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    Wyświetlanie 1-3 z 3
    Tytuł:
    The efficiency of single base and network RTK for Structural Health Monitoring
    Autorzy:
    Topal, Güldane Oku
    Akpinar, Burak
    Powiązania:
    https://bibliotekanauki.pl/articles/43852792.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    odbiornik GPS
    GNSS
    sejsmometr
    network RTK
    structural health monitoring
    single base RTK
    shake table
    time series analysis
    Opis:
    With the developing technology and increasing construction, the importance of structural observations, which are of great significance in disaster management, has increased. Geodetic methods have been preferred in recent years due to their high accuracy and ease of use in Structural Health Monitoring (SHM) Surveys. In this study, harmonic oscillation tests have been carried out on a shake table to determine the usability of the Single Base and the Network Real-Time Kinematic (RTK) Global Navigation Satellite Systems (GNSS) method in SHM studies. It is aimed to determine the harmonic movements of different amplitudes and frequencies created by the shake table with 20 Hz multi-GNSS equipment. The amplitude and frequency values of the movements created using Fast Fourier Transform (FFT) and Time Series Analysis have been calculated. The precision of the analysis results has been determined by comparing the LVDT (Linear Variable Differential Transformer) data, which is the position sensor of the shake table, with the GNSS data. The advantages of the two RTK methods over each other have been determined using the calculated amplitude and frequency differences. As a result of all experiments, it has been determined that network and single base RTK GNSS methods effectively monitor structural behaviours and natural frequencies.
    Źródło:
    Advances in Geodesy and Geoinformation; 2022, 71, 2; art. no. e28, 2022
    2720-7242
    Pojawia się w:
    Advances in Geodesy and Geoinformation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Implementation of digital twin and support vector machine in structural health monitoring of bridges
    Autorzy:
    Al-Hijazeen, Asseel Za'al Ode
    Fawad, Muhammad
    Gerges, Michael
    Koris, Kálmán
    Salamak, Marek
    Powiązania:
    https://bibliotekanauki.pl/articles/27312162.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    monitorowanie stanu konstrukcji
    most
    uszkodzenie
    bliźniak cyfrowy
    uczenie maszynowe
    maszyna wektorów wsparcia
    structural health monitoring
    bridge
    damage
    digital twin
    machine learning
    support vector machine
    Opis:
    Structural health monitoring (SHM) of bridges is constantly upgraded by researchers and bridge engineers as it directly deals with bridge performance and its safety over a certain time period. This article addresses some issues in the traditional SHM systems and the reason for moving towards an automated monitoring system. In order to automate the bridge assessment and monitoring process, a mechanism for the linkage of Digital Twins (DT) and Machine Learning (ML), namely the Support Vector Machine (SVM) algorithm, is discussed in detail. The basis of this mechanism lies in the collection of data from the real bridge using sensors and is providing the basis for the establishment and calibration of the digital twin. Then, data analysis and decision-making processes are to be carried out through regression-based ML algorithms. So, in this study, both ML brain and a DT model are merged to support the decision-making of the bridge management system and predict or even prevent further damage or collapse of the bridge. In this way, the SHM system cannot only be automated but calibrated from time to time to ensure the safety of the bridge against the associated damages.
    Źródło:
    Archives of Civil Engineering; 2023, 69, 3; 31--47
    1230-2945
    Pojawia się w:
    Archives of Civil Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble of feature extraction methods to improve the structural damage classification in a wind turbine foundation
    Autorzy:
    Leon-Medina, Jersson X.
    Parés, Núria
    Anaya, Maribel
    Tibaduiza, Diego A.
    Pozo, Francesc
    Powiązania:
    https://bibliotekanauki.pl/articles/27311417.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    structural health monitoring
    wind turbine foundation
    damage classification
    machine learning
    feature extraction
    XGBoost
    monitorowanie stanu konstrukcji
    fundament turbiny wiatrowej
    klasyfikacja uszkodzeń
    uczenie maszynowe
    ekstrakcja cech
    Opis:
    The condition monitoring of offshore wind power plants is an important topic that remains open. This monitoring aims to lower the maintenance cost of these plants. One of the main components of the wind power plant is the wind turbine foundation. This study describes a data-driven structural damage classification methodology applied in a wind turbine foundation. A vibration response was captured in the structure using an accelerometer network. After arranging the obtained data, a feature vector of 58 008 features was obtained. An ensemble approach of feature extraction methods was applied to obtain a new set of features. Principal Component Analysis (PCA) and Laplacian eigenmaps were used as dimensionality reduction methods, each one separately. The union of these new features is used to create a reduced feature matrix. The reduced feature matrix is used as input to train an Extreme Gradient Boosting (XGBoost) machine learning-based classification model. Four different damage scenarios were applied in the structure. Therefore, considering the healthy structure, there were 5 classes in total that were correctly classified. Five-fold cross validation is used to obtain a final classification accuracy. As a result, 100% of classification accuracy was obtained after applying the developed damage classification methodology in a wind-turbine offshore jacket-type foundation benchmark structure.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 3; art. no. e144606
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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