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Statistical Analysis Approaches in Scour Depth of Bridge Piers

Auteur(s):



Médium: article de revue
Langue(s): anglais
Publié dans: Civil Engineering Journal, , n. 1, v. 9
Page(s): 143-153
DOI: 10.28991/cej-2023-09-01-011
Abstrait:

A local scour is the removal of bed material from around the pier of the bridge. This bed removal is considered a big problem and is of great concern for hydraulic engineers. They should find economic solutions for this problem. The exaggerated local scour around bridge piers leads to many problems for the whole bridge structure, such as stability problems that may lead to the bridge's destruction. This paper aims to verify the scour depth around different shapes of uniform bridge piers for different flow conditions than those done by previous researchers using different prediction models. Where the consistency of previous experimental investigations is verified by multiple nonlinear regression analysis (MNLR), Gene Expression Programming (GEP) and Artificial Neural Network (ANN) models. In the comparison of values that were measured and predicted by the four models (CFD, MNLR, ANN, and Gene), it is seen that the ANN model has the ability to predict the Ys/b values higher than other models used in relation to the measured values. This makes the ANN model superior in predicting the Ys/b value over the other used models, followed by the Gene model. In comparison, the values of the R2and RMSE for the four models that were used in this study, for the Ys/b model using the ANN had a value of 0.9978 and 0.0147, respectively, while those for the Ys/b model using the Gene model were 0.9800 and 0.0375, respectively.

Copyright: © 2023 Shahad Abdulkathum, Hassan I. Al-Shaikhli, Ahmed A. Al-Abody, Tameem M. Hashim
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10715924
  • Publié(e) le:
    21.03.2023
  • Modifié(e) le:
    10.05.2023
 
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