00158-017-1817-y
[11] Nagaraj, M.H., Reiner, J., Vaziri, R., et al., 2021. Compressive damage modeling of fiber-reinforced composite laminates using 2D higher-order layer-wise models. Composites Part B: Engineering. 215, 108753. DOI: https://doi.org/10.1016/j.compositesb.2021.108753
[12] Thuis, H.G.S.J., 2004. Composite landing gear components for aerospace applications. Proceedings of the 24th International Congress of the Aeronautical Sciences; 29 August–3 September 2004; Yokohama, Japan.
[13] Heuss, R., Müller, N., Van Sintern, W., et al., 2012. Lightweight, heavy impact: How carbon fiber and other lightweight materials will develop across industries and specifically in automotive. McKinsey & Company: New York, NY, USA. pp. 1–21.
[14] Slayton, R., Spinardi, G., 2016. Radical innovation in scaling up: Boeing's Dreamliner and the challenge of socio-technical transitions. Technovation. 47, 47–58. DOI: https://doi.org/10.1016/j.technovation.2015.08.004
[15] Kuśmierek, A., Galiński, C., Stalewski, W., 2023. Review of the hybrid gas-electric aircraft propulsion systems versus alternative systems. Progress in Aerospace Sciences. 142, 100925. DOI: https://doi.org/10.1016/j.paerosci.2023.100925
[16] Stamopoulos, A.G., Tserpes, K.I., Prucha, P., et al., 2016. Evaluation of porosity effects on the mechanical properties of carbon fiber-reinforced plastic unidirectional laminates by X-ray computed tomography and mechanical testing. Journal of Composite Materials. 50(15), 2087–2098. DOI: https://doi.org/10.1177/0021998315602049
[17] Koricho, E.G., Khomenko, A., Fristedt, T., et al., 2015. Innovative tailored fiber placement technique for enhanced damage resistance in notched composite laminate. Composite Structures. 120, 378–385. DOI: https://doi.org/10.1016/j.compstruct.2014.10.016
[18] Czél, G., Wisnom, M.R., 2013. Demonstration of pseudo-ductility in high performance glass/epoxy composites by hybridisation with thin-ply carbon prepreg. Composites Part A: Applied Science and Manufacturing. 52, 23–30. DOI: https://doi.org/10.1016/j.compositesa.2013.04.006
[19] Ashby, M., 2010. Materials Selection in Mechanical Design, 4th ed. Butterworth-Heinemann: Oxford, UK. DOI: https://doi.org/10.1016/C2009-0-25539-5
[20] Xiao, L., Li, X., Han, R., et al., 2024. Prediction of pseudo-ductility and structural optimization of nacre-inspired carbon fiber-reinforced polymer laminate. Polymer Composites. 45(4), 3243–3257.
[21] Fotouhi, M., Suwarta, P., Tabatabaeian, A., et al., 2022. Investigating the fatigue behavior of quasi-isotropic pseudo-ductile thin-ply carbon/glass epoxy hybrid composites. Composites Part A: Applied Science and Manufacturing. 163, 107206. DOI: https://doi.org/10.1016/j.compositesa.2022.107206
[22] Khan, T., Ali, M.A., Irfan, M.S., et al., 2023. Visualizing pseudo-ductility in carbon/glass fiber hybrid composites manufactured using infusible thermoplastic Elium® resin. Polymer Composites. 44(3), 1859–1876.
[23] Pujar, N.V., Nanjundaradhya, N.V., Sharma, R.S., 2017. Damping behavior of hybrid composites-A Review. Journals of Mechanical and Mechanics Engineering. 3(1), 1–14.
