FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3204631755> ?p ?o ?g. }

• W3204631755 endingPage "125021" @default.
• W3204631755 startingPage "125021" @default.
• W3204631755 abstract "The cementitious composites have different properties in the changing environment. Thus, knowing their mechanical properties is very important for safety reasons. The most important in the case of concrete is the Compressive strength (CS). To predict the CS of concrete Machine learning (ML) approaches has been essential. This study includes the collection of data from the experimental work and the application of ML techniques to predict the CS of concrete containing fly ash. The chemical and physical properties of all the materials used in this study were evaluated. Although, the emphasis of this research is on the use of supervised machine learning algorithms to forecast the CS of concrete. The Gene expression programming (GEP), Artificial neural network (ANN), and Decision tree (DT) algorithms were investigated for the prediction of outcome (CS). Concrete samples (cylinders) with different mix ratios were cast and tested at various ages to maintain the required data for applying it to run the models. Total 98 data points were collected from the experimental approach, in which seven parameters namely cement, fly ash, superplasticizer, coarse aggregate, fine aggregate, water, and days were taken as input to predict the output which was CS parameter. The experimental data is further validated by mean of k-fold cross-validation using R2, root mean error (RME), and Root mean square error (RMSE). In addition, statistical checks were incorporated to evaluate the model performance. In comparison, the bagging algorithm shows high accuracy towards the prediction of outcome as indicated by its high coefficient correlation (R2) value equals to 0.95, while R2 value for GEP, ANN and DT comes to 0.86, 0.81 and 0.75 respectively." @default.
• W3204631755 created "2021-10-11" @default.
• W3204631755 creator A5001572579 @default.
• W3204631755 creator A5029564828 @default.
• W3204631755 creator A5053161664 @default.
• W3204631755 creator A5057578694 @default.
• W3204631755 creator A5057908066 @default.
• W3204631755 creator A5067574954 @default.
• W3204631755 creator A5078838278 @default.
• W3204631755 date "2021-11-01" @default.
• W3204631755 modified "2023-10-10" @default.
• W3204631755 title "Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms" @default.
• W3204631755 cites W1435587914 @default.
• W3204631755 cites W1970245951 @default.
• W3204631755 cites W1983735453 @default.
• W3204631755 cites W1992749124 @default.
• W3204631755 cites W1997334587 @default.
• W3204631755 cites W2006881475 @default.
• W3204631755 cites W2012316003 @default.
• W3204631755 cites W2027005599 @default.
• W3204631755 cites W2063629125 @default.
• W3204631755 cites W2096232552 @default.
• W3204631755 cites W2099454382 @default.
• W3204631755 cites W2102336703 @default.
• W3204631755 cites W2143528693 @default.
• W3204631755 cites W2418319674 @default.
• W3204631755 cites W2468331410 @default.
• W3204631755 cites W2551200462 @default.
• W3204631755 cites W2582587579 @default.
• W3204631755 cites W2583314205 @default.
• W3204631755 cites W2590209538 @default.
• W3204631755 cites W2602516395 @default.
• W3204631755 cites W2608710759 @default.
• W3204631755 cites W2610090978 @default.
• W3204631755 cites W2758887415 @default.
• W3204631755 cites W2768350808 @default.
• W3204631755 cites W2769217008 @default.
• W3204631755 cites W2791043882 @default.
• W3204631755 cites W2807042118 @default.
• W3204631755 cites W2807464630 @default.
• W3204631755 cites W2886514679 @default.
• W3204631755 cites W2886529392 @default.
• W3204631755 cites W2893482420 @default.
• W3204631755 cites W2911424673 @default.
• W3204631755 cites W2911508326 @default.
• W3204631755 cites W2917355953 @default.
• W3204631755 cites W2925700532 @default.
• W3204631755 cites W2942917447 @default.
• W3204631755 cites W2947117170 @default.
• W3204631755 cites W2952852102 @default.
• W3204631755 cites W2976353133 @default.
• W3204631755 cites W2977280727 @default.
• W3204631755 cites W2998134678 @default.
• W3204631755 cites W3015837740 @default.
• W3204631755 cites W3017201198 @default.
• W3204631755 cites W3038033549 @default.
• W3204631755 cites W3081231874 @default.
• W3204631755 cites W3081342661 @default.
• W3204631755 cites W3087991416 @default.
• W3204631755 cites W3094556934 @default.
• W3204631755 cites W3103799692 @default.
• W3204631755 cites W3113995462 @default.
• W3204631755 cites W3125850143 @default.
• W3204631755 cites W3126997349 @default.
• W3204631755 cites W3131046868 @default.
• W3204631755 cites W3133799028 @default.
• W3204631755 cites W3141768603 @default.
• W3204631755 cites W3159542470 @default.
• W3204631755 doi "https://doi.org/10.1016/j.conbuildmat.2021.125021" @default.
• W3204631755 hasPublicationYear "2021" @default.
• W3204631755 type Work @default.
• W3204631755 sameAs 3204631755 @default.
• W3204631755 citedByCount "122" @default.
• W3204631755 countsByYear W32046317552021 @default.
• W3204631755 countsByYear W32046317552022 @default.
• W3204631755 countsByYear W32046317552023 @default.
• W3204631755 crossrefType "journal-article" @default.
• W3204631755 hasAuthorship W3204631755A5001572579 @default.
• W3204631755 hasAuthorship W3204631755A5029564828 @default.
• W3204631755 hasAuthorship W3204631755A5053161664 @default.
• W3204631755 hasAuthorship W3204631755A5057578694 @default.
• W3204631755 hasAuthorship W3204631755A5057908066 @default.
• W3204631755 hasAuthorship W3204631755A5067574954 @default.
• W3204631755 hasAuthorship W3204631755A5078838278 @default.
• W3204631755 hasBestOaLocation W32046317551 @default.
• W3204631755 hasConcept C105795698 @default.
• W3204631755 hasConcept C11413529 @default.
• W3204631755 hasConcept C119857082 @default.
• W3204631755 hasConcept C128990827 @default.
• W3204631755 hasConcept C139945424 @default.
• W3204631755 hasConcept C159985019 @default.
• W3204631755 hasConcept C192562407 @default.
• W3204631755 hasConcept C192757587 @default.
• W3204631755 hasConcept C2779666059 @default.
• W3204631755 hasConcept C2780092901 @default.
• W3204631755 hasConcept C30407753 @default.
• W3204631755 hasConcept C33923547 @default.
• W3204631755 hasConcept C41008148 @default.

• next