Intelligent resilience analysis for infrastructure considering uncertain real-time data

考虑不确定实时数据的基础设施智能弹性分析

• 批准号: 501624329
• 负责人: Professor Dr.-Ing. Michael Beer
• 金额: --
• 依托单位: Institut für Risiko und Zuverlässigkeit
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/501624329?language=en
• 关键词: Intelligent; resilience; analysis; infrastructure; considering

Comprehensive yet efficient numerical modeling and analysis of large and complex infrastructure systems is becoming increasingly important for modern societies. Such models are at the heart of an infrastructure resilience framework for stakeholders to make cost-effective decisions about infrastructure operations, risk prevention, maintenance, repair, recovery, and planning. Key challenges for a realistic analysis include interconnecting numerous models and performance measures at all system levels, analyzing the massive data sets available for critical structures or generated from Structural Health Monitoring (SHM), and accounting for the inherent uncertainty in all available data. At the same time, the consideration of systemic interactions between key components modeled in detail is crucial.The present proposal addresses these challenges at the structural component, regional subsystem, and global infrastructure system levels. At the component level, an intelligent surrogate modeling procedure based on automated machine learning will be developed to condense the vast amount of data available for the SPP 2388 reference bridge into a probabilistic, time- & state-continuous surrogate model. This model will be updated based on real-time data collected by SHM. Uncertainties arising from conflicting, vague, or incorrect (real-time) data will be quantified and propagated through the systemic model. At the regional subsystem level, critical infrastructure components are linked and integrated into a newly developed system reliability model that enables comprehensive yet efficient numerical analysis. The complexity of this subsystem model is further reduced by clustering similar components into component types. Information resulting from subsystem reliability modeling is integrated into the developed Life Cycle Resilience decision-making Framework (LCRF), including recovery models and cost functions. This will enable decision-makers to make cost-efficient life cycle decisions in the maintenance, overhaul and repair planning as well as design process, taking into account component lifetimes and monetary constraints. The developed approaches on component level are demonstrated for the reference highway bridge (A2) "Weserstrombrücke" near Bad Oeynhausen. On the system level, the North Rhine-Westphalian infrastructure system is considered, whereby the counties are divided into regional subsystems and physical properties of further critical structural components are assumed for demonstration purposes.

对大型和复杂的基础设施系统进行全面而有效的数值建模和分析，对现代社会来说正变得越来越重要。此类模型是基础设施弹性框架的核心，利益相关者可以在基础设施运营、风险防范、维护、维修、恢复和规划方面做出经济高效的决策。现实分析的关键挑战包括将所有系统级别的众多模型和性能指标相互连接，分析可用于关键结构或由结构健康监测(SHM)生成的海量数据集，以及考虑所有可用数据中的固有不确定性。同时，详细建模的关键组件之间的系统相互作用的考虑至关重要。本提案在结构组件、区域子系统和全球基础设施系统级别解决这些挑战。在组件级，将开发一种基于自动机器学习的智能代理建模过程，以将SPP 2388参考电桥可用的海量数据压缩为概率、时间和状态连续的代理模型。该模型将根据卫生与公众服务部收集的实时数据进行更新。冲突、模糊或不正确(实时)数据产生的不确定性将被量化，并通过系统模型传播。在区域子系统一级，将关键基础设施组成部分联系起来，并纳入新开发的系统可靠性模型，该模型能够进行全面而有效的数值分析。通过将相似的组件聚集成组件类型，进一步降低了该子系统模型的复杂性。子系统可靠性建模得到的信息被集成到开发的生命周期弹性决策框架(LCRF)中，包括恢复模型和成本函数。这将使决策者能够在维护、大修和维修规划以及设计过程中做出具有成本效益的生命周期决策，同时考虑到部件寿命和资金限制。以巴特欧因豪森附近的参考公路大桥(A2)“Weserstrombrücke”为例，展示了所开发的部件级方法。在系统层面上，考虑了北莱茵-威斯特伐利亚基础设施系统，将各县划分为区域子系统，并为演示目的假设了其他关键结构组件的物理特性。