[24] Nagaraj, M.H., Reiner, J., Vaziri, R., et al., 2020. Progressive damage analysis of composite structures using higher-order layer-wise elements. Composites Part B: Engineering. 190, 107921. DOI: https://doi.org/10.1016/j.compositesb.2020.107921
[25] Goyal, V., Hoos, K.H., Lu, W.T., et al., 2023. Fail-Safe Prediction for Bonded Composite Structures Using Discrete Damage Modeling. AIAA SCITECH 2023 Forum; January 23–27, 2023; National Harbor, MD, USA. p.1318. DOI: https://doi.org/10.2514/6.2023-1318
[26] Vemuganti, S., Soliman, E., Reda Taha, M., 2020. 3D-Printed Pseudo Ductile Fiber-Reinforced Polymer (FRP) Composite Using Discrete Fiber Orientations. Fibers. 8(9), 53. DOI: https://doi.org/10.3390/fib8090053
[27] Xu, J., Geier, N., Shen, J., et al., 2023. A review on CFRP drilling: fundamental mechanisms, damage issues, and approaches toward high-quality drilling. Journal of Materials Research and Technology. 24, 9677–9707. DOI: https://doi.org/10.1016/j.jmrt.2023.05.023
[28] Agarwal, S., Pai, Y., Pai, D., et al., 2023. Assessment of ageing effect on the mechanical and damping characteristics of thin quasi-isotropic hybrid carbon-Kevlar/epoxy intraply composites and damping characteristics of thin intraply composites. Cogent Engineering. 10(1), 2235111. DOI: https://doi.org/10.1080/23311916.2023.2235111
[29] Hassani, S., Mousavi, M., Gandomi, A.H., 2022. Structural Health Monitoring in Composite Structures: A Comprehensive Review. Sensors. 22(1), 153. DOI: https://doi.org/10.3390/s22010153
[30] Kocoglu, H., Korkusuz, O.B., Ozzaim, P., et al., 2023. Solid particle erosion and scratch behavior of novel scrap carbon fiber/glass fabric/polyamide 6.6 hybrid composites. Polymer Composites. 44(10), 7197–7211. DOI: https://doi.org/10.1002/pc.27627
[31] Alam, P., Mamalis, D., Robert, C., et al., 2019. The fatigue of carbon fibre reinforced plastics - A review. Composites Part B: Engineering. 166, 555–579. DOI: https://doi.org/10.1016/j.compositesb.2018.12.016
[32] Koshima, S., Yoneda, S., Kajii, N., et al., 2019. Evaluation of strength degradation behavior and fatigue life prediction of plain-woven carbon-fiber-reinforced plastic laminates immersed in seawater. Composites Part A: Applied Science and Manufacturing. 127, 105645. DOI: https://doi.org/10.1016/j.compositesa.2019.105645
[33] Wang, Y., Zhang, X., Dong, X., et al., 2022. Multiaxial fatigue assessment for outer cylinder of landing gear by critical plane method. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 236(5), 993–1005. DOI: https://doi.org/10.1177/09544100211026042
[34] Jalalvand, M., Czél, G., Wisnom, M.R., 2015. Damage analysis of pseudo-ductile thin-ply UD hybrid composites – A new analytical method. Composites Part A: Applied Science and Manufacturing. 69, 83–93. DOI: https://doi.org/10.1016/j.compositesa.2014.11.006
[35] Fazlali, B., 2021. Damage Modelling of Uni-Directional Fiber Hybrid Composites [Ph.D. Thesis]. The Polytechnic University of Milan: Milan, Italy.
