CAREER: Infrastructure Management under Model Uncertainty: Adaptive Sequential Learning and Decision Making

职业：模型不确定性下的基础设施管理：自适应顺序学习和决策

• 批准号: 1653716
• 负责人: Matteo Pozzi
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1653716&HistoricalAwards=false
• 关键词: CAREER; Infrastructure; Management; under; Model

Maintenance and operation of interconnected infrastructures, including transportation and energy systems, are critical to provide adequate levels of safety and functionality to society. Critical decisions must be made about aging infrastructures under high uncertainty about the current condition of their components, their evolution, and the results of maintenance actions. As sensors, robotic inspectors, communication and computation become cheaper and more reliable, their use can be integrated into infrastructure management to improve maintenance policies and reduce uncertainty, risk and overall management cost. Full exploitation of this potential requires a change in paradigm in how we model and approach infrastructure decision making, and how novel computational methods may be adopted and extended in statistical inference and sequential optimization for managing infrastructure systems. This CAREER award exploits the interdependency among sensor deployment, probabilistic inference and decision making on maintenance and operation by developing advanced models and algorithms that would ultimately result in enhanced decision making for maintenance and operation purposes and reduced costs. The research will test the developed framework on three case studies including wind farms, networks of gas pipelines, and networks of bridges, in collaboration with companies that manage systems and develop software. The same framework underpinning the project research also forms the basis for a pedagogic approach on infrastructure management based on active, situated-learning activities, in which games will be developed to involve K-12 and college students with assigned roles as virtual infrastructure managers in an interactive environment. The PI will work closely with Carnegie Mellon's SEE (Summer Engineering Experience for Girls) program to inspire interest in risk analysis and infrastructure systems decision making. This project's goal is to develop scalable algorithms for long-term decision-making under persistent model uncertainty. These algorithms will target decisions about not only operation and maintenance of infrastructure systems, but also about the gathering of further information: placing sensors, scheduling inspections, planning tests and experimenting with new technologies for operation and sensing. By sequential iterations of learning and acting, managers can adaptively optimize resource allocation. Optimization becomes more complex, but with higher pay-off, when focusing on large interconnected systems: when numerous infrastructure components are statistically interdependent, because of spatial proximity, common stressors, or common dynamic models, information can propagate across them. This allows observations collected in one location to also be useful for inferring the conditions of others and, more importantly, the higher-level system evolution model. By identifying locations and components that deserve inspections, the agent can sequentially adapt the exploration of the system, avoid costly over-instrumentation, and guide management towards a sustainable use of the limited resources. To develop these approaches, this project will make use of Bayesian hierarchical modeling, random field modeling, pre-posterior and value-of-information analysis, sub-modularity analysis, approximate dynamic programming for Partially Observable Markov Decision Processes (POMDP), on-line optimization and network analysis.

互联基础设施（包括交通和能源系统）的维护和运营对于为社会提供足够的安全性和功能性至关重要。必须在其组件的当前状况、演变和维护操作结果高度不确定的情况下，针对老化的基础设施做出关键决策。随着传感器、机器人检查器、通信和计算变得更便宜、更可靠，它们的使用可以集成到基础设施管理中，以改进维护策略并减少不确定性、风险和总体管理成本。充分利用这种潜力需要改变我们如何建模和处理基础设施决策的范式，以及如何在管理基础设施系统的统计推断和顺序优化中采用和扩展新颖的计算方法。该职业奖通过开发先进的模型和算法，利用传感器部署、概率推理以及维护和操作决策之间的相互依赖性，最终增强维护和操作目的的决策并降低成本。该研究将与管理系统和开发软件的公司合作，在三个案例研究中测试开发的框架，包括风电场、天然气管道网络和桥梁网络。支持该项目研究的同一框架也构成了基于主动、情境学习活动的基础设施管理教学方法的基础，其中将开发游戏，让 K-12 和大学生参与，并在交互式环境中分配虚拟基础设施管理者的角色。 PI 将与卡内基梅隆大学的 SEE（女童夏季工程体验）项目密切合作，激发人们对风险分析和基础设施系统决策的兴趣。 该项目的目标是开发可扩展的算法，用于在持续模型不确定性下进行长期决策。这些算法不仅将针对基础设施系统的操作和维护做出决策，还将针对收集更多信息做出决策：放置传感器、安排检查、规划测试以及试验操作和传感新技术。通过学习和行动的连续迭代，管理者可以自适应地优化资源分配。当关注大型互连系统时，优化变得更加复杂，但回报更高：当许多基础设施组件在统计上相互依赖时，由于空间接近、共同压力源或共同动态模型，信息可以在它们之间传播。这使得在一个位置收集的观测结果也可用于推断其他位置的条件，更重要的是，可用于推断更高级别的系统演化模型。通过识别值得检查的位置和组件，代理可以依次调整系统的探索，避免代价高昂的过度检测，并指导管理层可持续地利用有限的资源。为了开发这些方法，该项目将利用贝叶斯分层建模、随机场建模、前后验和信息值分析、子模块分析、部分可观察马尔可夫决策过程 (POMDP) 的近似动态规划、在线优化和网络分析。

项目成果

Optimal adaptive inspection and maintenance for redundant systems

冗余系统的最佳自适应检查和维护

• DOI: 10.1177/1748006x211020151
• 发表时间: 2021
• 期刊: Part O: Journal of Risk and Reliability
• 作者: Lin, Chaochao;Pozzi, Matteo
• 通讯作者: Pozzi, Matteo

Submodularity issues in value-of-information-based sensor placement

基于信息值的传感器放置中的子模块化问题

• DOI: 10.1016/j.ress.2018.11.010
• 发表时间: 2019
• 期刊: Reliability Engineering & System Safety
• 影响因子: 8.1
• 作者: Malings, C.;Pozzi, M.
• 通讯作者: Pozzi, M.

Optimal inspection of binary systems via value of information analysis

通过信息分析的价值对二元系统进行优化检查

• DOI: 10.1016/j.ress.2021.107944
• 发表时间: 2021
• 期刊: Reliability engineering systems safety
• 作者: Chaochao Lin, Junho Song

What makes long-term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance: What makes long-term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

是什么让长期监测变得方便？

• DOI: 10.1002/stc.2329
• 发表时间: 2019
• 期刊: Structural Control and Health Monitoring
• 影响因子: 5.4
• 作者: Li, Shuo;Pozzi, Matteo
• 通讯作者: Pozzi, Matteo

Gaussian Process Regression and Classification for Probabilistic Damage Assessment of Spatially Distributed Systems

• DOI: 10.1007/s12205-018-0014-x
• 发表时间: 2018-03
• 期刊: KSCE Journal of Civil Engineering
• 影响因子: 2.2
• 作者: M. Pozzi;Qiaochu Wang