Optimal Structural Design in the USSR

1989 ◽  
Vol 42 (2) ◽  
pp. 27-37 ◽  
Author(s):  
Mark I. Reitman

Studies in structural optimization in Russia began more than a century ago and initially satisfied the needs of railroad engineering. Later Soviet academic researchers and engineers considered the optimum design of compressed and twisted bars, beams, arches, rigid frames, plates, shells, and various 3D structures under single and multiple statical, dynamical, and moving loads. Some new formulations of the optimization problems have been introduced and solved using classical and new mathematical methods. Several hundred contributions are briefly covered with references to 50 bibliographical sources.

1983 ◽  
Vol 50 (4b) ◽  
pp. 1139-1151 ◽  
Author(s):  
N. Olhoff ◽  
J. E. Taylor

This paper presents a survey of the field of optimal structural design, with the main emphasis laid on fundamental aspects. The basic concepts for structural optimization problems are outlined, and we discuss the mathematical formulation and the characteristic properties and features of such problems for both discrete and continuum structures. A picture of the present status of the field is given, and we present an assessment of areas that are currently of special importance and undergoing rapid development. Furthermore, we identify some types of problems that require particular care in their formulation, and we indicate issues for future research.


Author(s):  
Jiantao Liu ◽  
Hae Chang Gea ◽  
Ping An Du

Robust structural design optimization with non-probabilistic uncertainties is often formulated as a two-level optimization problem. The top level optimization problem is simply to minimize a specified objective function while the optimized solution at the second level solution is within bounds. The second level optimization problem is to find the worst case design under non-probabilistic uncertainty. Although the second level optimization problem is a non-convex problem, the global optimal solution must be assured in order to guarantee the solution robustness at the first level. In this paper, a new approach is proposed to solve the robust structural optimization problems with non-probabilistic uncertainties. The WCDO problems at the second level are solved directly by the monotonocity analysis and the global optimality is assured. Then, the robust structural optimization problem is reduced to a single level problem and can be easily solved by any gradient based method. To illustrate the proposed approach, truss examples with non-probabilistic uncertainties on stiffness and loading are presented.


2001 ◽  
Vol 01 (01) ◽  
pp. 105-123 ◽  
Author(s):  
MANOLIS PAPADRAKAKIS ◽  
NIKOS D. LAGAROS ◽  
VAGELIS PLEVRIS

The objective of this paper is to perform structural optimization under seismic loading. Combinatorial optimization methods and in particular algorithms based on Evolution Strategies are implemented for the solution of large-scale structural optimization problems under seismic loading. In this work the efficiency of a rigorous approach in treating dynamic loading is investigated and compared with a simplified dynamic analysis in the framework of finding the optimum design of structures with minimum weight. In this context a number of accelerograms are produced from the elastic design response spectrum of the region. These accelerograms constitute the multiple loading conditions under which the structures are optimally designed. This approach is compared with an approximate design approach based on simplifications adopted by the seismic codes. The results obtained for a characteristic test problem indicate a substantial improvement in the final design when the proposed optimization procedure is implemented.


2013 ◽  
Vol 397-400 ◽  
pp. 1021-1024 ◽  
Author(s):  
Tien Tung Chung ◽  
Qi Hong Jiang ◽  
Jia Rong Zhu

This paper proposes a new approximation method called Enhanced Two-Point Exponential Approximation (ETPEA), which is derived based on Two-Point Exponential Approximation (TPEA). In TPEA, when the exponents of intervening variables cannot be calculated by fitting the sensitivities of the previous design point, the accuracy of TPEA is decreased. In such cases, ETPEA uses a substitution function to construct the approximate function, and the optimum result can be improved. Some typical structural optimization problems are tested, and the results indicate that the optimum design can be found accurately and quickly with the new approximation method. It is verified that the new approximation method is also efficient for large structural optimization problems.


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