FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4378575323> ?p ?o ?g. }

• W4378575323 endingPage "1384" @default.
• W4378575323 startingPage "1384" @default.
• W4378575323 abstract "The impact effect is a crucial issue in civil engineering and has received considerable attention for decades. For the first time, this study develops hybrid machine learning models that integrate the novel Extreme Gradient Boosting (XGB) model with Particle Swam Optimization (PSO), Grey Wolf Optimizer (GWO), Moth Flame Optimizer (MFO), Jaya (JA), and Multi-Verse Optimizer (MVO) algorithms for predicting the permanent transverse displacement of circular hollow section (CHS) steel members under impact loads. The hybrid machine learning models are developed using data collected from 357 impact tests of CHS steel members. The efficacy of hybrid machine learning models is evaluated using three performance metrics. The results show that the GWO-XGB model achieves high accuracy and outperforms the other models. The values of R2, RMSE, and MAE obtained from the GWO-XGB model for the test set are 0.981, 2.835 mm, and 1.906 mm, respectively. The SHAP-based model explanation shows that the initial impact velocity of the indenter, the impact mass, and the ratio of impact position to the member length are the most sensitive parameters, followed by the yield strength of the steel member and the member length; meanwhile, member diameter and member thickness are the parameters least sensitive to the permanent transverse displacement of CHS steel members. Finally, this study develops a web application tool to help rapidly estimate the permanent transverse displacement of CHS steel members under impact loads." @default.
• W4378575323 created "2023-05-28" @default.
• W4378575323 creator A5000349881 @default.
• W4378575323 creator A5024527579 @default.
• W4378575323 creator A5040752326 @default.
• W4378575323 creator A5073964441 @default.
• W4378575323 date "2023-05-26" @default.
• W4378575323 modified "2023-09-26" @default.
• W4378575323 title "Development of Hybrid Machine Learning Models for Predicting Permanent Transverse Displacement of Circular Hollow Section Steel Members under Impact Loads" @default.
• W4378575323 cites W1678356000 @default.
• W4378575323 cites W1972119531 @default.
• W4378575323 cites W1973270792 @default.
• W4378575323 cites W1979242126 @default.
• W4378575323 cites W1981364236 @default.
• W4378575323 cites W1986238972 @default.
• W4378575323 cites W1995009405 @default.
• W4378575323 cites W2000219223 @default.
• W4378575323 cites W2006823550 @default.
• W4378575323 cites W2008729712 @default.
• W4378575323 cites W2012025364 @default.
• W4378575323 cites W2020642941 @default.
• W4378575323 cites W2025676834 @default.
• W4378575323 cites W2027453253 @default.
• W4378575323 cites W2031183907 @default.
• W4378575323 cites W2031624327 @default.
• W4378575323 cites W2061438946 @default.
• W4378575323 cites W2082544636 @default.
• W4378575323 cites W2083376912 @default.
• W4378575323 cites W2084235049 @default.
• W4378575323 cites W2086960100 @default.
• W4378575323 cites W2134719452 @default.
• W4378575323 cites W2160842249 @default.
• W4378575323 cites W2170998939 @default.
• W4378575323 cites W2754685359 @default.
• W4378575323 cites W2772770917 @default.
• W4378575323 cites W2787924513 @default.
• W4378575323 cites W2807042118 @default.
• W4378575323 cites W2808720612 @default.
• W4378575323 cites W2889419438 @default.
• W4378575323 cites W2969779475 @default.
• W4378575323 cites W2970896257 @default.
• W4378575323 cites W3007798961 @default.
• W4378575323 cites W3012960996 @default.
• W4378575323 cites W3013477797 @default.
• W4378575323 cites W3013498854 @default.
• W4378575323 cites W3041981321 @default.
• W4378575323 cites W3102476541 @default.
• W4378575323 cites W3121293617 @default.
• W4378575323 cites W3128290931 @default.
• W4378575323 cites W3131046868 @default.
• W4378575323 cites W3133841051 @default.
• W4378575323 cites W3134790670 @default.
• W4378575323 cites W3136951162 @default.
• W4378575323 cites W3159623433 @default.
• W4378575323 cites W3183637089 @default.
• W4378575323 cites W3185551827 @default.
• W4378575323 cites W3188968325 @default.
• W4378575323 cites W3196184381 @default.
• W4378575323 cites W3209059351 @default.
• W4378575323 cites W4205892687 @default.
• W4378575323 cites W4206718313 @default.
• W4378575323 cites W4210543791 @default.
• W4378575323 cites W4213248101 @default.
• W4378575323 cites W4220934920 @default.
• W4378575323 cites W4224236323 @default.
• W4378575323 cites W4224286316 @default.
• W4378575323 cites W4229055150 @default.
• W4378575323 cites W4280564806 @default.
• W4378575323 cites W4280589646 @default.
• W4378575323 cites W4281714576 @default.
• W4378575323 cites W4281804356 @default.
• W4378575323 cites W4283699468 @default.
• W4378575323 cites W4285080189 @default.
• W4378575323 cites W4300687551 @default.
• W4378575323 cites W4303649579 @default.
• W4378575323 cites W4311425406 @default.
• W4378575323 cites W4313568200 @default.
• W4378575323 cites W4318455275 @default.
• W4378575323 cites W4319841050 @default.
• W4378575323 cites W4322621407 @default.
• W4378575323 cites W4366544215 @default.
• W4378575323 cites W883434633 @default.
• W4378575323 doi "https://doi.org/10.3390/buildings13061384" @default.
• W4378575323 hasPublicationYear "2023" @default.
• W4378575323 type Work @default.
• W4378575323 citedByCount "3" @default.
• W4378575323 countsByYear W43785753232023 @default.
• W4378575323 crossrefType "journal-article" @default.
• W4378575323 hasAuthorship W4378575323A5000349881 @default.
• W4378575323 hasAuthorship W4378575323A5024527579 @default.
• W4378575323 hasAuthorship W4378575323A5040752326 @default.
• W4378575323 hasAuthorship W4378575323A5073964441 @default.
• W4378575323 hasBestOaLocation W43785753231 @default.
• W4378575323 hasConcept C107551265 @default.
• W4378575323 hasConcept C127413603 @default.
• W4378575323 hasConcept C154945302 @default.
• W4378575323 hasConcept C154954056 @default.
• W4378575323 hasConcept C15744967 @default.

• next