000150654 001__ 150654 000150654 005__ 20251017144628.0 000150654 0247_ $$2doi$$a10.1016/j.probengmech.2022.103250 000150654 0248_ $$2sideral$$a128452 000150654 037__ $$aART-2022-128452 000150654 041__ $$aeng 000150654 100__ $$aRens, Marlon T.H. 000150654 245__ $$aMarkov chain based non-linear fatigue damage accumulation model 000150654 260__ $$c2022 000150654 5060_ $$aAccess copy available to the general public$$fUnrestricted 000150654 5203_ $$aMethodology 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 000150654 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 000150654 590__ $$a2.6$$b2022 000150654 591__ $$aSTATISTICS & PROBABILITY$$b20 / 125 = 0.16$$c2022$$dQ1$$eT1 000150654 591__ $$aENGINEERING, MECHANICAL$$b62 / 136 = 0.456$$c2022$$dQ2$$eT2 000150654 591__ $$aMECHANICS$$b60 / 137 = 0.438$$c2022$$dQ2$$eT2 000150654 592__ $$a0.845$$b2022 000150654 593__ $$aAerospace Engineering$$c2022$$dQ1 000150654 593__ $$aCivil and Structural Engineering$$c2022$$dQ1 000150654 593__ $$aCondensed Matter Physics$$c2022$$dQ1 000150654 593__ $$aStatistical and Nonlinear Physics$$c2022$$dQ1 000150654 593__ $$aNuclear Energy and Engineering$$c2022$$dQ1 000150654 593__ $$aOcean Engineering$$c2022$$dQ1 000150654 593__ $$aMechanical Engineering$$c2022$$dQ1 000150654 594__ $$a4.1$$b2022 000150654 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000150654 700__ $$aBea-Bragado, Ignacio 000150654 700__ $$0(orcid)0000-0002-9417-2705$$aBea, José Antonio$$uUniversidad de Zaragoza 000150654 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est. 000150654 773__ $$g68 (2022), 103250 [10 pp.]$$pProbab. eng. mech.$$tPROBABILISTIC ENGINEERING MECHANICS$$x0266-8920 000150654 8564_ $$s1601642$$uhttps://zaguan.unizar.es/record/150654/files/texto_completo.pdf$$yVersión publicada 000150654 8564_ $$s2756430$$uhttps://zaguan.unizar.es/record/150654/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000150654 909CO $$ooai:zaguan.unizar.es:150654$$particulos$$pdriver 000150654 951__ $$a2025-10-17-14:24:58 000150654 980__ $$aARTICLE