Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms

作     者：Thuy-Anh NGUYEN Hai-Bang LY Van Quan TRAN Thuy-Anh NGUYEN;Hai-Bang LY;Van Quan TRAN

作者机构：Department of Civil EngineeringUniversity of Transport TechnologyHanoi 100000Vietnam 

出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)

年 卷 期：2022年第16卷第10期

页      面：1267-1286页

核心收录：

学科分类：08[工学] 080104[工学-工程力学] 0815[工学-水利工程] 0801[工学-力学（可授工学、理学学位）] 

主　　题：slender beam shear strength gradient boosting optimization algorithms 

摘      要：Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately *** primary purpose of this work is to develop machine learning models capable of reliably predicting the shear strength of non-shear-reinforced slender beams(SB).A database encompassing 1118 experimental findings from the relevant literature was compiled,containing eight distinct *** Boosting(GB)technique was developed and evaluated in combination with three different optimization algorithms,namely Particle Swarm Optimization(PSO),Random Annealing Optimization(RA),and Simulated Annealing Optimization(SA).The findings suggested that GB-SA could deliver strong prediction results and effectively generalizes the connection between the input and output *** values and two-dimensional PDP analysis were then carried *** may use the findings in this work to define beam’s geometrical components and material used to achieve the desired shear strength of SB without reinforcement.