[36] Dress, G.A., Koricho, E.G., Regassa, Y., et al., 2024. Multi objective optimization methods for damage assessment of composite laminates: A review. Composite Structures. 327, 117655. DOI: https://doi.org/10.1016/j.compstruct.2023.117655
[37] Aditya, A., Sharma, K., Srinivas, G., 2020. A step towards safety: Material failure analysis of landing gear. Materials Today: Proceedings. 27(1), 402–409. DOI: https://doi.org/10.1016/j.matpr.2019.11.245
[38] Zhang, G., Liu, Y., Liu, J., et al., 2022. Causes and statistical characteristics of bridge failures: A review. Journal of Traffic and Transportation Engineering (English Edition). 9(3), 388–406. DOI: https://doi.org/10.1016/j.jtte.2021.12.003
[39] Karakale, V., Layas, F.M., Suleiman, R.E., 2023. Assessment and Rehabilitation of Damaged Buildings in Historic Benghazi City. Journal of Building Material Science. 5(2), 51–59. DOI: https://doi.org/10.30564/jbms.v5i2.6098
[40] Civera, M., Surace, C., 2022. Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years. Sensors. 22(4), 1627. DOI: https://doi.org/10.3390/s22041627
[41] Kharoufah, H., Murray, J., Baxter, G., et al., 2018. A review of human factors causations in commercial air transport accidents and incidents: From 2000–2016. Progress in Aerospace Sciences. 99, 1–13. DOI: https://doi.org/10.1016/j.paerosci.2018.03.002
[42] Pitropakis, I., 2015. Dedicated Solutions for Structural Health Monitoring of Aircraft Components. MTM KU Leuven: Leuven, Belgium. p.200.
[43] Lee, H., Lim, H.J., Chattopadhyay, A., 2021. Data-driven system health monitoring technique using autoencoder for the safety management of commercial aircraft. Neural Computing and Applications. 33, 3235–3250. DOI: https://doi.org/10.1007/s00521-020-05186-x
[44] Hameed, A., Zubair, O., Shams, T.A., et al., 2020. Failure analysis of a broken support strut of an aircraft landing gear. Engineering Failure Analysis. 117, 104847. DOI: https://doi.org/10.1016/j.engfailanal.2020.104847
[45] Korvesis, P., Besseau, S., Vazirgiannis, M., 2018. Predictive maintenance in aviation: Failure prediction from post-flight reports. Proceedings of the 34th IEEE International Conference on Data Engineering; 16–20 April 2018; Paris, France. pp. 1423–1434. DOI: https://doi.org/10.1109/ICDE.2018.00160
[46] Brühl, R., Fricke, H., Schultz, M., 2021. Air taxi flight performance modeling and application. Proceedings of the USA/Europe ATM R&D Seminar; 13–16 September, 2021; Online.
[47] Ciobanu, I., Drǎgus, L., Tigleanu, L., et al., 2019. Manufacturing of a landing gear using composite materials for an aerial target. Journal of Physics: Conference Series. 1297, 012038. DOI: https://doi.org/10.1088/1742-6596/1297/1/012038
[48] Kamocka, M., Zglinicki, M., Mania, R.J., 2016. Multi-method approach for FML mechanical properties prediction. Composites Part B: Engineering. 92, 322–330. DOI: https://doi.org/10.1016/j.compositesb.2016.01.014
[49] Mayall, A., Carolan, D., Fergusson, A., et al., 2017. A numerical method for predicting the mechanical properties of tightly packed syntactic foams. Proceedings of the 21st International Conference on Composite Materials; 20–25 August 2017; Xi'an, China.
