Mathematical Problems in Engineering
Volume 2008 (2008), Article ID 512343, 26 pages
Research Article

Probabilistic and Fuzzy Arithmetic Approaches for the Treatment of Uncertainties in the Installation of Torpedo Piles

Denise Margareth Kazue Nishimura Kunitaki,1,2 Beatriz Souza Leite Pires de Lima,2 Alexandre Gonçalves Evsukoff,2 and Breno Pinheiro Jacob1,2

1Laboratory of Computer Methods and Offshore Systems (LAMCSO), Civil Engineering Department, COPPE/UFRJ-Postgraduate Institute of the Federal University of Rio de Janeiro, 21945-970 Rio de Janeiro, RJ, Brazil
2COPPE/UFRJ, Civil Engineering Department, Centro de Tecnologia Bloco B sala B-101, Cidade Universitária, Ilha do Fundão, Caixa Postal 68.506, 21945-970 Rio de Janeiro, RJ, Brazil

Received 2 December 2007; Accepted 27 March 2008

Academic Editor: Paulo Gonçalves

Copyright © 2008 Denise Margareth Kazue Nishimura Kunitaki 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.


The “torpedo” pile is a foundation system that has been recently considered to anchor mooring lines and risers of floating production systems for offshore oil exploitation. The pile is installed in a free fall operation from a vessel. However, the soil parameters involved in the penetration model of the torpedo pile contain uncertainties that can affect the precision of analysis methods to evaluate its final penetration depth. Therefore, this paper deals with methodologies for the assessment of the sensitivity of the response to the variation of the uncertain parameters and mainly to incorporate into the analysis method techniques for the formal treatment of the uncertainties. Probabilistic and “possibilistic” approaches are considered, involving, respectively, the Monte Carlo method (MC) and concepts of fuzzy arithmetic (FA). The results and performance of both approaches are compared, stressing the ability of the latter approach to efficiently deal with the uncertainties of the model, with outstanding computational efficiency, and therefore, to comprise an effective design tool.