Discrete Structural Optimization through a Sequential Decision Process

通过顺序决策过程进行离散结构优化

• 批准号: 2322853
• 负责人: Gordon Warn
• 金额: $ 36.4万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322853&HistoricalAwards=false
• 关键词: Discrete; Structural; Optimization; through; Sequential

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.

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识别材料高效设计解决方案的设计技术，广泛应用于各种领域。因此，能够确定有效的新颖解决方案的新设计框架可以改善设计成果，并通过减少自然资源的消耗、减少隐含碳、提高安全性和可维护性，并增强土木结构（例如由钢或木材制成的结构）通常采用标准化元素构建，使用传统方法优化此类结构可能会导致计算效率低下、应用受到限制，或者将离散结构优化引入到解决方案中。序列决策过程可以通过当代人工智能技术巧妙地解决，派生的框架将特别适合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进设计结果。通过发展一款面向 K-12 和 STEM 领域本科生的教育软件应用程序，该应用程序将以游戏形式公开，以促进结构行为、设计和优化的广泛采用和包容性学习机会。通过接触各种多元化项目和学生组织，为新兴领域代表性不足群体的学生提供教学、培训和指导的机会。这项研究的具体目标是发现将离散结构优化构建为马尔可夫决策过程所需的知识可以通过深度强化学习技术自适应地解决因此，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。适应代表标准化元素横截面几何形状的离散设计变量；(ii) 研究并导出针对离散结构优化问题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； ）将该框架应用于各种设计实例来验证和基准化所学习的策略和综合解决方案；以及（v）将先前目标中选定的设计实例集成到教育应用程序/软件的开发中，其中桁架和框架结构的设计呈现为基于顺序的游戏该奖项反映了 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。