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Eleni Chatzi   Professor  Senior Scientist or Principal Investigator 
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Eleni Chatzi published an article in July 2018.
Top co-authors See all
Stefano Mariani

78 shared publications

Politecnico di Milano, Dipartimento di Ingegneria Civile e Ambientale, Piazza L. da Vinci 32, 20133 Milano, Italy

Andrew W. Smyth

69 shared publications

Department of Civil Engineering and Engineering Mechanics; Columbia University; New York 10027 NY USA

Filippo Ubertini

63 shared publications

Department of Civil and Environmental Engineering, University of Perugia, Perugia, Umbria, Italy

Costas Papadimitriou

61 shared publications

Department of Mechanical Engineering, University of Thessaly, Volos, Greece

Filipe Magalhães

41 shared publications

Construct-ViBest, Faculty of Engineering (FEUP), University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal

57
Publications
51
Reads
16
Downloads
207
Citations
Publication Record
Distribution of Articles published per year 
(1970 - 2018)
Total number of journals
published in
 
32
 
Publications See all
BOOK-CHAPTER 4 Reads 0 Citations On the Monitoring-Driven Assessment of Engineered Systems Eleni N. Chatzi, Vasilis K. Dertimanis Published: 31 July 2018
Conference Proceedings of the Society for Experimental Mechanics Series, doi: 10.1007/978-3-319-74793-4_36
DOI See at publisher website
Article 4 Reads 0 Citations Robust-to-Uncertainties Optimal Design of Seismic Metamaterials Paul-Remo Wagner, Vasilis K. Dertimanis, Eleni N. Chatzi, Ja... Published: 01 March 2018
Journal of Engineering Mechanics, doi: 10.1061/(asce)em.1943-7889.0001404
DOI See at publisher website
Article 3 Reads 1 Citation On the use of mode shape curvatures for damage localization under varying environmental conditions Yaser Shokrani, Vasilis K. Dertimanis, Eleni N. Chatzi, Marc... Published: 29 January 2018
Structural Control and Health Monitoring, doi: 10.1002/stc.2132
DOI See at publisher website ABS Show/hide abstract
A novel damage localization method is introduced in this study, which exploits mode shape curvatures as damage features, while accounting for operational variability. The developed framework operates in an output-only regime,that is, it does not assume availability of records from the influencing environmental/operational quantities but rather from response quantities alone. The introduced tool comprises 3 stages pertaining to training, validation, and diagnostics. During the training stage, a representation of the healthy, or baseline, structural state is acquired over varying operational conditions. A data matrix is formulated, whose individual columns correspond to mode shape curvatures at distinct operational conditions, and principal component analysis (PCA) is applied for extraction of the imprints of separate operational sources on these curvatures. To this end, a residual matrix between the original and the PCA mapped data is formed serving for statistical characterization of each mode. Subsequently, during the validation and diagnostics stages, the mode shape curvature matrices for the currently inspected structural state are assembled and the same PCA mapping is enforced. A typical hypothesis test and a corresponding damage index are then adopted in order to firstly detect damage, and to secondly localize damage, should this exist. The implementation of the proposed method in 2 numerical case studies confirms its effectiveness and the encouraging results suggest further investigation on operating structural systems.
BOOK-CHAPTER 4 Reads 0 Citations Numerical and Experimental Investigations of Reinforced Masonry Structures Across Multiple Scales Eleni N. Chatzi, Savvas P. Triantafyllou, Clemente Fuggini Published: 12 January 2018
Intelligent Systems, Control and Automation: Science and Engineering, doi: 10.1007/978-3-319-68646-2_15
DOI See at publisher website
CONFERENCE-ARTICLE 15 Reads 1 Citation Cost-benefit optimization of sensor networks for SHM applications Giovanni Capellari, Eleni Chatzi, Stefano Mariani Published: 14 November 2017
Proceedings, doi: 10.3390/ecsa-4-04891
DOI See at publisher website ABS Show/hide abstract

Structural health monitoring (SHM) is aimed to obtain information about the structural integrity of a system, e.g. via the estimation of its mechanical properties through observations
collected with a network of sensors. In the present work, we provide a method to optimally design sensor networks in terms of spatial configuration, number and accuracy of sensors. The utility of the sensor network is quantified through the expected Shannon information gain of the measurements with respect to the parameters to be estimated. At assigned number of sensors to be deployed over the structure, the optimal sensor placement problem is ruled by the objective function computed and maximized by combining surrogate models and stochastic optimization algorithms. For a general case, two formulations are introduced and compared: (i) the maximization of the information obtained through the measurements, given the appropriate constraints (i.e. identifiability, technological and budgetary ones); (ii) the maximization of the utility efficiency, defined as the ratio between the information provided by the sensor network and its cost. The method is applied to a large-scale structural problem, and the outcomes of the two different approaches are discussed.

BOOK-CHAPTER 2 Reads 0 Citations Operational Damage Localization of Wind Turbine Blades Yaowen W. Ou, Vasilis K. Dertimanis, Eleni N. Chatzi Published: 13 October 2017
Proceedings of the 1st International Conference on Numerical Modelling in Engineering, doi: 10.1007/978-3-319-67443-8_22
DOI See at publisher website
Conference papers
CONFERENCE-ARTICLE 12 Reads 0 Citations <strong>Computer Vision Aided Structural Identification: Feature Tracking Using Particle Tracking Velocimetry versus Opt... Yunus Harmanci, Zhilu Lai, Utku Gülan, Markus Holzner, Eleni... Published: 14 November 2018
doi: 10.3390/ecsa-5-05750
DOI See at publisher website ABS Show/hide abstract

Recent advances in computer vision techniques allow to obtain information on the dynamic behavior of structures using commercial grade video recording devices. The advantage of such schemes is due to the non-invasive nature of video recording, and the ability to extract information at a high spatial density utilizing features on the structure. This creates an advantage over conventional contact sensors since constraints such as cabling and maximum channel availability are alleviated. In this study, two such schemes are explored, namely Particle Tracking Velocimetry (PTV) and the optical flow algorithm. Both are validated against conventional sensors for a lab-scale shear frame and compared. In cases of imperceptible motion, the recently proposed Phase-based Motion Magnification (PBMM) technique is employed to obtain modal information within frequency bands of interest and further used for modal analysis. The optical flow scheme combined with (PBMM) is further tested on a large-scale post-tensioned concrete beam and validated against conventional measurements, as a transition from lab- to outdoor field applications.

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