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

作　　者：Aliasghar Azma Ramin Kiyanfar Yakun Liu Masoumeh Azma Di Zhang Ze Cao Zhuoyue Li 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第4期1711-1733,共23页计算机、材料和连续体（英文）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos.52179060 and 51909024).

摘　　要：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.

关 键 词：Bridge pier scour process deck width machine learning turbulent structure 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]