FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309287427> ?p ?o ?g. }

• W4309287427 endingPage "110318" @default.
• W4309287427 startingPage "110318" @default.
• W4309287427 abstract "Despite its multiple benefits, recycled aggregate concrete (RAC) usually exhibits inferior properties compared with natural aggregate concrete, which has been deemed as a hurdle to its widespread use. One way to overcome this roadblock is to apply FRP jacketing. However, it appears not easy to quantify the axial performance of FRP-confined recycled aggregate concrete columns (FRACC), due largely to the complex load-resisting mechanisms involved. Also, the weakness of RAC itself further compound the problem. This paper aspires to deliver an alternative means to address this difficulty. A powerful boosting approach, XGBoost, was developed to fulfill the goal, where its hyperparameters was fine-tuned by a beetle antennae search metaheuristic algorithm. Meanwhile, a synthetic data generator, tabular generative adversarial networks, was introduced to supplement the limited training data. The model developed outperformed existing empirical equations and several baseline machine learning models. Teng et al.’s FRP-confined concrete model was also improved for better tracing the axial stress–strain behavior. Besides, interpreting the model allows better understanding of the underlying mechanisms such as the minimum reinforcement ratio of FRP required to mitigate the negative effects of RAC. Finally, two data-driven, explicit design equations are given for practical design of FRACC." @default.
• W4309287427 created "2022-11-25" @default.
• W4309287427 creator A5004355445 @default.
• W4309287427 creator A5006111271 @default.
• W4309287427 creator A5019556442 @default.
• W4309287427 creator A5028884885 @default.
• W4309287427 creator A5087269163 @default.
• W4309287427 date "2023-01-01" @default.
• W4309287427 modified "2023-10-14" @default.
• W4309287427 title "Prediction of ultimate condition of FRP-confined recycled aggregate concrete using a hybrid boosting model enriched with tabular generative adversarial networks" @default.
• W4309287427 cites W1145034317 @default.
• W4309287427 cites W1513390939 @default.
• W4309287427 cites W1810120829 @default.
• W4309287427 cites W1901616594 @default.
• W4309287427 cites W1966266172 @default.
• W4309287427 cites W1966781214 @default.
• W4309287427 cites W1976789772 @default.
• W4309287427 cites W1980271342 @default.
• W4309287427 cites W1989274752 @default.
• W4309287427 cites W1990383153 @default.
• W4309287427 cites W2001705326 @default.
• W4309287427 cites W2006528023 @default.
• W4309287427 cites W2008056655 @default.
• W4309287427 cites W2012299702 @default.
• W4309287427 cites W2025293883 @default.
• W4309287427 cites W2031144461 @default.
• W4309287427 cites W2036817976 @default.
• W4309287427 cites W2047094503 @default.
• W4309287427 cites W2048956032 @default.
• W4309287427 cites W2057721587 @default.
• W4309287427 cites W2077209627 @default.
• W4309287427 cites W2080848268 @default.
• W4309287427 cites W2092512255 @default.
• W4309287427 cites W2125283600 @default.
• W4309287427 cites W2143606740 @default.
• W4309287427 cites W2186361217 @default.
• W4309287427 cites W2215144887 @default.
• W4309287427 cites W2216946510 @default.
• W4309287427 cites W2223491879 @default.
• W4309287427 cites W2279139179 @default.
• W4309287427 cites W2514835544 @default.
• W4309287427 cites W2531779434 @default.
• W4309287427 cites W2533400658 @default.
• W4309287427 cites W2556156268 @default.
• W4309287427 cites W2594056788 @default.
• W4309287427 cites W2606428259 @default.
• W4309287427 cites W2794137295 @default.
• W4309287427 cites W2807042118 @default.
• W4309287427 cites W2807659468 @default.
• W4309287427 cites W2810438996 @default.
• W4309287427 cites W2894741937 @default.
• W4309287427 cites W2912576013 @default.
• W4309287427 cites W2923297733 @default.
• W4309287427 cites W2943513704 @default.
• W4309287427 cites W2966786596 @default.
• W4309287427 cites W2970602317 @default.
• W4309287427 cites W2989513683 @default.
• W4309287427 cites W2999075180 @default.
• W4309287427 cites W3004047739 @default.
• W4309287427 cites W3008815647 @default.
• W4309287427 cites W3037485026 @default.
• W4309287427 cites W3080711273 @default.
• W4309287427 cites W3082098875 @default.
• W4309287427 cites W3092222842 @default.
• W4309287427 cites W3092883779 @default.
• W4309287427 cites W3093895805 @default.
• W4309287427 cites W3102476541 @default.
• W4309287427 cites W3120973509 @default.
• W4309287427 cites W3123132532 @default.
• W4309287427 cites W3130163837 @default.
• W4309287427 cites W3133841051 @default.
• W4309287427 cites W3134790670 @default.
• W4309287427 cites W3143397222 @default.
• W4309287427 cites W3153764057 @default.
• W4309287427 cites W3161806626 @default.
• W4309287427 cites W3172479371 @default.
• W4309287427 cites W3180041642 @default.
• W4309287427 cites W3182779719 @default.
• W4309287427 cites W3185551827 @default.
• W4309287427 cites W3198148990 @default.
• W4309287427 doi "https://doi.org/10.1016/j.tws.2022.110318" @default.
• W4309287427 hasPublicationYear "2023" @default.
• W4309287427 type Work @default.
• W4309287427 citedByCount "29" @default.
• W4309287427 countsByYear W43092874272023 @default.
• W4309287427 crossrefType "journal-article" @default.
• W4309287427 hasAuthorship W4309287427A5004355445 @default.
• W4309287427 hasAuthorship W4309287427A5006111271 @default.
• W4309287427 hasAuthorship W4309287427A5019556442 @default.
• W4309287427 hasAuthorship W4309287427A5028884885 @default.
• W4309287427 hasAuthorship W4309287427A5087269163 @default.
• W4309287427 hasConcept C111919701 @default.
• W4309287427 hasConcept C119857082 @default.
• W4309287427 hasConcept C127413603 @default.
• W4309287427 hasConcept C138673069 @default.
• W4309287427 hasConcept C154945302 @default.
• W4309287427 hasConcept C159985019 @default.
• W4309287427 hasConcept C165696696 @default.

• next