150654 20251017144628.0 doi 10.1016/j.probengmech.2022.103250 sideral 128452 ART-2022-128452 eng Rens, Marlon T.H. Markov chain based non-linear fatigue damage accumulation model 2022 Access copy available to the general public Unrestricted Methodology to model fatigue damage accumulation of Fiber-Reinforced Plastics (FRP) composite laminates subjected to variable amplitude spectrum loading is proposed in this paper. Markov chain based Probability Transition Matrices (PTM) are modeled for each of the different block load levels, applying matrix multiplication to combine the PTM''s, resulting in a single probability mass function for the full variable amplitude load spectrum. Variable amplitude block load experimental data was used to demonstrate and validate the methodology and compare against other for Fiber-Reinforced Plastics commonly applied linear and non-linear damage accumulation and strength degradation based fatigue damage accumulation models. PTM''s modeled directly from experimental data for the different variable amplitude load levels were obtained to calculate the probability of failure for the full load spectrum. When applying Markov chain based probability transition matrices together with matrix multiplications, both load sequence effects as well as non-linear fatigue damage growth behavior can be accurately modeled, resulting that the model predicts failure probabilities very close to the actual experimental results. © 2022 info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 2.6 2022 STATISTICS & PROBABILITY 20 / 125 = 0.16 2022 Q1 T1 ENGINEERING, MECHANICAL 62 / 136 = 0.456 2022 Q2 T2 MECHANICS 60 / 137 = 0.438 2022 Q2 T2 0.845 2022 Aerospace Engineering 2022 Q1 Civil and Structural Engineering 2022 Q1 Condensed Matter Physics 2022 Q1 Statistical and Nonlinear Physics 2022 Q1 Nuclear Energy and Engineering 2022 Q1 Ocean Engineering 2022 Q1 Mechanical Engineering 2022 Q1 4.1 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Bea-Bragado, Ignacio Bea, José Antonio Universidad de Zaragoza (orcid)0000-0002-9417-2705 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 68 (2022), 103250 [10 pp.] Probab. eng. mech. PROBABILISTIC ENGINEERING MECHANICS 0266-8920 1601642 http://zaguan.unizar.es/record/150654/files/texto_completo.pdf Versión publicada 2756430 http://zaguan.unizar.es/record/150654/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:150654 articulos driver 2025-10-17-14:24:58 ARTICLE