Abstract
This work presents computational strategies used in an im- plementation of the probabilistic discrete cracking model for concrete of Rossi suitable to parallel vector processor (PVP). The computational strategies used for each realization, within the framework of Monte Carlo simulation, are the inexact Newton method to solve the highly nonlinear problem and element-by-element (EBE) iterative strategies considering that nonlinear behavior is restricted to interface elements. The simulation of a direct tension test is used to illustrate the influence of adaptive inexact Newton strategy in code performance on a CRAY T90.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Rossi P., Richer S.: Numerical modeling of concrete cracking based on a stochastic approach. Materials and Structures, 20 (119), (1987) 334–337.
Rossi P., Wu X., Maou F. le, and Belloc A.: Scale effect on concrete in tension. Materials and Structures, 27 (172), (1994) 437–444.
Rossi P., Ulm F.-J., and Hachi F.: Compressive behavior of concrete: physical mechanisms and modeling. Journal of Engineering Mechanics ASCE, 122 (11), (1996), 1038–1043.
Rossi P. and Ulm F.-J.: Size effects in the biaxial behavior of concrete: physical mechanisms and modeling. Materials and Structures, 30 (198), (1997) 210–216.
Paz C. N. M., Development and Implementation Probabilistic Model for 2D and 3D Discrete Cracking Concrete in Parallel Computing, D.Sc. Thesis, COPPE/UFRJ, the Graduate Institute of Federal University of Rio de Janeiro, Brazil (2000) [in Portuguese].
Press W. Teukolski H., S., Vetterling W.T. and Flannery B.: Numerical Recipes, Cambridge University Press (1992).
Fairbairn E. M. R., Paz C. N. M., Ebecken N. F. F., and Ulm F-J., Use of neural network for fitting of probabilistic scaling model parameter, Int. J. Fracture, 95, (1999), 315–324.
Fairbairn E. M. R., Ebecken N. F. F., Paz C. N. M., and Ulm F-J.: Determination of probabilistic parameters of concrete: solving the inverse problem by using artificial neural networks, Computers and Structures, 78, (2000) 497–503.
Kelley C. T.: Iterative Methods for Linear and Nonlinear Equations. Frontiers in applied mathematics, SIAM. Society for Industrial and Applied Mathematics, Philadelphia, (1995).
Hughes T. J. R., Ferenez R. M., Hallquist J. O.: Large-Scale Vectorized Implicit Calculation in Solid Mechanics on a CRAY X-MP/48 Utilizing EBE Preconditionated Conjugate Gradients Computer Methods in applied Mechanics and Engineering, 61, (1987) 2115–2248.
Papadrakakis M.: Solving Large-Scale Problems in Mechanics: The Development and Application of Computational Solution, Editor, M. Papadrakakis, John Wiley and Sons, (1993).
Li Q. and Ansari F.: High Concrete in Uniaxial Tension, ACI Material J. 97-(1), (2000) 49–57.
Coutinho A. L. G. A., Martins M. A. D., Alves J. L. D., Landau L., and Moraes A.: Edge-based finite element techniques for nonlinear solid mechanics problems, Int. J. for Numerical Methods in Engineering, 50 (9), (2001) 2050–2068.
Fairbairn E. M. R., Debeux V.J.C., Paz C. N. M., and Ebecken N. F. F.: Application of probabilistic Approach to the Analysis of gravity Dam Centrifuge Test, 8th ASCE Specialty Conference on Probabilistic Mechanics and Structural Reliability (2000) PMC 2000–2261.
Kelley C. T.: Iterative methods for optimization. Frontiers in applied mathematics, SIAM Society for Industrial and Applied Mathematics, Philadelphia, (1999).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paz, C.N.M., Martha, L.F., Alves, J.L.D., Fairbairn, E.M.R., Ebecken, N.F.F., Coutinho, A.L.G.A. (2003). Parallel Implementation for Probabilistic Analysis of 3D Discrete Cracking in Concrete. In: Palma, J.M.L.M., Sousa, A.A., Dongarra, J., Hernández, V. (eds) High Performance Computing for Computational Science — VECPAR 2002. VECPAR 2002. Lecture Notes in Computer Science, vol 2565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36569-9_6
Download citation
DOI: https://doi.org/10.1007/3-540-36569-9_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00852-1
Online ISBN: 978-3-540-36569-3
eBook Packages: Springer Book Archive