Vasileios Ntertimanis


Last Name

Ntertimanis

First Name

Vasileios

ORCID

0000-0002-4671-1962

Organisational unit

03890 - Chatzi, Eleni / Chatzi, Eleni

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2018 - 201972020 - 20253
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Publications 1 - 10 of 10
  • Monitoring-Driven Seismic Assessment of Existing Masonry Buildings
    Item type: Conference Paper
    Martakis, Panagiotis; Reuland, Yves; Ntertimanis, Vasileios; et al. (2019)
    ANCRiSST 2019 Procedia 14th International Workshop on Advanced Smart Materials and Smart Structures Technology
  • Baudynamische Bemessung & Identifizierung einer aufgehängten Treppenkonstruktion
    Item type: Conference Paper
    Martakis, Panagiotis; Ntertimanis, Vasileios; Chatzi, Eleni (2019)
  • Fault diagnosis of wind turbine structures using decision tree learning algorithms with big data
    Item type: Conference Paper
    Abdallah, Imad; Ntertimanis, Vasileios; Mylonas, Charilaos; et al. (2018)
    Safety and Reliability – Safe Societies in a Changing World
  • On the Consistent Classification and Treatment of Uncertainties in Structural Health Monitoring Applications
    Item type: Journal Article
    Kamariotis, Antonios; Vlachas, Konstantinos; Ntertimanis, Vasileios; et al. (2025)
    ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
    In this paper, we provide a comprehensive definition and classification of various sources of uncertainty within the fields of structural dynamics, system identification, and structural health monitoring (SHM), with a primary focus on the latter. Utilizing the classical input–output system representation as a main contextual framework, we present a taxonomy of uncertainties, intended for consistent classification of uncertainties in SHM applications: (i) input uncertainty; (ii) model form uncertainty; (iii) model parameter/variable uncertainty; (iv) measurement uncertainty; and (v) inherent variability. We then critically review methods and algorithms that address these uncertainties in the context of key SHM tasks: system identification and model inference, model updating, accounting for environmental and operational variability (EOV), virtual sensing, damage identification, and prognostic health management. A benchmark shear frame model with hysteretic links is employed as a running example to illustrate the application of selected methods and algorithmic tools. Finally, we discuss open challenges and future research directions in uncertainty quantification for SHM. [DOI: 10.1115/1.4067140]
  • A method to reduce model uncertainty by fusing the output from multiple stochastic simulators
    Item type: Conference Paper
    Abdallah, Imad; Tatsis, Konstantinos; Mylonas, Charilaos; et al. (2020)
    Journal of Physics: Conference Series
    In virtualizing engineered systems, it is essential to come up with simulators that are essentially capable of representing the system in its "as-deployed" state. Any attempt to this end may only be approximate given the inherent uncertainties present in the loadings and operational conditions of the system, as well as the configuration of the system itself (geometry, materials, control systems, boundary conditions, etc.). This is especially true for complex systems, such as wind turbines, where often a number of assumptions govern the setup of the engineering models. Such models are often made available at different granularities with each one offering a diversified level of precision depending on the quantity of interest (e.g. macroscopic displacements or microscopic strains) and the properties of the acting loads (e.g. amplitude and frequency content). This implies that the predictive capabilities are severely hampered when a single so-deemed best model is chosen for simulation. Building on this idea, we here present a method for fusing the outputs from multiple simulators (e.g. aero-servo-hydro-elastic simulators) for estimating a quantity of interest (QoI) with higher precision. The proposed ensemble learning approach comprises two main building blocks. Firstly, a clustering step by means of a Variational Bayesian Gaussian mixture model, employed for the weighing of each available simulator. Clustering is performed on the basis of the binned input space, which allows for extraction of a probability map for each local region of the binned input space. This delivers an adaptive scheme, which allows different simulators to more or less prominently contribute to the prediction of the QoI, depending on the range of the input parameters. Local weighted Bootstrap Aggregation is then executed in a second step for combining the clustered ensemble of outputs from the individual simulators. A simulated toy example and a wind turbine blade fatigue case study are herein exploited to demonstrate the efficacy of the suggested ensemble learning scheme. The approach is compared against alternatives typically adopted in existing literature, such as Stacking, classical Bagging, and Bayesian Model Averaging. The results confirm an improvement in predictive capabilities as expressed via the reduction in the generalization error and the narrowing of the associated confidence interval.
  • Crack detection through spatio-temporal pattern recognition
    Item type: Other Conference Item
    Mylonas, Charilaos; Abdallah, Imad; Ntertimanis, Vasileios; et al. (2018)
  • On the use of Meta-Foundations for Seismic Isolation: a practical application for conventional buildings
    Item type: Conference Paper
    Martakis, Panagiotis; Ntertimanis, Vasileios; Chatzi, Eleni (2019)
    COMPDYN 2019, 7th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Crete, Greece, 24–26 June 2019
  • Vibration monitoring of an existing Masonry Building under Demolition
    Item type: Conference Paper
    Martakis, Panagiotis; Reuland, Yves; Ntertimanis, Vasileios; et al. (2020)
  • Metamaterials and seismic waves in 4 square metres
    Item type: Other Conference Item
    Colombi, Andrea; Zaccherini, Rachele; Ntertimanis, Vasileios; et al. (2019)
    Abstract Book. 2019 International Congress on Ultrasonics, Bruges, Belgium, 3-6 September 2019
  • An autonomous real-time decision tree framework for monitoring & diagnostics on wind turbines
    Item type: Conference Paper
    Abdallah, Imad; Ntertimanis, Vasileios; Chatzi, Eleni (2018)
    WINERCOST'18 & Aeolus4future Final Conference 2018: "Wind Energy Harvesting (… focusing on exploitation of the Mediterranean Area)"
    This paper describes a conceptual framework for real-time monitoring and diagnostics, root cause analysis of faults and quantitative risk assessment in the context of operation and maintenance of wind turbine components. An autonomous software-hardware solution is proposed that implements a real-time decision tree learning algorithm for smart monitoring and diagnosis of the state of structural and mechanical components on wind turbines. A decision tree is a machine learning algorithm that classifies the outcome of events resulting in a "flow-chart" like structure, laying a path from an initiationg event to an end state of system with each event associated with a probability of occurrence. A paradigm of the proposed conceptual framework focuses on the tower substructure of the wind turbine indicationg the potential of the approach with respect to the design of specialized software for monitoring and diagnostics of both new and existing installations.
Publications 1 - 10 of 10