Intelligent resilience analysis for infrastructure considering uncertain real-time data

• Applicants: Professor Dr.-Ing. Michael Beer; Dr. Matteo Broggi
• Subject Area: Structural Engineering, Building Informatics and Construction Operation
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 501624329

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.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2388: Hundred plus - Extending the Lifetime of Complex Engineering Structures through Intelligent Digitalization
• International Connection: China, USA
• Cooperation Partners: Professor Vladik Kreinovich, Ph.D.; Professor Pengfei Wei, Ph.D.; Professor Konstantin Zuev, Ph.D.