CAREER: Optimal Engineering Decision-making Under Uncertainties for Enhanced Structural Life-cycle

该学院早期职业发展计划（Career）奖的目标是在不确定性和风险下推进优化工程决策的创新，重点是生命周期分析和结构应用，以解决与老龄化基础设施环境相关的持续和新兴的科学和社会挑战。因此，该项目提出了一个综合的结构生命周期分析、设计、维护、改造和恢复框架，旨在降低基础设施生命周期成本，优化基础设施的社会投资，使结构更安全，并有助于提高国家安全和经济竞争力。除了作为该项目的主要焦点的结构工程应用之外，研究结果还可用于涉及智能、自动化、自主决策支持框架的大量应用。该项目还将教育下一代工程师和科学家，他们除了其他技能外，还需要提高计算能力。计划中的活动影响广泛的受众，包括在校学生和教师，本科生和研究生，研究人员，教师和执业工程师。在本项目中，建议从随机最优控制和强化学习的角度来处理工程决策，包括并充分整合基于预测物理的随机模型和不确定的生命周期观测。由于决策在工程问题中的重要作用，这种观点导致了结构系统基于状态的估计规则，并增强了基于性能的分析的新含义。传统的结构设计、分析、改造、修复和维护方法也被重新考虑，从传统的静态优化问题到不断变化的结构的综合终身控制过程。中心随机控制组件基于完全和部分可观察马尔可夫决策过程、异步动态规划和深度强化学习技术。在存在风险和不确定性的情况下，需要一些理论追求和进步来实现这种对老化结构和基础设施系统的决策方法。在这个项目中，将研究主要挑战的答案，如维度和历史的诅咒，以及多目标和决策者的解决方案，非线性滤波的结合，结构可靠性更新和广义脆弱性函数等。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 objective of this Faculty Early Career Development Program (CAREER) award is to advance innovation in optimal engineering decision-making under uncertainty and risks, with a focus on life-cycle analysis and structural applications, in order to address ongoing and emerging scientific and societal challenges relevant to the aging infrastructure environment. An integrated structural life-cycle analysis, design, maintenance, retrofit and recovery framework is thus suggested in this project with the aim to reduce infrastructure life-cycle costs, optimize societal investments to infrastructure, lead to safer structures and assist in improving national security and economic competitiveness. Apart from structural engineering applications that is the main focus of the project, the findings can be also used for a plethora of applications involving intelligent, automated, autonomous decision support frameworks. This project will also educate the next diverse generation of engineers and scientists, who, in addition to other skills, need increased computational competence. The planned activities impact a wide audience including school students and teachers, undergraduate and graduate students, researchers, faculty and practicing engineers.In this project, engineering decision-making is suggested to be approached from a stochastic optimal control and reinforcement learning perspective, embracing and fully integrating predictive physics-based stochastic models and uncertain life-cycle observations. Owing to the vital role of decision-making in engineering problems, this viewpoint leads to condition-based estimation rules for structural systems and an enhanced new meaning of performance-based analysis. The traditional approach to structural design, analysis, retrofit, recovery and maintenance is also reconsidered, from a conventional static optimization problem to an integrated lifelong controlling process of ever changing structures. The central stochastic control component is based on fully and Partially Observable Markov Decision Processes, asynchronous dynamic programming and deep reinforcement learning techniques. Several theoretical pursuits and advances are needed to enable such a decision-making approach to aging structures and infrastructure systems in the presence of risks and uncertainties. In this project, answers to main challenges will be investigated, such as the curses of dimensionality and history, together with solutions for multiple objectives and decision makers, incorporation of nonlinear filtering, structural reliability updating, and generalized fragility functions, among others.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.