FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4295778093> ?p ?o ?g. }

• W4295778093 endingPage "104461" @default.
• W4295778093 startingPage "104461" @default.
• W4295778093 abstract "Fiber-reinforced polymer (FRP) composites have recently been considered in the field of structural engineering as one of the best alternatives to conventional steel reinforcement due to their high tensile strength, lightweight, cost-effectiveness, and superior corrosion resistance. However, the variation in FRP physical and mechanical characteristics among the different FRP types and manufacturers makes it difficult to predict the strength of FRP-reinforced concrete (RC) members. For that reason, an efficient prediction tool was developed for a fast, accurate, and intelligent (FAI) prediction of the flexural capacity of FRP-RC beams based on the result of an optimized super-learner machine learning (ML) model. A database of the experimental results on the flexural strength of FRP-RC beams was compiled and randomly split into 80% train and 20% test sets. Six factors were considered in the model; namely, width and effective depth of the beam, concrete compressive strength, FRP flexural reinforcement ratio, FRP modulus of elasticity, and FRP ultimate tensile strength. Grid search is combined with a 10-fold cross-validation to optimize the hyperparameters of the ML models. The prediction capability of the proposed super-learner ML model was benchmarked against boosting- and tree-based ML models, such as classification and regression trees, adaptive boosting, gradient boosted decision trees, and extreme gradient boosting. Moreover, a comparison with the existing code and guideline equations showed that the proposed super-learner ML model provided the most desirable prediction of the flexural capacity of FRP-RC beams." @default.
• W4295778093 created "2022-09-15" @default.
• W4295778093 creator A5044957323 @default.
• W4295778093 creator A5048694666 @default.
• W4295778093 creator A5059718549 @default.
• W4295778093 creator A5081522084 @default.
• W4295778093 date "2022-12-01" @default.
• W4295778093 modified "2023-10-14" @default.
• W4295778093 title "FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model" @default.
• W4295778093 cites W1141096245 @default.
• W4295778093 cites W151108041 @default.
• W4295778093 cites W1584236903 @default.
• W4295778093 cites W1973484369 @default.
• W4295778093 cites W1976940329 @default.
• W4295778093 cites W1994463844 @default.
• W4295778093 cites W2000548672 @default.
• W4295778093 cites W2024618809 @default.
• W4295778093 cites W2025355899 @default.
• W4295778093 cites W2043574056 @default.
• W4295778093 cites W2050230332 @default.
• W4295778093 cites W2060092722 @default.
• W4295778093 cites W2060254212 @default.
• W4295778093 cites W2065566730 @default.
• W4295778093 cites W2067494269 @default.
• W4295778093 cites W2074433865 @default.
• W4295778093 cites W2088231485 @default.
• W4295778093 cites W2089834397 @default.
• W4295778093 cites W2098806753 @default.
• W4295778093 cites W2121043290 @default.
• W4295778093 cites W2131898799 @default.
• W4295778093 cites W2137226359 @default.
• W4295778093 cites W2558193118 @default.
• W4295778093 cites W2905178864 @default.
• W4295778093 cites W2911710334 @default.
• W4295778093 cites W2947180465 @default.
• W4295778093 cites W2964772981 @default.
• W4295778093 cites W2974551902 @default.
• W4295778093 cites W2981416566 @default.
• W4295778093 cites W2989434656 @default.
• W4295778093 cites W2993954761 @default.
• W4295778093 cites W3006597564 @default.
• W4295778093 cites W3048633163 @default.
• W4295778093 cites W3085100981 @default.
• W4295778093 cites W3088849575 @default.
• W4295778093 cites W3089194252 @default.
• W4295778093 cites W3102476541 @default.
• W4295778093 cites W3107262792 @default.
• W4295778093 cites W3133841051 @default.
• W4295778093 cites W3155034689 @default.
• W4295778093 cites W3159242461 @default.
• W4295778093 cites W3180041642 @default.
• W4295778093 cites W3185551827 @default.
• W4295778093 cites W3186710883 @default.
• W4295778093 cites W3190908459 @default.
• W4295778093 cites W3196585211 @default.
• W4295778093 cites W3198148990 @default.
• W4295778093 cites W3198710620 @default.
• W4295778093 cites W3202931876 @default.
• W4295778093 cites W3214196902 @default.
• W4295778093 cites W4207073938 @default.
• W4295778093 cites W4213119690 @default.
• W4295778093 cites W4220946337 @default.
• W4295778093 cites W4233056867 @default.
• W4295778093 cites W4292581477 @default.
• W4295778093 doi "https://doi.org/10.1016/j.mtcomm.2022.104461" @default.
• W4295778093 hasPublicationYear "2022" @default.
• W4295778093 type Work @default.
• W4295778093 citedByCount "16" @default.
• W4295778093 countsByYear W42957780932022 @default.
• W4295778093 countsByYear W42957780932023 @default.
• W4295778093 crossrefType "journal-article" @default.
• W4295778093 hasAuthorship W4295778093A5044957323 @default.
• W4295778093 hasAuthorship W4295778093A5048694666 @default.
• W4295778093 hasAuthorship W4295778093A5059718549 @default.
• W4295778093 hasAuthorship W4295778093A5081522084 @default.
• W4295778093 hasBestOaLocation W42957780931 @default.
• W4295778093 hasConcept C112950240 @default.
• W4295778093 hasConcept C127413603 @default.
• W4295778093 hasConcept C159985019 @default.
• W4295778093 hasConcept C168834538 @default.
• W4295778093 hasConcept C178405089 @default.
• W4295778093 hasConcept C192562407 @default.
• W4295778093 hasConcept C41008148 @default.
• W4295778093 hasConcept C66938386 @default.
• W4295778093 hasConcept C76344452 @default.
• W4295778093 hasConceptScore W4295778093C112950240 @default.
• W4295778093 hasConceptScore W4295778093C127413603 @default.
• W4295778093 hasConceptScore W4295778093C159985019 @default.
• W4295778093 hasConceptScore W4295778093C168834538 @default.
• W4295778093 hasConceptScore W4295778093C178405089 @default.
• W4295778093 hasConceptScore W4295778093C192562407 @default.
• W4295778093 hasConceptScore W4295778093C41008148 @default.
• W4295778093 hasConceptScore W4295778093C66938386 @default.
• W4295778093 hasConceptScore W4295778093C76344452 @default.
• W4295778093 hasFunder F4320309815 @default.
• W4295778093 hasFunder F4320332753 @default.
• W4295778093 hasLocation W42957780931 @default.
• W4295778093 hasLocation W42957780932 @default.

• next