FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4320341943> ?p ?o ?g. }

• W4320341943 endingPage "e01890" @default.
• W4320341943 startingPage "e01890" @default.
• W4320341943 abstract "Compressive strength determination of high-performance concrete (HPC) is necessary for its practical usage. However, experimental testing for this purpose is resource intensive and time-consuming. Recently, machine learning has emerged in this field, especially data-driven modeling. In this study, a novel hybrid model for the compressive strength prediction of HPC is developed using the cascade forward neural network (CFNN) and artificial bee colony (ABC) optimization. The hybrid model used the ABC optimization method to select the optimal architecture of the neural network. A comprehensive database of 2171 data points containing information about cement, blast furnace slag, fly ash, water, coarse aggregate, sand, and age as input variables, and compressive strength as output variable is used to develop the model. Results indicated that the optimal neural network architecture selected by the ABC method consists of 2 layers and the developed model (CFNN-ABC) could accurately predict the compressive strength of HPC with correlation (R) and determination coefficients (R2) of 0.976 and 0.953, respectively. The feature importance of the model revealed that the cement and sand were more influential features as compared to the other features. The partial dependence analysis demonstrated the effect of variation in input parameters on the attained compressive strength. Furthermore, the model validation with previously developed models using performance indices showed that the proposed hybrid model outperformed other models in all performance indices including root mean square error (RMSE ∼ 4.04) and mean absolute error (MAE ∼ 3.10). Therefore, the present work provides a novel and efficient option to predict the compressive strength of HPC which can aid in the design of sustainable infrastructures without going through costly and time-intensive experimentation." @default.
• W4320341943 created "2023-02-13" @default.
• W4320341943 creator A5058276369 @default.
• W4320341943 creator A5060447844 @default.
• W4320341943 creator A5067496461 @default.
• W4320341943 date "2023-07-01" @default.
• W4320341943 modified "2023-10-12" @default.
• W4320341943 title "A hybrid data-driven and metaheuristic optimization approach for the compressive strength prediction of high-performance concrete" @default.
• W4320341943 cites W1592968810 @default.
• W4320341943 cites W1752416898 @default.
• W4320341943 cites W1979721602 @default.
• W4320341943 cites W2014512646 @default.
• W4320341943 cites W2037669400 @default.
• W4320341943 cites W2045259560 @default.
• W4320341943 cites W2054557393 @default.
• W4320341943 cites W2058435730 @default.
• W4320341943 cites W2061933243 @default.
• W4320341943 cites W2067930403 @default.
• W4320341943 cites W2073093937 @default.
• W4320341943 cites W2080406748 @default.
• W4320341943 cites W2086832245 @default.
• W4320341943 cites W2114368359 @default.
• W4320341943 cites W2114652055 @default.
• W4320341943 cites W2143560894 @default.
• W4320341943 cites W2144494611 @default.
• W4320341943 cites W2154631075 @default.
• W4320341943 cites W2164888938 @default.
• W4320341943 cites W2474262425 @default.
• W4320341943 cites W2555697849 @default.
• W4320341943 cites W2796265311 @default.
• W4320341943 cites W2803235424 @default.
• W4320341943 cites W2805257321 @default.
• W4320341943 cites W2964938350 @default.
• W4320341943 cites W2977280727 @default.
• W4320341943 cites W2999699150 @default.
• W4320341943 cites W3004315419 @default.
• W4320341943 cites W3026112595 @default.
• W4320341943 cites W3030874143 @default.
• W4320341943 cites W3046169907 @default.
• W4320341943 cites W3080952128 @default.
• W4320341943 cites W3112168709 @default.
• W4320341943 cites W3125850143 @default.
• W4320341943 cites W3128350030 @default.
• W4320341943 cites W3138432961 @default.
• W4320341943 cites W3179009462 @default.
• W4320341943 cites W3198918024 @default.
• W4320341943 cites W4200115633 @default.
• W4320341943 cites W4308889198 @default.
• W4320341943 doi "https://doi.org/10.1016/j.cscm.2023.e01890" @default.
• W4320341943 hasPublicationYear "2023" @default.
• W4320341943 type Work @default.
• W4320341943 citedByCount "2" @default.
• W4320341943 countsByYear W43203419432023 @default.
• W4320341943 crossrefType "journal-article" @default.
• W4320341943 hasAuthorship W4320341943A5058276369 @default.
• W4320341943 hasAuthorship W4320341943A5060447844 @default.
• W4320341943 hasAuthorship W4320341943A5067496461 @default.
• W4320341943 hasBestOaLocation W43203419431 @default.
• W4320341943 hasConcept C105795698 @default.
• W4320341943 hasConcept C109718341 @default.
• W4320341943 hasConcept C11413529 @default.
• W4320341943 hasConcept C119857082 @default.
• W4320341943 hasConcept C122383733 @default.
• W4320341943 hasConcept C138885662 @default.
• W4320341943 hasConcept C139945424 @default.
• W4320341943 hasConcept C150217764 @default.
• W4320341943 hasConcept C154945302 @default.
• W4320341943 hasConcept C159985019 @default.
• W4320341943 hasConcept C192562407 @default.
• W4320341943 hasConcept C2776401178 @default.
• W4320341943 hasConcept C2780092901 @default.
• W4320341943 hasConcept C30407753 @default.
• W4320341943 hasConcept C33923547 @default.
• W4320341943 hasConcept C41008148 @default.
• W4320341943 hasConcept C41895202 @default.
• W4320341943 hasConcept C50644808 @default.
• W4320341943 hasConceptScore W4320341943C105795698 @default.
• W4320341943 hasConceptScore W4320341943C109718341 @default.
• W4320341943 hasConceptScore W4320341943C11413529 @default.
• W4320341943 hasConceptScore W4320341943C119857082 @default.
• W4320341943 hasConceptScore W4320341943C122383733 @default.
• W4320341943 hasConceptScore W4320341943C138885662 @default.
• W4320341943 hasConceptScore W4320341943C139945424 @default.
• W4320341943 hasConceptScore W4320341943C150217764 @default.
• W4320341943 hasConceptScore W4320341943C154945302 @default.
• W4320341943 hasConceptScore W4320341943C159985019 @default.
• W4320341943 hasConceptScore W4320341943C192562407 @default.
• W4320341943 hasConceptScore W4320341943C2776401178 @default.
• W4320341943 hasConceptScore W4320341943C2780092901 @default.
• W4320341943 hasConceptScore W4320341943C30407753 @default.
• W4320341943 hasConceptScore W4320341943C33923547 @default.
• W4320341943 hasConceptScore W4320341943C41008148 @default.
• W4320341943 hasConceptScore W4320341943C41895202 @default.
• W4320341943 hasConceptScore W4320341943C50644808 @default.
• W4320341943 hasLocation W43203419431 @default.
• W4320341943 hasOpenAccess W4320341943 @default.
• W4320341943 hasPrimaryLocation W43203419431 @default.
• W4320341943 hasRelatedWork W2188032833 @default.

• next