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Predicting shear strength of slender beams without reinforcement using hybrid gradient boosting trees and optimization algorithms
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作者 Thuy-Anh NGUYEN Hai-Bang LY Van Quan TRAN 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2022年第10期1267-1286,共20页
Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand.The primary purpose of this work is to develop machine ... Shear failure of slender reinforced concrete beams without stirrups has surely been a complicated occurrence that has proven challenging to adequately understand.The 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 factors.Gradient 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 variables.Shap values and two-dimensional PDP analysis were then carried out.Engineers 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. 展开更多
关键词 slender beam shear strength gradient boosting optimization algorithms
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