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

职业：不确定性下的最佳工程决策以增强结构生命周期

• 批准号: 1751941
• 负责人: Konstantinos Papakonstantinou
• 金额: $ 50万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751941&HistoricalAwards=false
• 关键词: CAREER; Optimal; Engineering; Decision; making

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.

该学院早期职业发展计划（CAREER）奖项的目标是在不确定性和风险下推进最佳工程决策的创新，重点是生命周期分析和结构应用，以解决与老化基础设施环境相关的持续和新兴科学和社会挑战。因此，本项目建议采用综合结构生命周期分析、设计、维护、改造和恢复框架，以降低基础设施生命周期成本，优化社会对基础设施的投资，建立更安全的结构，并协助提高国家安全和经济竞争力。除了该项目的主要重点结构工程应用外，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多样化的工程师和科学家，他们除了其他技能外，还需要提高计算能力。计划中的活动影响了广泛的受众，包括学生和教师，本科生和研究生，研究人员，教师和实习工程师。在这个项目中，工程决策建议从随机最优控制和强化学习的角度来处理，拥抱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，这一观点导致了基于状态的结构系统的估计规则和增强的新含义的基于性能的分析。传统的方法，结构设计，分析，改造，恢复和维护也被重新考虑，从传统的静态优化问题，不断变化的结构的集成终身控制过程。中央随机控制组件基于完全和部分可观测马尔可夫决策过程、异步动态规划和深度强化学习技术。需要一些理论追求和进步，使这样的决策方法老化的结构和基础设施系统的风险和不确定性的存在。在本项目中，将研究主要挑战的答案，例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，结构可靠性更新和广义脆弱性函数的结合，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

项目成果

Managing offshore wind turbines through Markov decision processes and dynamic Bayesian networks

通过马尔可夫决策过程和动态贝叶斯网络管理海上风力涡轮机

• 发表时间: 2022
• 期刊: 13th International Conference on Structural Safety & Reliability (ICOSSAR
• 作者: Morato, P.G.;Papakonstantinou, K.G.;Andriotis, C.P.;Rigo, P.
• 通讯作者: Rigo, P.

Optimal inspection and maintenance planning for deteriorating structural components through dynamic Bayesian networks and Markov decision processes

• DOI: 10.1016/j.strusafe.2021.102140
• 发表时间: 2021-10-30
• 期刊: STRUCTURAL SAFETY
• 影响因子: 5.8
• 作者: Morato, P. G.;Papakonstantinou, K. G.;Rigo, P.
• 通讯作者: Rigo, P.

Quasi-Newton Hamiltonian MCMC sampling for reliability estimation in high-dimensional non-Gaussian spaces

用于高维非高斯空间可靠性估计的拟牛顿哈密顿 MCMC 采样

• 发表时间: 2022
• 期刊: 13th International Conference on Structural Safety & Reliability (ICOSSAR
• 作者: Papakonstantinou, K.G.;Eshra, E.;Nikbakht, H.
• 通讯作者: Nikbakht, H.

Quantifying the value of structural monitoring for decision making

量化结构监测对决策的价值

• 发表时间: 2019
• 期刊: 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13
• 作者: Papakonstantinou, K.G.;Andriotis, C.P.;Gao, H.;Chatzi, E.N.
• 通讯作者: Chatzi, E.N.

Managing engineering systems with large state and action spaces through deep reinforcement learning

• DOI: 10.1016/j.ress.2019.04.036
• 发表时间: 2019-11-01
• 期刊: RELIABILITY ENGINEERING & SYSTEM SAFETY
• 影响因子: 8.1
• 作者: Andriotis, C. P.;Papakonstantinou, K. G.
• 通讯作者: Papakonstantinou, K. G.