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 

作者机构：School of Hydraulic EngineeringFaculty of Infrastructure EngineeringDalian University of TechnologyDalian116024China Department of Art and ArchitecturePayame Noor UniversityShiraz19395-4697Iran School of Foreign LanguagesNanjing Xiaozhuang UniversityNanjing211171China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第4期

页      面：1711-1733页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：Bridge pier scour process deck width machine learning turbulent structure 

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 *** a result,specific infrastructures,notably bridges,would experience significant flooding for which they were not intended and would be *** 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)*** is indicated that scenarios with a deck width to pier diameter(Ld/p)ratio of 3 experience the highest levels of turbulent *** addition,maximum velocity and shear stresses occur in cases with Ld/p equal to *** 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%***,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 *** indicated that the highest accuracy for estimation of U,and W,were obtained using AdaBoost ensemble with R2=0.946 and 0.951,***,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.