MOHAMMED ELHASSAN OMER ELHASSAN

@tongji.edu.cn

Department of Bridge Engineering
Department of Bridge Engineering, Tongji University

MOHAMMED ELHASSAN OMER ELHASSAN


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https://researchid.co/mohammed_elhassan

RESEARCH, TEACHING, or OTHER INTERESTS

Civil and Structural Engineering
4

Scopus Publications

6

Scholar Citations

2

Scholar h-index

Scopus Publications

  • Enhancement of Aerostatic Stability of a 1600 m-Span Centrally-Slotted Box Girder Cable-Stayed Bridge: A Parametric Analysis
    Mohammed Elhassan Omer Elhassan, Le-Dong Zhu, Hao Sun
    International Journal of Structural Stability and Dynamics, 2025
    Modern long-span cable-supported bridges are increasingly vulnerable to strong winds, making wind-induced static instability a critical design consideration. This paper comprehensively investigates the aerostatic stability of a 1[Formula: see text]600[Formula: see text]m centrally-slotted box girder cable-stayed bridge through finite element analysis and wind tunnel testing. A parametric sensitivity analysis examined the influence of aerodynamic coefficients, cable drag, cross-section shape parameters, and control measures on bridge stability. Results reveal that the drag coefficient [Formula: see text] exerts the most significant influence on critical wind speed [Formula: see text], followed by the lift coefficient [Formula: see text], while the moment coefficient [Formula: see text] has a lesser impact. Increasing cable drag significantly reduces [Formula: see text], underscoring the importance of cable aerodynamics. Conversely, increasing wind fairing apex height [Formula: see text] enhances stability, while increasing the inner-side web height ratio [Formula: see text] is detrimental. Among investigated flow control measures, a guide plate combined with a wind barrier proved most effective in improving critical wind speed. These results offer significant implications for designing long-span bridges, providing a comprehensive understanding of key design parameters and effective flow control strategies to enhance wind-induced static stability and the structure’s wind resistance.
  • Surrogate modeling for aerodynamic static instability of central-slotted box decks using machine learning approaches
    Mohammed Elhassan Omer Elhassan, Le-Dong Zhu, Wael Alhaddad, Zhongxu Tan
    Advances in Structural Engineering, 2024
    Studies on aerodynamic controls of central-slotted box decks primarily focused on mitigating vortex-induced vibrations (VIV), as this type of deck typically performs well against flutter instability. However, as the span length increases, the critical wind speed of aerodynamic static instability ( U cr) might be lower than flutter critical wind speed. Thus, U cr will determine the overall aerodynamic performance of such bridges. Investigating this instability through wind tunnel testing methods and numerical simulation can be expensive and time-consuming. In this paper, surrogate models using machine learning approaches, specifically artificial neural network (ANN) and extreme gradient boosting (XGBoost), were developed and optimized for fast and reliable prediction for U cr based on wind tunnel tests and simulation data. The results demonstrated that the built surrogate models can predict U cr accurately. The parametric study results showed that the height ratio of wind fairing apex ( a/b), wind angle of attack ( α), and length of the main span ( L) have the most influence on the U cr compared with other parameters. Finally, based on the developed ANN surrogate model and the artificial bee colony (ABC) optimization algorithm, an optimized section was proposed to enhance the section’s performance against aerodynamic static instability.
  • Modeling and Optimization for The Tensile Properties of 3D-Printed FRP using Artificial Neural Network and Artificial Bee Colony Algorithm
    Wael Alhaddad, Khalil Yahya Mohammed Almajhali, Yahia Halabi, Mohammed Elhassan
    IABSE Congress Nanjing 2022 Bridges and Structures Connection Integration and Harmonisation Report, 2022
    <p>Fiber-reinforced polymer (FRP) has multiple applications as a primary material or reinforcing material for the structural elements. Controlling the quality of the 3D printed FRP is critical to guarantee a FRP material of high performance. In this research, machine learning (ML) model based on data collected from experimental studies was developed by artificial neural network (ANN) to control the quality of 3D printed FRP. ANN model predicts the ultimate tensile strength (UTS) of the FRP as function of 7 material and printing parameters. The UTS of the FRP was maximized via optimizing the printing and material parameters by using artificial bee colony (ABC) algorithm. ANN and ABC algorithms were coded by MATLAB. The results showed that the developed ANN model can predict with good accuracy the UTS of FRP. Moreover, it was found that the ABC optimization algorithm can design the input parameters such that a FRP with maximum UTS can be obtained.</p>
  • Prediction of Aerodynamic Coefficients using Artificial Neural Network in Shape Optimization of Centrally-Slotted Box Deck Bridge
    IABSE Congress Nanjing 2022 Bridges and Structures Connection Integration and Harmonisation Report, 2022

RECENT SCHOLAR PUBLICATIONS

  • Enhancement of Aerostatic Stability of a 1 600 m-Span Centrally-Slotted Box Girder Cable-Stayed Bridge: A Parametric Analysis
    MEO Elhassan, LD Zhu, H Sun
    International Journal of Structural Stability and Dynamics, 2650353 , 2025
    2025.0
    Citations: 3
  • Surrogate modeling for aerodynamic static instability of central-slotted box decks using machine learning approaches
    MEO Elhassan, LD Zhu, W Alhaddad, Z Tan
    Advances in Structural Engineering 27 (13), 2271-2288 , 2024
    2024.0
    Citations: 3
  • Prediction of Static Critical Wind Speed of Centrally-Slotted Box Deck Bridge Using Artificial Neural Network
    M Elhassan, LD Zhu, W Alhaddad, Z Tan

MOST CITED SCHOLAR PUBLICATIONS

  • Enhancement of Aerostatic Stability of a 1 600 m-Span Centrally-Slotted Box Girder Cable-Stayed Bridge: A Parametric Analysis
    MEO Elhassan, LD Zhu, H Sun
    International Journal of Structural Stability and Dynamics, 2650353 , 2025
    2025.0
    Citations: 3
  • Surrogate modeling for aerodynamic static instability of central-slotted box decks using machine learning approaches
    MEO Elhassan, LD Zhu, W Alhaddad, Z Tan
    Advances in Structural Engineering 27 (13), 2271-2288 , 2024
    2024.0
    Citations: 3
  • Prediction of Static Critical Wind Speed of Centrally-Slotted Box Deck Bridge Using Artificial Neural Network
    M Elhassan, LD Zhu, W Alhaddad, Z Tan