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Wyszukujesz frazę "Gerges, Michael" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Finite element model updating of steel-concrete composite bridge: A study case of the Ruri bridge in Vietnam
Autorzy:
Nguyen, Duc Cong
Salamak, Marek
Katunin, Andrzej
Gerges, Michael
Abdel-Maguid, Mohamed
Powiązania:
https://bibliotekanauki.pl/articles/27312150.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
analiza drgań
akcelerometr
model elementów skończonych
uaktualnienie
most betonowy
optymalizacja roju cząstek
algorytm genetyczny
vibration analysis
accelerometer
finite element model
update
concrete bridge
particle swarm optimisation
genetic algorithm
Opis:
The study presents the finite element (FE) model update of the existing simple-spans steel-concrete composite bridge structure using a particle swarm optimisation (PSO) and genetic algorithm (GA) approaches. The Wireless Structural Testing System (STS-WiFi) of Bridge Diagnostic, Inc. from the USA, implemented various types of sensors including: LVDT displacement sensors, intelligent strain transducers, and accelerometers that the static and dynamic historical behaviors of the bridge structure have been recorded in the field testing. One part of all field data sets has been used to calibrate the cross-sectional stiffness properties of steel girders and material of steel beams and concrete deck in the structural members including 16 master and slave variables, and that the PSO and GA optimisation methods in the MATLAB software have been developed with the new innovative tools to interface with the analytical results of the FE model in the ANSYS APDL software automatically. The vibration analysis from the dynamic responses of the structure have been conducted to extract four natural frequencies from experimental data that have been compared with the numerical natural frequencies in the FE model of the bridge through the minimum objective function of percent error to be less than 10%. In order to identify the experimental mode shapes of the structure more accurately and reliably, the discrete-time state-space model using the subspace method (N4SID) and fast Fourier transform (FFT) in MATLAB software have been applied to determine the experimental natural frequencies in which were compared with the computed natural frequencies. The main goal of the innovative approach is to determine the representative FE model of the actual bridge in which it is applied to various truck load configurations according to bridge design codes and standards. The improved methods in this document have been successfully applied to the Vietnamese steel-concrete composite bridge in which the load rating factors (RF) of the AASHTO design standards have been calculated to predict load limits, so the final updated FE model of the existing bridge is well rated with all RF values greater than 1.0. The presented approaches show great performance and the potential to implement them in industrial conditions.
Źródło:
Archives of Civil Engineering; 2023, 69, 3; 425--443
1230-2945
Pojawia się w:
Archives of Civil Engineering
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
    Wyświetlanie 1-2 z 2

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