[50] Li, S., Sitnikova, E., Liang, Y., et al., 2017. The Tsai-Wu failure criterion rationalised in the context of UD composites. Composites Part A: Applied Science and Manufacturing. 102, 207–217. DOI: https://doi.org/10.1016/j.compositesa.2017.08.007
[51] Dixit, A., Mali, H.S., 2013. Modeling techniques for predicting the mechanical properties of woven-fabric textile composites: A Review. Mechanics of Composite Materials. 49(1), 1–20. DOI: https://doi.org/10.1007/s11029-013-9316-8
[52] Kabir, H., Aghdam, M.M., 2019. A robust Bézier based solution for nonlinear vibration and post-buckling of random checkerboard graphene nano-platelets reinforced composite beams. Composite Structures. 212, 184–198. DOI: https://doi.org/10.1016/j.compstruct.2019.01.041
[53] Okabe, T., Takeda, N., Kamoshida, Y., et al., 2001. A 3D shear-lag model considering micro-damage and statistical strength prediction of unidirectional fiber-reinforced composites. Composites Science and Technology. 61(12), 1773–1787. DOI: https://doi.org/10.1016/S0266-3538(01)00079-3
[54] Bigaud, D., Hamelin, P., 1997. Mechanical properties prediction of textile-reinforced composite materials using a multiscale energetic approach. Composite Structures. 38(1–4), 361–371. DOI: https://doi.org/10.1016/S0263-8223(97)00071-8
[55] Gao, J., Yang, X., Huang, L.H., 2018. Numerical prediction of mechanical properties of rubber composites reinforced by aramid fiber under large deformation. Composite Structures. 201, 29–37. DOI: https://doi.org/10.1016/j.compstruct.2018.05.132
[56] Yu, X.G., Cui, J.Z., 2007. The prediction on mechanical properties of 4-step braided composites via two-scale method. Composites Science and Technology. 67(3–4), 471–480. DOI: https://doi.org/10.1016/j.compscitech.2006.08.028
[57] Lekou, D.L., Philippidis, T.P., 2008. Mechanical property variability in FRP laminates and its effect on failure prediction. Composites Part B: Engineering. 39(7–8), 1247–1256. DOI: https://doi.org/10.1016/j.compositesb.2008.01.004
[58] Xu, Y., Weng, H., Ju, X., et al., 2021. A method for predicting mechanical properties of composite microstructure with reduced dataset based on transfer learning. Composite Structures. 275, 114444. DOI: https://doi.org/10.1016/j.compstruct.2021.114444
[59] Zhang, Z., Klein, P., Friedrich, K., 2002. Dynamic mechanical properties of PTFE based short carbon fibre reinforced composites: experiment and artificial neural network prediction. Composites Science and Technology. 62(7–8), 1001–1009. DOI: https://doi.org/10.1016/S0266-3538(02)00036-2
[60] Abueidda, D.W., Almasri, M., Ammourah, R., et al., 2019. Prediction and optimization of mechanical properties of composites using convolutional neural networks. Composite Structures. 227, 111264. DOI: https://doi.org/10.1016/j.compstruct.2019.111264
[61] Huang, J.S., Liew, J.X., Liew, K.M., 2021. Data-driven machine learning approach for exploring and assessing mechanical properties of carbon nanotube-reinforced cement composites. Composite Structures. 267, 113917. DOI: https://doi.org/10.1016/j.compstruct.2021.113917
[62] Li, M., Zhang, H., Li, S., et al., 2022. Machine learning and materials informatics approaches for predicting transverse mechanical properties of unidirectional CFRP composites with microvoids. Materials & Design. 224, 111340. DOI: https://doi.org/10.1016/j.matdes.2022.111340
[63] Malley, S., Reina, C., Nacy, S., et al., 2022. Predictability of mechanical behavior of additively manufactured particulate composites using machine learning and data-driven approaches. Computers in Industry. 142, 103739. DOI: https://doi.org/10.1016/j.compind.2022.103739
[64] Shah, V., Zadourian, S., Yang, C., et al., 2022. Data-driven approach for the prediction of mechanical properties of carbon fiber reinforced composites. Materials Advances. 3(19), 7319–7327. DOI: https://doi.org/10.1039/d2ma00698g
[65] Tobon-Mejia, D.A., Medjaher, K., Zerhouni, N., et al., 2012. A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models. IEEE Transactions on Reliability. 61(2), 491–503. DOI: https://doi.org/10.1109/TR.2012.2194177
