Discrete Structural Optimization through a Sequential Decision Process

该奖项的目的是通过融合结构优化原则和顺序决策算法来得出严格和高效的离散结构优化框架。结构优化是一种用于确定材料效率设计解决方案的设计技术，广泛应用于许多学科，包括土木工程、航空航天和机械工程。因此，能够识别新的高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、减少具体化的碳、提高安全性和适用性以及增强美感等方式广泛受益。土建结构，例如那些由钢或木材制成的结构，通常是用标准化构件建造的。使用传统方法优化这种结构可能在计算上效率低下，在应用中受到限制，或者在解中引入近似。通过将离散结构优化定义为一个可以用现代人工智能技术熟练解决的连续决策过程，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，从而直接产生高效的离散解决方案并改进设计结果。作为这项研究的补充，将开发一个面向K-12和STEM领域本科生的教育软件应用程序，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关于结构行为、设计和优化的包容性学习机会。这项研究还将通过接触各种不同的项目和学生组织，提供教授、培训和指导新兴领域中代表性不足群体的学生的机会。本研究的具体目标是发现将离散结构优化作为一个马尔可夫决策过程所必需的知识，该过程可以用深度强化学习技术自适应地求解，从而得出严格和高效的离散结构优化框架。因此，本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作用，以包括拓扑和参数分量，以适应代表标准化单元截面几何的离散设计变量；(Ii)研究和推导为离散结构优化问题量身定做的深度强化学习解决方案体系结构；(Iii)将框架的适用性扩展到突出的体积最小化优化问题；(Iv)将该框架应用于各种设计实例，以验证和基准学习的策略和综合解决方案；以及(V)将从前述目标中选择的设计实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以基于顺序决策的游戏的形式呈现。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.