Resiliente Infrastruktur basierend auf kognitiven Bauwerken
Zusammenfassung der Projektergebnisse
Infrastructure resilience has come to the forefront, following incidents of structural failure in the recent past as a result of extreme loading conditions, exacerbated by climate change. The consequent urge to enhance public safety by averting structural failure has drawn attention to integrating aspects of infrastructure resilience, such as robustness, redundancy, resourcefulness and rapidity, into structural design and structural assessment strategies. In this context, the well-established field of structural health monitoring (SHM) has been used, in this project, as a basis for developing structures capable of perceiving their surroundings and of forecasting the structural behavior, in an attempt to facilitate timely remedial action via structural control (SC). Referred to as “cognitive structures”, the proposed project essentially has focused on civil infrastructure assets that are typically equipped with “smart” wireless SHM systems (“smart structures”) and has complemented the “smart” aspects (i.e. ability to extract information on the structural condition via data analysis) with “cognition” aspects, adopted from the area of cognitive buildings (i.e. ability to anticipate structural behaviors), in an attempt to advance the resilience of civil infrastructure. The key point towards granting cognition capabilities to smart structures has been to embed physics-based models into wireless sensor nodes, which have enabled (i) making educated assessments of the structural condition and (ii) conducting “what-if” scenarios to forecast structural behaviors. As departure point for developing physics-based modeling methods for smart wireless SHM systems, the finite element (FE) method has been selected. Leveraging the well-established methods for model-order reduction, a methodology for producing “lightweight” FE models, amenable to embedment in wireless sensor nodes has been developed. The methodology is based on dynamic condensation, which offers the advantage of reducing FE models to the degrees of freedom actually measured, thereby avoiding the ill-posed nature of model updating methods stemming from the discretization mismatch between typical FE models and degrees of freedom measured by smart wireless SHM systems. The aforementioned concept has also been integrated into a SC method, which builds upon the concept of temporal FE as applied in preliminary work on SC. The SC method essentially yields velocities to be applied to the structural system of a cognitive structure so as to keep the structural response within predefined bounds. The results of the project align with the state of the art in SHM and smart structures, essentially enhancing the levels of SHM-relevant information, knowledge and “wisdom”, as well as with emerging paradigms, such as the “Internet of Everything”, through the integration of physics-based modeling both in SHM and in SC.
Projektbezogene Publikationen (Auswahl)
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Sensorintegrierte Digitale Zwillinge für das automatisierte Monitoring von Infrastrukturbauwerken. Bautechnik, 99(6), 471-476.
Smarsly, Kay; Dragos, Kosmas & Kölzer, Thomas
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Adaptive Fault Diagnosis for Simultaneous Sensor Faults in Structural Health Monitoring Systems. Infrastructures, 8(3), 39.
Al-Zuriqat, Thamer; Chillón Geck, Carlos; Dragos, Kosmas & Smarsly, Kay
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Mobile Structural Health Monitoring Based on Legged Robots. Infrastructures, 8(9), 136.
Smarsly, Kay; Dragos, Kosmas; Stührenberg, Jan & Worm, Mathias
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Advancing civil infrastructure assessment through robotic fleets. Internet of Things and Cyber-Physical Systems, 4, 138-140.
Smarsly, Kay & Dragos, Kosmas
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Frequency-domain synchronization of structural health monitoring data. Journal of Sound and Vibration, 571, 118017.
Dragos, Kosmas; Magalhães, Filipe; Manolis, George D. & Smarsly, Kay
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Identification of combined sensor faults in structural health monitoring systems. Smart Materials and Structures, 33(8), 085026.
Al-Nasser, Heba; Al-Zuriqat, Thamer; Dragos, Kosmas; Geck, Carlos Chillón & Smarsly, Kay
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A Smart Monitoring Approach Based on Decentralized Digital Twins. Advances in Information Technology in Civil and Building Engineering, 492–506.
Dragos, Kosmas & Smarsly, Kay
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An Embedded Computing Approach for Vibration-Based System Identification Using Reduced-Order Finite Elements. Lecture Notes in Civil Engineering, 573-582. Springer Nature Switzerland.
Dragos, Kosmas; Magalhães, Filipe & Smarsly, Kay
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Damage detection in lightweight bridges with traveling masses using machine learning. Engineering Structures, 322, 119216.
Dadoulis, Georgios; Manolis, George D.; Katakalos, Konstantinos; Dragos, Kosmas & Smarsly, Kay
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PHYSICS-BASED WIRELESS STRUCTURAL HEALTH MONITORING AND CONTROL USING TEMPORAL FINITE ELEMENTS. Proceedings of the 10th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COMPDYN 2025), 2731-2740. Institute of Structural Analysis and Antiseismic Research National Technical University of Athens.
Dragos, Kosmas; Martínez, Clara; Manolis, George & Smarsly, Kay
