Knowledge-based global optimization of cold-formed steel columns
Knowledge-based global optimization of cold-formed steel columns
复制标题
基于知识的冷弯钢柱全局优化
DOI：
10.1016/j.tws.2004.01.001
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发表时间：
2004
影响因子：
6.4
通讯作者：
B. Schafer
中科院分区：
文献类型：
--
作者：
H. Liu;T. Igusa;B. Schafer
冷弯型钢构件的截面形状难以优化，因为这种构件在屈曲荷载下的非线性行为。传统的基于梯度的优化方案，采用确定性的设计规范的目标函数，是低效的，严重限制了他们的能力，搜索的全部解决方案空间的成员横截面。在这里，一个新的全局优化方法，是非常适合优化这样的横截面。这种方法有两个显著的特点：（1）它是在一个低维的基于专家的特征空间，而不是截面参数的高维设计空间;（2）它使用直接强度法（DSM）的目标函数的数值实现。通过使用贝叶斯分类树，定义了基于专家的特征空间的最重要的坐标;这些坐标是低维的，并且是根据提供对结构行为的洞察的特征。然后，分类树被用来有效地生成候选成员的横截面原型，随后细化局部优化。三个结构上可区分的长度制度的优化结果，以提供所提出的方案的概念验证。结果表明，基于专家的特征空间及其相关的分类树可以有效地封装在设计优化过程中获得的知识，并可以随后作为相关的设计优化问题的起始框架。从本质上讲，这是一种高效的知识转移机制，在大多数优化方案中都没有。薄壁构件的优化将大大受益于更灵活和通用的设计方法（例如，DSM）和新颖的、新兴的优化方案，例如本文所提出的方案。
Cold-formed steel member cross-section shapes are difficult to optimize because of the nonlinear behavior of such members under buckling loads. Traditional gradient-based optimization schemes, employing deterministic design specifications for the objective function, are inefficient and severely limited in their ability to search the full solution space of member cross-sections. Herein, a new global optimization approach that is well suited for optimization of such cross-sections is introduced. There are two distinguishing characteristics of this approach: (1) it operates within a low-dimensional expert-based feature space rather than the high-dimensional design space of cross-section parameters; and (2) it uses a numerical implementation of the direct strength method (DSM) for the objective function. Through the use of Bayesian classification trees, the most significant coordinates of the expert-based feature space are defined; these coordinates are of low dimension and are in terms of features which provide insight into structural behavior. The classification trees are then used to efficiently generate candidate member cross-section prototypes for subsequent refined local optimization. Optimization results are presented for three structurally distinguishable length regimes to provide proof-of-concept of the proposed scheme. It is demonstrated that an expert-based feature space and its associated classification tree can effectively encapsulate the knowledge gained in the design optimization process and can be subsequently used as a starting framework for related design optimization problems. This is, in essence, a highly efficient knowledge transfer mechanism that is absent in most optimization schemes. Optimization of thin-walled members stands to benefit greatly from the combination of more flexible and general design methodologies (e.g., the DSM) and novel, emerging, optimization schemes such as the one presented herein.