[66] Cheng, S., Pecht, M., 2009. A fusion prognostics method for remaining useful life prediction of electronic products. Proceedings of the IEEE International Conference on Automation Science and Engineering; 22–25 August 2009; Bangalore, India. pp. 102–107. DOI: https://doi.org/10.1109/COASE.2009.5234098
[67] Forrest, C., Wiser, D., 2017. Landing Gear Structural Health Monitoring (SHM). Procedia Structural Integrity. 5, 1153–1159. DOI: https://doi.org/10.1016/j.prostr.2017.07.025
[68] Kumar, V., Devi, K., 2025. Condition Assessment of Existing RCC Building Using Non-Destructive Testing. Journal of Building Material Science. 7(1), 62–72. DOI: https://doi.org/10.30564/jbms.v7i1.8160
[69] Naderpour, H., Nagai, K., Fakharian, P., et al., 2019. Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Composite Structures. 215, 69–84. DOI: https://doi.org/10.1016/j.compstruct.2019.02.048
[70] Zio, E., Maio, D.F., Stasi, M., 2010. A data-driven approach for predicting failure scenarios in nuclear systems. Annals of Nuclear Energy. 37(4), 482–491. DOI: https://doi.org/10.1016/j.anucene.2010.01.017
[71] Zhong, K., Han, M., Han, B., 2020. Data-driven based fault prognosis for industrial systems: a concise overview. IEEE/CAA Journal of Automatica Sinica. 7(2), 330–345. DOI: https://doi.org/10.1109/JAS.2019.1911804
[72] Cheng, S., Pecht, M., 2009. A fusion prognostics method for remaining useful life prediction of electronic products. Proceedings of the IEEE International Conference on Automation Science and Engineering; 22–25 August 2009; Bangalore, India. pp. 102–107. DOI: https://doi.org/10.1109/COASE.2009.5234098
[73] Amasyali, K., El-Gohary, N.M., 2018. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews. 81, 1192–1205. DOI: https://doi.org/10.1016/j.rser.2017.04.095
[74] Bourdeau, M., Zhai, X., Nefzaoui, E., et al., 2019. Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society. 48, 101533. DOI: https://doi.org/10.1016/j.scs.2019.101533
[75] Pugalenthi, K., Duong, P.L.T., Doh, J., et al., 2021. Online prognosis of bimodal crack evolution for fatigue life prediction of composite laminates using particle filters. Applied Sciences. 11(13), 6046. DOI: https://doi.org/10.3390/app11136046
[76] Pillai, P., Kaushik, A., Bhavikatti, S., et al., 2016. A hybrid approach for fusing physics and data for failure prediction. International Journal of Prognostics and Health Management. 7(4), 1–12. DOI: https://doi.org/10.36001/ijphm.2016.v7i4.2463
[77] Wei, Y., Zhang, X., Shi, Y., et al., 2018. A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews. 82, 1027–1047. DOI: https://doi.org/10.1016/j.rser.2017.09.108
[78] Dawood, T., Elwakil, E., Novoa, H.M., et al., 2020. Pressure data-driven model for failure prediction of PVC pipelines. Engineering Failure Analysis. 116, 104769. DOI: https://doi.org/10.1016/j.engfailanal.2020.104769
[79] Mohammadi, B., Rohanifar, M., Salimi-Majd, D., et al., 2017. Micromechanical prediction of damage due to transverse ply cracking under fatigue loading in composite laminates. Journal of Reinforced Plastics and Composites. 36(5), 377–395. DOI: https://doi.org/10.1177/0731684416676635
[80] Kumar, A., Singh, A.K., Shrivastva, A., et al., 2018. Failure prediction in incremental sheet forming based on Lemaitre damage model. Journal of Physics: Conference Series. 1063, 012152. DOI: https://doi.org/10.1088/1742-6596/1063/1/012152
[81] Lemaitre, J., 2022. A Continuous Damage Mechanics Model for Ductile Fracture. HAL Archives-Ouvertes: Paris, France. hal-03609806.
[82] Hashin, Z., 1986. Analysis of stiffness reduction of cracked cross-ply laminates. Engineering Fracture Mechanics. 25(5–6), 771–778. DOI: https://doi.org/10.1016/0013-7944(86)90040-8
[83] Nairn, J.A., 1995. Some new variational mechanics results on composite microcracking. Proceedings of the 10th International Conference on Composite Materials; 14–18 August 1995; Whistler, Canada. pp. 423–430.
