Discrete Structural Optimization through a Sequential Decision Process

该奖项的目的是通过融合结构优化原理和顺序决策算法来获得严格和高效的离散结构优化框架。结构优化是一种设计技术，用于确定材料效率的设计解决方案，并广泛应用于许多学科，包括民用，航空航天和机械工程。因此，能够识别新颖有效解决方案的新设计框架可以改善设计结果，并通过例如减少自然资源消耗、减少隐含碳、提高安全性和可服务性以及增强美观性而广泛受益。土木结构，例如由钢或木材制成的土木结构，通常用标准化元件建造。使用传统方法优化这样的结构可能在计算上效率低下，应用受限，或者将近似引入到解决方案中。通过将离散结构优化作为一个连续的决策过程，可以熟练地解决与当代人工智能技术，派生的框架将特别适合于优化工程系统构建的标准化元素，从而直接导致高效的离散解决方案，提高设计成果。这项研究将通过开发一个教育软件应用程序来补充，该应用程序适用于K-12和STEM领域的本科生，该应用程序将公开提供，以促进广泛采用和关于结构行为，设计和优化的包容性学习机会。这项研究还将提供机会，教学，培训，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，该过程可以通过深度强化学习技术自适应地解决，从而获得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动作，以包括拓扑和参数组件，从而适应代表标准化元件横截面几何形状的离散设计变量;（ii）研究并推导出针对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）将框架应用于各种设计示例，以验证和基准测试学习的策略和合成的解决方案;以及（v）将来自前述目标的所选设计示例集成到教育应用程序的开发中。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.