Eleni Chatzi


Last Name

Chatzi

First Name

Eleni

ORCID

0000-0002-6870-240X

Organisational unit

03890 - Chatzi, Eleni / Chatzi, Eleni

Search Results

Publications 1 - 10 of 609
  • Numerical and Experimental Investigations of Reinforced Masonry Structures Across Multiple Scales
    Item type: Book Chapter
    Chatzi, Eleni; Triantafyllou, Savvas P.; Fuggini, Clemente (2018)
    Intelligent Systems, Control and Automation: Science and Engineering ~ Mechatronics for Cultural Heritage and Civil Engineering
  • Monitoring-supported value generation for managing structures and infrastructure systems
    Item type: Journal Article
    Kamariotis, Antonios; Chatzi, Eleni; Straub, Daniel; et al. (2024)
    Data-Centric Engineering
    To maximize its value, the design, development and implementation of structural health monitoring (SHM) should focus on its role in facilitating decision support. In this position paper, we offer perspectives on the synergy between SHM and decision-making. We propose a classification of SHM use cases aligning with various dimensions that are closely linked to the respective decision contexts. The types of decisions that have to be supported by the SHM system within these settings are discussed along with the corresponding challenges. We provide an overview of different classes of models that are required for integrating SHM in the decision-making process to support the operation and maintenance of structures and infrastructure systems. Fundamental decision-theoretic principles and state-of-the-art methods for optimizing maintenance and operational decision-making under uncertainty are briefly discussed. Finally, we offer a viewpoint on the appropriate course of action for quantifying, validating, and maximizing the added value generated by SHM. This work aspires to synthesize the different perspectives of the SHM, Prognostic Health Management, and reliability communities, and provide directions to researchers and practitioners working towards more pervasive monitoring-based decision-support.
  • Dynamic Response Assessment of Impact Meta-Dampers
    Item type: Other Conference Item
    Chondrogiannis, Kyriakos Alexandros; Dertimanis, Vasilis; Masri, Sami F.; et al. (2022)
    Recently, the concept of metamaterials, initially studied for electromagnetic applications has gained interest within a civil engineering context. It has been shown that periodic arrangement of resonators, referred to as unit cells, can provide beneficial properties in vibration absorption, within a specific range of frequencies, the so-called bandgap. In order to take advantage of the full potential these unique structures can offer, a wider breadth of the bandgap is pursued, while additionally shifting the lower bound to lower frequencies renders metamaterials appealing for civil engineering applications. One promising solution to this requirement is the use of nonlinear resonators, which form part of the metamaterial configuration. In the current work, periodic structures consisting of impact damper resonators are investigated, thus introducing a highly nonlinear behaviour. The performance of a single impact damper has already been investigated and its dynamic response has been proven to be advantageous for vibration attenuation. Further exploiting this phenomenon, the current study is extended to multiple degree of freedom (MDOF) systems, which are investigated both numerically and analytically. The MDOF systems occur as concatenation of a finite number of impact damper unit cells. Several characteristics of the proposed configuration are explored as variable parameters for optimization purposes. For the assessment of the system’s efficiency, different metrics are being studied, including the frequency response function and the total energy loss as a result of the metastructure contribution. The results confirm the potential of utilization of the metamaterial concept, building on impact phenomena, for vibration attenuation.
  • System Identification at the Extreme Edge for Network Load Reduction in Vibration-Based Monitoring
    Item type: Journal Article
    Zonzini, Federica; Dertimanis, Vasilis; Chatzi, Eleni; et al. (2022)
    IEEE Internet of Things Journal
    Mechanical complexity, wide dimensions, and big data volume may hamper the implementation of Internet of Things (IoT)-enabled structural health monitoring (SHM) systems. In particular, one of the most important challenges is the reduction of the data payload to be transmitted over the monitoring network. Addressing the problem in the context of vibration-based SHM, this work explores system identification (SysId) as an innovative strategy for data compression at the extreme edge. Indeed, SysId is a signal processing technique aiming at finding a very reduced (i.e., less then one tenth of the total signal length) set of meaningful parameters, which can provide an alternative, but yet completely equivalent, frequency characterization of the structure. In the proposed approach, an embedded system-oriented adaptation of the sequential tall-skinny QR decomposition (eS-TSQR) from the dense linear algebra domain has been exploited to tackle both the memory and computational complexity of the involved algorithms. This yielded to the embodiment of input-output and output-only SysId models into a resource constrained device (i.e., an STM32L5 microcontoller unit), targeted on low-power and low-cost SHM applications, proving high effectiveness for the structural assessment of civil and industrial plants. Besides, a cost-benefit analysis is also presented, in which the energy saving brought by SysId running in a sensor-near manner is comprehensively measured against the power consumption due to data transmission, as implied by stateof-the-art communication protocols for IoT. Results demonstrate that SysId is 1.19x and 2.78x less energy demanding (with a payload reduction of 9x and 45x) w.r.t. compressed sensing-driven and compression-free solutions, respectively.