[84] Kaddour, A., Hinton, M., Smith, P., et al., 2013. The background to the third world-wide failure exercise. Journal of Composite Materials. 47(20–21), 2417–2426. DOI: https://doi.org/10.1177/0021998313499475
[85] Feih, S., Shercliff, H.R., 2005. Composite failure prediction of single-L joint structures under bending. Composites Part A: Applied Science and Manufacturing. 36(3), 381–395. DOI: https://doi.org/10.1016/j.compositesa.2004.06.021
[86] Baptista, M., Sankararaman, S., Medeiros, I., et al., 2018. Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Computers & Industrial Engineering. 115, 41–53. DOI: https://doi.org/10.1016/j.cie.2017.10.033
[87] Li, Y., Liu, K., Foley, A.M., et al., 2019. Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review. Renewable and Sustainable Energy Reviews. 113, 109254. DOI: https://doi.org/10.1016/j.rser.2019.109254
[88] Khan, A., Kim, N., Shin, J.K., et al., 2019. Damage assessment of smart composite structures via machine learning: a review. JMST Advances. 1(1–2), 107–124. DOI: https://doi.org/10.1007/s42791-019-0012-2
[89] Jia, J., Davalos, J.F., 2006. An artificial neural network for the fatigue study of bonded FRP-Wood interfaces. Composite Structures. 74(1), 106–114. DOI: https://doi.org/10.1016/j.compstruct.2005.03.012
[90] Si, X.S., Wang, W., Hu, C.H., et al., 2011. Remaining useful life estimation - A review on the statistical data driven approaches. European Journal of Operational Research. 213(1), 1–14. DOI: https://doi.org/10.1016/j.ejor.2010.11.018
[91] Pecht, M., Jaai, R., 2010. A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability. 50(3), 317–323. DOI: https://doi.org/10.1016/j.microrel.2010.01.006
[92] Banerjee, P., Karpenko, O., Udpa, L., et al., 2018. Prediction of impact-damage growth in GFRP plates using particle filtering algorithm. Composite Structures. 194, 527–536. DOI: https://doi.org/10.1016/j.compstruct.2018.04.033
[93] Liu, X., Jia, Y., He, Z., et al., 2017. Hybrid residual fatigue life prediction approach for gear based on Paris law and particle filter with prior crack growth information. Journal of Vibroengineering. 19(8), 5908–5919. DOI: https://doi.org/10.21595/jve.2017.18327
[94] Pecht, M., Jaai, R., 2010. A prognostics and health management roadmap for information and electronics-rich systems. Microelectronics Reliability. 50(3), 317–323. DOI: https://doi.org/10.1016/j.microrel.2010.01.006
[95] Alsina, E.F., Chica, M., Trawiński, K., et al., 2018. On the use of machine learning methods to predict component reliability from data-driven industrial case studies. International Journal of Advanced Manufacturing Technology. 94, 2419–2433. DOI: https://doi.org/10.1007/s00170-017-1039-x
[96] Kazemian, M., Cherniaev, A., 2022. Prediction of Damage in Non-Crimp Fabric Composites Subjected to Transverse Crushing: A Comparison of Two Constitutive Models. Journal of Composites Science. 6(8), 224. DOI: https://doi.org/10.3390/jcs6080224
[97] Holmes, G., Sartor, P., Reed, S., et al., 2016. Prediction of landing gear loads using machine learning techniques. Structural Health Monitoring. 15(5), 568–582. DOI: https://doi.org/10.1177/1475921716651809
[98] Hart-Smith, L.J., 2004. Predictions of the original and truncated maximum-strain failure models for certain fibrous composite laminates. In: Hinton, M.J., Kaddour, A.S., Soden, P.D. (eds.). Failure criteria in fibre reinforced polymer composites: the world-wide failure exercise. Elsevier Science: Oxford, UK. pp. 179–218. DOI: https://doi.org/10.1016/B978-008044475-8/50009-3
