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BioMed Research International
Volume 2015 (2015), Article ID 825402, 7 pages
Research Article

Dental Implants Fatigue as a Possible Failure of Implantologic Treatment: The Importance of Randomness in Fatigue Behaviour

1Department of Stomatology, Rey Juan Carlos University, C/ Tulipán s/n, Móstoles, 28933 Madrid, Spain
2Applied Modelling and Instrumentation Group, Aragón Institute of Engineering Research, University of Zaragoza, C/ Mariano Esquillor s/n, 50018 Zaragoza, Spain
3Department of Biomedical and Neuromotor Sciences, Unit of Periodontology and Implantology, University of Bologna, Via Zamboni 33, 40126 Bologna, Italy
4Aragón Institute of Engineering Research, University of Zaragoza, C/ Mariano Esquillor s/n, 50018 Zaragoza, Spain

Received 22 June 2015; Revised 17 August 2015; Accepted 1 September 2015

Academic Editor: David M. Dohan Ehrenfest

Copyright © 2015 María Prados-Privado et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. To show how random variables concern fatigue behaviour by a probabilistic finite element method. Methods. Uncertainties on material properties due to the existence of defects that cause material elastic constant are not the same in the whole dental implant the dimensions of the structural element and load history have a decisive influence on the fatigue process and therefore on the life of a dental implant. In order to measure these uncertainties, we used a method based on Markoff chains, Bogdanoff and Kozin cumulative damage model, and probabilistic finite elements method. Results. The results have been obtained by conventional and probabilistic methods. Mathematical models obtained the same result regarding fatigue life; however, the probabilistic model obtained a greater mean life but with more information because of the cumulative probability function. Conclusions. The present paper introduces an improved procedure to study fatigue behaviour in order to know statistics of the fatigue life (mean and variance) and its probability of failure (fatigue life versus probability of failure).