Discrete Structural Optimization through a Sequential Decision Process

该奖项的目的是通过收敛结构优化原理和顺序决策算法推导出严格且高效的离散结构优化框架。结构优化是一种用于确定材料高效设计方案的设计技术，广泛应用于许多学科，包括土木、航空航天和机械工程。因此，能够确定新颖有效解决方案的新设计框架可以改善设计结果，并通过减少自然资源消耗，减少隐含碳，提高安全性和可维护性以及增强美观性等方式广泛受益。土木结构，例如那些由钢或木材制成的，通常是用标准化的元素建造的。使用传统方法优化这样的结构可能在计算上效率低下，在应用上受到限制，或者在解决方案中引入近似。通过将离散结构优化作为一个顺序决策过程，可以熟练地使用现代人工智能技术解决，派生的框架将特别适合于优化由标准化元素构建的工程系统，从而直接导致高效的离散解决方案并改善设计结果。该研究将通过开发教育软件应用程序进行补充，该应用程序旨在为STEM领域的K-12和本科水平的学生提供服务，该应用程序将公开提供，以促进广泛采用，并在作为游戏呈现时提供有关结构行为，设计和优化的包容性学习机会。这项研究还将提供机会，通过与各种多元化项目和学生组织的接触，在一个新兴领域教授、培训和指导来自代表性不足群体的学生。本研究的具体目标是发现将离散结构优化框架为马尔可夫决策过程所需的知识，该决策过程可以用深度强化学习技术自适应地求解，从而推导出严格且高效的离散结构优化框架。因此，该项目的具体研究目标是：(i)研究如何最好地定义马尔可夫决策过程的行为，以包括拓扑和参数组件，以适应代表标准化元素截面几何形状的离散设计变量；（ii）研究并推导为离散结构优化问题量身定制的深度强化学习解决方案架构；（iii）将框架的适用性扩展到突出的体积最小化优化问题；（iv）将框架应用于不同的设计实例，以验证所学习的政策和综合解决方案并对其进行基准测试；(v)将从上述目标中选择的设计示例整合到教育应用程序/软件的开发中，其中桁架和框架结构的设计作为基于顺序决策的游戏呈现。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The aim of this award is to derive a rigorous and highly effective discrete structural optimization framework by converging structural optimization principles and sequential decision-making algorithms. Structural optimization is a design technique that is used to identify material-efficient design solutions and is widely used in many disciplines, including civil, aerospace, and mechanical engineering. Hence, new design frameworks that are capable of identifying novel and efficient solutions can improve design outcomes and be broadly beneficial by, for example, reducing the consumption of natural resources, reducing embodied carbon, improving safety and serviceability, and enhancing aesthetics. Civil structures, for example those made from steel or timber, are often constructed with standardized elements. Optimizing such structures using conventional approaches can be computationally inefficient, limited in application, or introduce approximation into the solution. By framing discrete structural optimization as a sequential decision process, that can be adeptly solved with contemporary artificial intelligence techniques, the derived framework will be particularly well suited for optimizing engineered systems constructed from standardized elements, thus leading directly to highly efficient discrete solutions and improving design outcomes. The research will be complemented by the development of an educational software application, intended for K-12 and undergraduate level students in STEM fields, that will be made publicly available to promote broad adoption and an inclusive learning opportunity about structural behavior, design, and optimization when presented as a game. The research will also provide opportunities to teach, train, and mentor students from underrepresented groups in an emerging area through outreach to various diversity programs and student organizations.The specific goal of this research is to discover the knowledge necessary to frame discrete structural optimization as a Markov Decision Process that can be adaptly solved with deep reinforcement learning techniques so as to derive a rigorous and highly effective discrete structural optimization framework. Thus, the specific research objectives of this project are to: (i) investigate how best to define the actions of the Markov Decision Process to include both topological and parametric components so as to accommodate the discrete design variables representing standardized element cross-sectional geometries; (ii) investigate and derive deep reinforcement learning solution architectures tailored for the discrete structural optimization problem; (iii) extend the framework’s applicability to the prominent volume minimization optimization problem; (iv) apply the framework to various design examples to validate and benchmark the learned policies and synthesized solutions; and (v) integrate selected design examples from the preceding objective into the development of the educational application/software where the design of truss and frame structures is presented as a game based upon sequential decision making.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.