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Development of a GA-based method for reliability-based optimization of structures with discrete and continuous design variables using OpenSees and Tcl

机译:开发基于GA的方法,以使用OpenSees和Tcl对具有离散和连续设计变量的结构进行基于可靠性的优化

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This paper develops a method based on genetic algorithm (GA) for reliability-based optimization (RBO) of structures through a software application. This method employs GA as an optimization technique in which reliability constraints of RBO problem are evaluated using finite element reliability analysis modules offered by OpenSees. Since Tcl is a programmable and interpreted language, the proposed method is implemented using Tcl scripting language together with OpenSees software framework. Developing the RBO method through this software application leads to the important advantage of utilizing new advanced structural reliability methods implemented in OpenSees without compiling the source code. In the RBO problem, the cost of design is defined as an objective function and a number of design requirements are defined as reliability constraints. This method is also able to deal with RBO problems with discrete and continuous design variables. Six numerical examples are provided to investigate the strengths of the method through the software application. Results illustrate the advantages of this application in RBO of structures. Since OpenSees is used for finite element reliability analysis, this application is readily extendable to nonlinear structures.
机译:本文通过软件应用开发了一种基于遗传算法(GA)的基于可靠性的结构优化(RBO)方法。该方法采用遗传算法作为一种优化技术,该技术使用OpenSees提供的有限元可靠性分析模块评估RBO问题的可靠性约束。由于Tcl是一种可编程和解释性语言,因此使用Tcl脚本语言和OpenSees软件框架可以实现所提出的方法。通过该软件应用程序开发RBO方法具有重要的优势,即利用OpenSees中实现的新的高级结构可靠性方法而无需编译源代码。在RBO问题中,设计成本定义为目标函数,而许多设计要求定义为可靠性约束。这种方法还能够处理具有离散和连续设计变量的RBO问题。提供了六个数值示例,以通过软件应用程序研究该方法的优势。结果说明了该应用在结构RBO中的优势。由于OpenSees用于有限元可靠性分析,因此该应用程序很容易扩展到非线性结构。

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