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Enhanced ANN predictive model for composite pipes subjected to low-velocity impact loads

(2023) BUILDINGS. 13(4).
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Abstract
This paper presents an enhanced artificial neural network (ANN) to predict the displacement in composite pipes impacted by a drop weight having different velocities. The impact response of fiber-reinforced polymer composite pipes depends on several factors including thickness, stacking sequence, and the number of layers. These factors were investigated in an earlier study using sensitivity analysis, and it was found that they had the most prominent effect on the impact resistance of the composite pipes. In this present study, composite pipes with a diameter of 54 mm are considered to explore the damages induced by low-velocity impact and the influence of these damages on their strength. To evaluate the effect of low-velocity, the pipes were exposed to impacts at different velocities of 1.5, 2, 2.5, and 3 m/s, and preliminary damage was initiated. Next, we used Jaya and E-Jaya algorithms to enhance the ANN algorithm for good training and prediction. The Jaya algorithm has a basic structure and needs only two requirements, namely, population size and terminal condition. Recently, Jaya algorithm has been widely utilized to solve various problems. Due to its single learning technique and limited population information, Jaya algorithm may quickly be trapped in local optima while addressing complicated optimization problems. For better prediction, an enhanced Jaya (E-Jaya) algorithm has been presented to enhance global searchability. In this study, ANN is enhanced based on the influential parameters using E-Jaya to test its effectiveness. The results showed the effectiveness of the E-Jaya algorithm for best training and prediction compared with the original algorithm.
Keywords
Building and Construction, Civil and Structural Engineering, Architecture, composite pipes, impact loads, ANN, Jaya, E-Jaya, DAMAGE, FAILURE, TUBES, FRICTION, BEHAVIOR, FIBERS, CARBON, PLATES, WEAR

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MLA
Ghandourah, Emad, et al. “Enhanced ANN Predictive Model for Composite Pipes Subjected to Low-Velocity Impact Loads.” BUILDINGS, vol. 13, no. 4, 2023, doi:10.3390/buildings13040973.
APA
Ghandourah, E., Khatir, S., Banoqitah, E. M., Alhawsawi, A. M., Benaissa, B., & Abdel Wahab, M. (2023). Enhanced ANN predictive model for composite pipes subjected to low-velocity impact loads. BUILDINGS, 13(4). https://doi.org/10.3390/buildings13040973
Chicago author-date
Ghandourah, Emad, Samir Khatir, Essam Mohammed Banoqitah, Abdulsalam Mohammed Alhawsawi, Brahim Benaissa, and Magd Abdel Wahab. 2023. “Enhanced ANN Predictive Model for Composite Pipes Subjected to Low-Velocity Impact Loads.” BUILDINGS 13 (4). https://doi.org/10.3390/buildings13040973.
Chicago author-date (all authors)
Ghandourah, Emad, Samir Khatir, Essam Mohammed Banoqitah, Abdulsalam Mohammed Alhawsawi, Brahim Benaissa, and Magd Abdel Wahab. 2023. “Enhanced ANN Predictive Model for Composite Pipes Subjected to Low-Velocity Impact Loads.” BUILDINGS 13 (4). doi:10.3390/buildings13040973.
Vancouver
1.
Ghandourah E, Khatir S, Banoqitah EM, Alhawsawi AM, Benaissa B, Abdel Wahab M. Enhanced ANN predictive model for composite pipes subjected to low-velocity impact loads. BUILDINGS. 2023;13(4).
IEEE
[1]
E. Ghandourah, S. Khatir, E. M. Banoqitah, A. M. Alhawsawi, B. Benaissa, and M. Abdel Wahab, “Enhanced ANN predictive model for composite pipes subjected to low-velocity impact loads,” BUILDINGS, vol. 13, no. 4, 2023.
@article{01GXQJHXW53R5M1F5XWBG0YJXQ,
  abstract     = {{This paper presents an enhanced artificial neural network (ANN) to predict the displacement in composite pipes impacted by a drop weight having different velocities. The impact response of fiber-reinforced polymer composite pipes depends on several factors including thickness, stacking sequence, and the number of layers. These factors were investigated in an earlier study using sensitivity analysis, and it was found that they had the most prominent effect on the impact resistance of the composite pipes. In this present study, composite pipes with a diameter of 54 mm are considered to explore the damages induced by low-velocity impact and the influence of these damages on their strength. To evaluate the effect of low-velocity, the pipes were exposed to impacts at different velocities of 1.5, 2, 2.5, and 3 m/s, and preliminary damage was initiated. Next, we used Jaya and E-Jaya algorithms to enhance the ANN algorithm for good training and prediction. The Jaya algorithm has a basic structure and needs only two requirements, namely, population size and terminal condition. Recently, Jaya algorithm has been widely utilized to solve various problems. Due to its single learning technique and limited population information, Jaya algorithm may quickly be trapped in local optima while addressing complicated optimization problems. For better prediction, an enhanced Jaya (E-Jaya) algorithm has been presented to enhance global searchability. In this study, ANN is enhanced based on the influential parameters using E-Jaya to test its effectiveness. The results showed the effectiveness of the E-Jaya algorithm for best training and prediction compared with the original algorithm.}},
  articleno    = {{973}},
  author       = {{Ghandourah, Emad and Khatir, Samir and Banoqitah, Essam Mohammed and Alhawsawi, Abdulsalam Mohammed and Benaissa, Brahim and Abdel Wahab, Magd}},
  issn         = {{2075-5309}},
  journal      = {{BUILDINGS}},
  keywords     = {{Building and Construction,Civil and Structural Engineering,Architecture,composite pipes,impact loads,ANN,Jaya,E-Jaya,DAMAGE,FAILURE,TUBES,FRICTION,BEHAVIOR,FIBERS,CARBON,PLATES,WEAR}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{16}},
  title        = {{Enhanced ANN predictive model for composite pipes subjected to low-velocity impact loads}},
  url          = {{http://doi.org/10.3390/buildings13040973}},
  volume       = {{13}},
  year         = {{2023}},
}

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