[99] Getahun, M.A., Shitote, S.M., Abiero Gariy, Z.C., 2018. Artificial neural network-based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes. Construction and Building Materials. 190, 517–525. DOI: https://doi.org/10.1016/j.conbuildmat.2018.09.097
[100] Rahimi, N., Musa, M., Hussain, A.K., et al., 2012. Finite element implementations to predict the failure of composite laminates under uniaxial tension. Advanced Materials Research. 499, 20–24. DOI: https://doi.org/10.4028/www.scientific.net/AMR.499.20
[101] Seon, G., 2014. Failure Predictions for Carbon/Epoxy Tape Laminates with Wavy Plies. Journal of Composite Materials. 44(1), 95–112. DOI: https://doi.org/10.1177/0021998309345352
[102] Cuntze, R.G., Freund, A., 2004. The predictive capability of failure mode concept-based strength criteria for multidirectional laminates. Composites Science and Technology. 64(3–4), 343–377. DOI: https://doi.org/10.1016/S0266-3538(03)00218-5
[103] Peng, Z., Wang, X., Wu, Z., 2020. A bundle-based shear-lag model for tensile failure prediction of unidirectional fiber-reinforced polymer composites. Materials & Design. 196, 109103. DOI: https://doi.org/10.1016/j.matdes.2020.109103
[104] Khan, S.Z., Suman, S., Pavani, M., et al., 2016. Prediction of the residual strength of clay using functional networks. Geoscience Frontiers. 7(1), 67–74. DOI: https://doi.org/10.1016/j.gsf.2014.12.008
[105] Alessio, R.P., Andre, N.M., Goushegir, S.M., et al., 2020. Prediction of the mechanical and failure behavior of metal-composite hybrid joints using cohesive surfaces. Materials Today Communications. 24, 101205. DOI: https://doi.org/10.1016/j.mtcomm.2020.101205
[106] Ghalehbandi, S.M., Biglari, F., 2020. Predicting damage and failure under thermomechanical fatigue in hot forging tools. Engineering Failure Analysis. 113, 104545. DOI: https://doi.org/10.1016/j.engfailanal.2020.104545
[107] Aranda, M.T., Leguillon, D., 2023. Prediction of failure of hybrid composites with ultra-thin carbon/epoxy layers using the Coupled Criterion. Engineering Fracture Mechanics. 281, 109053. DOI: https://doi.org/10.1016/j.engfracmech.2023.109053
[108] Zhang, K., Badreddine, H., Yue, Z., et al., 2021. Failure prediction of magnesium alloys based on improved CDM model. International Journal of Solids and Structures. 217–218, 155–177. DOI: https://doi.org/10.1016/j.ijsolstr.2021.01.013
[109] Yang, C., Kim, Y., Ryu, S., et al., 2020. Prediction of composite microstructure stress-strain curves using convolutional neural networks. Materials & Design. 189, 108509. DOI: https://doi.org/10.1016/j.matdes.2020.108509
[110] Cheng, X., Zhang, Q., Zhang, J., et al., 2019. Parameters prediction of cohesive zone model for simulating composite/adhesive delamination in hygrothermal environments. Composites Part B: Engineering. 166, 710–721. DOI: https://doi.org/10.1016/j.compositesb.2019.03.002
[111] Sun, L., Tie, Y., Hou, Y., et al., 2020. Prediction of failure behavior of adhesively bonded CFRP scarf joints using a cohesive zone model. Engineering Fracture Mechanics. 228, 106897. DOI: https://doi.org/10.1016/j.engfracmech.2020.106897
[112] Deng, N.F., Qiao, L., Li, Q.W., et al., 2021. A Method to Predict Rock Fracture with Infrared Thermography Based on Heat Diffusion Analysis. Geofluids. 2021, 6669016. DOI: https://doi.org/10.1155/2021/6669016
