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    Item type:Publication, Access status: without files ,
    A decentralized digital twinning approach for wireless structural health monitoring systems
    (Springer, 2025-07)
    Dragos, Kosmas
    ;
    Smarsly, Kay
    Since the introduction of wireless technologies in structural health monitoring (SHM), researchers have focused on exploiting the computational capabilities of wireless sensor nodes via embedded computing approaches that enable on-board data analysis, in lieu of wirelessly transmitting large amounts of data. Embedded computing for wireless SHM has largely been based on data-driven analysis methods, which only implicitly account for the physics governing the structural behavior. However, the increasing digitalization of societal activities, including structural maintenance, is frequently associated with digital twinning, which builds upon continuous exchange of data between the digital and the physical world and, often, upon physics-based modeling. In this context, this paper presents an approach for integrating digital twins into wireless SHM systems. Given the distributed nature of wireless sensor networks, characterized by the quasi-independent operation of wireless sensor nodes, the digital twins are designed in a decentralized manner, leveraging the computational power of wireless sensor nodes. In particular, the decentralized digital twins are represented by partial finite element (FE) models of a structure being monitored that are embedded in the wireless sensor nodes and “live” and “evolve” in tandem with the structure. The wireless sensor nodes use the partial FE models and structural response data locally collected to collaboratively assess the structural condition. The proposed approach is validated via laboratory tests, the results of which showcase the capability of the approach to yield reliable estimates of the structural condition.
    Publicationtype: Conference Paper
      23
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    Modeling and predicting soil vibrations during vibrodriving installation using noisy data and dynamic mode decomposition
    (Springer, 2025-07)
    Williams Riquer, Francisco
    ;
    Abdennadher, Mohamed Ali
    ;
    Chmelnizkij, Alexander
    ;
    Grabe, Jürgen
    ;
    Smarsly, Kay
    Accurately predicting ground vibrations during vibrodriving remains a significant challenge due to the risks posed to nearby structures. In the present study a data-driven approach to modeling soil vibrations using dynamic mode decomposition (DMD), an equation-free framework, is introduced. However, the performance of DMD is susceptible to noise, which may compromise its accuracy. To address the effects of noise, two DMD modifications are evaluated and compared: (i) augmented DMD (Aug-DMD), which uses time-delayed coordinates, and (ii) forward-backward DMD (FB-DMD), a denoising strategy that combines forward and backward time evolution to reduce systematic eigenvalue bias caused by noise. Synthetic geophone data augmented with white noise is used to simulate noise during the measurement process and to evaluate the effectiveness of the algorithms. In summary, the present study identifies the conditions under which each method predominates and provides recommendations for their application in real-time field scenarios.
    Publicationtype: Conference Paper
      11
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    Item type:Publication, Access status: without files ,
    An embedded computing approach for vibration-based system identification using reduced-order finite elements
    (Springer, 2025-07)
    Dragos, Kosmas
    ;
    Magalhães, Filipe
    ;
    Smarsly, Kay
    System identification (SID) constitutes a focal point in experimental testing of civil infrastructure as well as in structural health monitoring (SHM). Conventional SID approaches have relied on creating finite element (FE) models of civil infrastructure assets and on calibrating the models against in-situ derived information from testing equipment or against SHM data. FE models are capable of providing valuable information on the structural condition. However, the equations governing the FE method are coupled, usually calling for centralized data collection, which may be unsuitable for modern wireless SHM systems that employ independently operating wireless sensor nodes. In this paper, a SID approach for making full use of FE models on board wireless sensor nodes is presented, based on embedded computing and on reduced-order finite elements. Specifically, FE models of civil infrastructure assets are reduced, using model-order reduction, and partitioned into partial models, which represent different substructures and are computationally efficient for embedment in wireless sensor nodes. Using the partial models and structural response data from the substructures, insights into the structural condition of the civil infrastructure assets are obtained. The proposed approach is suitable for vibration-based SID, applied as part of long-term SHM strategies, and implementable in wireless SHM systems. Validation tests using SHM data recorded from a real-world road bridge showcase the validity of the proposed SID approach.
    Publicationtype: Conference Paper
      14