Open Access

ARTICLE

ML and CFD Simulation of Flow Structure around Tandem Bridge Piers in Pressurized Flow

Aliasghar Azma¹, Ramin Kiyanfar², Yakun Liu^1,*, Masoumeh Azma^3,*, Di Zhang¹, Ze Cao¹, Zhuoyue Li¹

1 School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China
2 Department of Art and Architecture, Payame Noor University, Shiraz, 19395-4697, Iran
3 School of Foreign Languages, Nanjing Xiaozhuang University, Nanjing, 211171, China

* Corresponding Authors: Yakun Liu. Email: ; Masoumeh Azma. Email:

Computers, Materials & Continua 2023, 75(1), 1711-1733. https://doi.org/10.32604/cmc.2023.036680

Received 09 October 2022; Accepted 09 December 2022; Issue published 06 February 2023

Abstract

Various regions are becoming increasingly vulnerable to the increased frequency of floods due to the recent changes in climate and precipitation patterns throughout the world. As a result, specific infrastructures, notably bridges, would experience significant flooding for which they were not intended and would be submerged. The flow field and shear stress distribution around tandem bridge piers under pressurized flow conditions for various bridge deck widths are examined using a series of three-dimensional (3D) simulations. It is indicated that scenarios with a deck width to pier diameter (Ld/p) ratio of 3 experience the highest levels of turbulent disturbance. In addition, maximum velocity and shear stresses occur in cases with Ld/p equal to 6. Results indicate that increasing the number of piers from 1 to 2 and 3 results in the increase of bed shear stress by 24% and 20% respectively. Finally, five machine learning algorithms, including Decision Trees (DT), Feed Forward Neural Networks (FFNN), and three Ensemble models, are implemented to estimate the flow field and the turbulent structure. Results indicated that the highest accuracy for estimation of U, and W, were obtained using AdaBoost ensemble with R² = 0.946 and 0.951, respectively. Besides, the Random Forest algorithm outperformed AdaBoost slightly in the estimation of V and turbulent kinetic energy (TKE) with R² = 0.894 and 0.951, respectively.

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Keywords

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