[113] Zhang, W., Han, G., Wang, J., et al., 2019. A BP Neural Network Prediction Model Based on Dynamic Cuckoo Search Optimization Algorithm for Industrial Equipment Fault Prediction. IEEE Access. 7, 11736–11746. DOI: https://doi.org/10.1109/ACCESS.2019.2892729
[114] Ahn, J., He, E., Chen, L., et al., 2017. Prediction and measurement of residual stresses and distortions in fibre laser welded Ti-6Al-4V considering phase transformation. Materials & Design. 115, 441–457. DOI: https://doi.org/10.1016/j.matdes.2016.11.078
[115] Tura, A.D., Lemu, H.G., Mamo, H.B., 2022. Experimental Investigation and Prediction of Mechanical Properties in a Fused Deposition Modeling Process. Crystals. 12(6), 844. DOI: https://doi.org/10.3390/cryst12060844
[116] Kayiran, H.F., 2024. Investigation of Radial and Tangential Stresses Occurring in Epoxy (T300) Material Disc with Different Methods. Journal of Mechanical Materials and Mechanics Research. 7(1), 1–7. DOI: https://doi.org/10.30564/jmmmr.v7i1.6050
[117] Strozzi, M., 2022. Open Issues in Continuum Modeling of Carbon Nanotubes. Journal of Mechanical Materials and Mechanics Research. 5(1), 18–20. DOI: https://doi.org/10.30564/jmmmr.v5i1.4693
[118] Huang, Y., Wang, X., 2023. Challenges and Trends for Multifunctional Materials. Journal of Building Material Science. 5(1), 17–19. DOI: https://doi.org/10.30564/jbms.v5i1.5521
[119] Zhou, T., Zhang, G., Cai, Y., 2024. Research on Aircraft Engine Bearing Clearance Fault Diagnosis Method Based on MFO-VMD and GMFE. Journal of Mechanical Materials and Mechanics Research. 7(1), 1–12. DOI: https://doi.org/10.30564/jmmmr.v7i1.7906
[120] Makeev, A., Seon, G., Lee, E., 2010. Failure predictions for carbon/epoxy tape laminates with wavy plies. Journal of Composite Materials. 44(1), 95–112. DOI: https://doi.org/10.1177/0021998309345352
[121] Tabesh, M., Soltani, J., Farmani, R., et al., 2009. Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling. Journal of Hydroinformatics. 11(1), 1–17. DOI: https://doi.org/10.2166/hydro.2009.008
[122] Lu, C., Wang, Y., Ragulskis, M., et al., 2016. Fault diagnosis for rotating machinery: A method based on image processing. PLoS ONE. 11(10), e0164111. DOI: https://doi.org/10.1371/journal.pone.0164111
[123] Landis, E.N., Zhang, T., Nagy, E.N., et al., 2007. Cracking, damage and fracture in four dimensions. Materials and Structures. 40(4), 357–364. DOI: https://doi.org/10.1617/s11527-006-9145-5
[124] Townsend, J., Meyers, C., Ortega, R., et al., 1993. Review of the probabilistic failure analysis methodology and other probabilistic approaches for application in aerospace structural design. NASA Technical Memorandum M-734, 1 November 1993.
[125] Suhir, E., 2013. Predicted reliability of aerospace electronics: Application of two advanced probabilistic concepts. Proceedings of the 2013 IEEE Aerospace Conference; 2–9 March 2013; Big Sky, MT, USA. pp. 1–13.
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Dress, G. A., Regassa, Y., & Ermias Gebrekidan Koricho. (2025). Model-Based Mechanical Property and Structural Failure Prediction of Pseudo Ductile Hybrid Composite. Journal of Building Material Science, 7(2), 1–21. https://doi.org/10.30564/jbms.v7i2.8642
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Copyright © 2025 Genetu Amare Dress, Yohannes Regassa, Ermias Gebrekidan Koricho
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
