1、英文原文Optimize the reliability of mechanical structure designIt is now generally recognized that structural and mechanical problems are nondeterministic and, consequently, engineering optimum design must cope with un-certainties，Reliability technology provides tools for formal assessment and analysis
2、of such uncertainties，Thus, the combination of reliability-based design procedure sand optimization promises to provide a practical optimum design solution, i，e，, a de-sign having an optimum balance between cost and risk， However, reliabilty-based structural optimization programs have not enjoyed th
3、e name popularity as their deterministic counterparts， Some reasons for this are suggested， First, reliability analysis can be complicated even for simple systems， There are various methods for handling the uncertainty in similar situations (e，g，, first order second moment methods, full distribution
4、 methods)， Lacking a single method, individuals are likely to adopt separate strategies for handling the uncertainty in their particular problems， This suggests the possibility of different reliability predictions in similar structural design situations， Then, there are diverging opinions on many ba
5、sic issues, from the very definition of reliability-based optimization, including the definition of the optimum solution, the objective function and the constraints, to its application in structural design practice， There is a need to formally consider these itess in the merger of present structural
6、 optimization research with reliability-based design philosophy。In general, an optimization problem can be stated as follows,Minimize （1.1）subject to the constraints （1.2） （1.3）where X is an-dimensional vector called the design vector， f(X) is called the objective function and, k(X) and i(X) are, re
7、spectively, the inequality and equality constraints， The number of variables n and the number of constraints， L need not be related in any way， Thus, L could be less than, equal to or greater than n in a given mathematical programming problem， In some problems, the value of L might be zero which mea
8、ns there are no constraints on the problem， Such type of problems are called unconstrained optimization problems， Those problems for which L is not equal to zero are known as constrained optimization problems。 Traditionally the designer assumes the loading on an element and the strength of that elem
9、ent to be a single valued characteristic or design value， Perhaps it is equal to some maximum (or minimum) anticipated or nominal value， Safety is assured by introducing a factor of safety, greater than one, usually applied as a reduction factor to strength。Probabilistic design is propose: as an alt
10、ernative to the conventional approach with the promise of producing better engineered systems， each factor in the design process can be defined and treated as a random variable， Using method-ology from probabilistic theory, the designer defines the appropriate limit state and computes the probabilit
11、y of failure P of the element， The basic design requirement is that，where p f is the maximum allowable probability of failure。Advantages of adopting the probabilistic design approach are well documented (Wu, 1984)， Basically the arguments for probabilistic design center around the fact that, relativ
12、e to the conventional approach, a) risk is a more meaningful index of structural performance, and b) a reliability approach to design of a sys-tom can tend to produce an optimum design by ensuring a uniform risk in all components。 Optimization, which may be considered a component of operations resea
13、rch, is the process of obtaining the best result by finding conditions that produce the maximum or minimum value of a function， Table 1，1 illustrates area of operations research。 Mathematical programming techniques, also known as optimization methods, are useful in finding the minimum (or maximum) o
14、f a function of several variables under a prescribed set of constraints， Rao (1979) presented a definition and description of some of the various methods of mathematical programming， Stochas-tic process techniques can be used to analyze problems which are described by a set of random variables， Stat
15、istical methods enable one to analyze the experimental data and build empirical models to obtain the most accurate representations of physical behavior。 In spite of these early contributions, very little progress was made until the middle of the twentieth Gentry, when high-speed digital computers ma
16、de the implementation of optimization procedures possible and stimulate, d further research on new methods， Spectacular advances followed, producing a m;sssive literature on optimization techniques， This advancement also resulted in the emergence of several well-defined new areas in optimization theory。It is interesting to note that major dev
