Numerical collapse simulation of large-scale structural systems using an optimization-based algorithm

被引:56
|
作者
Sivaselvan, Mettupalayam V. [1 ]
Lavan, Oren [2 ]
Dargush, Gary F. [3 ]
Kurino, Haruhiko [4 ]
Hyodo, Yo [4 ]
Fukuda, Ryusuke [4 ]
Sato, Kiochi [4 ]
Apostolakis, Georgios [3 ]
Reinhorn, Andre M. [3 ]
机构
[1] Univ Colorado, Dept Civil Environm & Architectural Engn, Boulder, CO 80309 USA
[2] Technion Israel Inst Technol Technion City, Fac Civil Environm Engn, IL-32000 Haifa, Israel
[3] SUNY Buffalo, Dept Civil Struct & Environm Engn, Buffalo, NY 14260 USA
[4] Kajima Corp, Tokyo 1078502, Japan
来源
关键词
nonlinear dynamic simulation; convex optimization; generalized standard material; stored energy; dissipation potential; mixed Lagrangian formalism; POINT PROJECTION ALGORITHMS; ELASTOPLASTICITY; FORMULATION;
D O I
10.1002/eqe.895
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
A new algorithm for nonlinear dynamic simulation of structures is presented. The algorithm is based on a mixed Lagrangian approach described by Sivaselvan and Reinhorn (J. Eng. Mech. (ASCE) 2006; 132(8):795-805). The algorithm developed in this paper is for the simulation of large-scale structural systems. The algorithm is applicable to a wide class of structural systems whose constituent material or component behavior can be derived from a stored energy function and a dissipation potential. The algorithm is based on the fact that for such systems, when using a certain class of time-discretization schemes to numerically compute the system response, the incremental problem of computing the system state at the next sample time knowing the current state and the input is one of convex minimization. As a result, the algorithm possesses excellent convergence characteristics. It is also applicable to geometric nonlinear problems. The implementation of the algorithm is described, and its applicability to the collapse analysis of large-scale structures is demonstrated through numerical examples. Copyright (C) 2009 John Wiley & Sons, Ltd.
引用
收藏
页码:655 / 677
页数:23
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