  • Arresting fatigue cracks in steel plates using prestressed bonded Fe-SMA strips: Analytical prediction and experimental validation
    Item type: Journal Article
    Li, Lingzhen; Wang, Sizhe; Chen, Tao; et al. (2025)
    Thin-Walled Structures
    Bonded iron-based shape memory alloy (Fe-SMA) strengthening has shown great potential in fatigue strengthening for steel structures. However, there is a lack of a comprehensive model for analysing the fatigue behaviour of systems strengthened with prestressed bonded Fe-SMA. This study proposes the first analytical model, integrating a prestress analysis and fatigue analysis, to predict fatigue crack arrest in steel plates strengthened with prestressed bonded Fe-SMA strips. Four steel plates, each featuring a central through-thickness crack, were strengthened using bonded Fe-SMA strips, and subsequently, tested under fatigue loading after generating prestress via heating and cooling. The proposed model succeeded in predicting (i) the fatigue crack arrest at certain load levels and (ii) onset of crack propagation at increased load levels.
  • Data-driven model updating for seismic assessment of existing buildings
    Item type: Conference Paper
    Martakis, Panagiotis; Reuland, Yves; Chatzi, Eleni (2021)
    Proceedings of the International Conference on Structural Health Monitoring of Intelligent Infrastructure
  • Damage Detection in Rods via Use of a Genetic Algorithm and a Deep-Learning Based Surrogate
    Item type: Conference Paper
    Sharma, Jitendra K.; Soman, Rohan; Kudela, Pawel; et al. (2023)
    Lecture Notes in Civil Engineering ~ European Workshop on Structural Health Monitoring
    This research demonstrates the use of genetic algorithms for damage detection in isotropic rods. The spectral element method and a deep-learning-based surrogate model is utilized for simulating wave propagation in an isotropic cracked rod. The genetic algorithm employs results (“numerical experiment") obtained from the spectral element model and the deep-learning-based surrogate to determine the optimized crack locations and crack depths as output parameters. The objective function used in the genetic algorithm is the mean square error between the response obtained from spectral element model and the deep-learning-based surrogate model.
  • A graded metamaterial for broadband and high-capability piezoelectric energy harvesting
    Item type: Journal Article
    Zhao, Bao; Thomsen, Henrik R.; De Ponti, Jacopo M.; et al. (2022)
    Energy Conversion and Management
    This work proposes a graded metamaterial-based energy harvester integrating the piezoelectric energy harvesting function targeting low-frequency ambient vibrations (¡100 Hz). The harvester combines a graded metamaterial with beam-like resonators, piezoelectric patches, and a self-powered interface circuit for broadband and high-capability energy harvesting. Firstly, an integrated lumped parameter model is derived from both the mechanical and the electrical sides to determine the power performance of the proposed design. Secondly, thorough numerical simulations are carried out to optimize both the grading profile and wave field amplification, as well as to highlight the effects of spatial-frequency separation and the slow-wave phenomenon on energy harvesting performance and efficiency. Finally, experiments with realistic vibration sources validate the theoretical and numerical results from the mechanical and electrical sides. Particularly, the harvested power of the proposed design yields a five-fold increase with respect to conventional harvesting solutions based on single cantilever harvesters. Our results reveal that by bridging the advantages of graded metamaterials with the design targets of piezoelectric energy harvesting, the proposed design shows significant potential for realizing self-powered Internet of Things devices.
  • Application of Differential GPS Sensing on Long-Span Bridges with Accelerometer Data Fusion
    Item type: Conference Proceedings
    Smyth, Andrew W.; Chatzi, Eleni (2011)
    Proceedings of the 8th International Conference on Structural Dynamics, Eurodyn 2011
  • Improving the Accuracy of Stress Intensity Factors Obtained by the Scaled Boundary Finite Element Method on Hierarchical Meshes
    Item type: Conference Paper
    Egger, Adrian; Triantafyllou, Savvas P.; Chatzi, Eleni (2019)
    Proceedings of the International Conference on Computational Methods
Publications 1 - 10 of 609