FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313338210> ?p ?o ?g. }

• W4313338210 endingPage "2160" @default.
• W4313338210 startingPage "2160" @default.
• W4313338210 abstract "Concrete is a heterogeneous material that is extensively used as a construction material. However, to improve the toughness and mechanical properties of concrete, various ingredients (fillers) have been added in the past. The addition of nanomaterials for the improvement of the aforementioned properties has attracted many researchers worldwide. The high surface area, high reactivity, and finer size of various nanomaterials have made them preferable for the enhancement of durability, as well as compressive and flexural strength. The aim of the current research is focused on the estimation of compressive strength for the concrete modified with various nanomaterials using two machine learning techniques, namely decision tree technique (DTT) and random forest technique (RFT), and comparison with existing models. The database is collected for different percentages of four major widely used nanomaterials in concrete, i.e., carbon nanotubes, nano silica, nano clay, and nano alumina. The other four input variables used for the calibration of the models are: cement content (CC); water–cement ratio (W/C); fine aggregate, i.e., sand (FA); and coarse aggregate (CA). Both DTT and RFT models were developed for 94 collected experimental datasets from the published literature. The predicted results are further validated through K-fold cross-validation using correlation coefficient (R2), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error, relative square error (RRMSE), and performance index factor (PiF). The RFT model was found to have the lowermost MAE 3.253, RMSE 4.387, RRMSE 0.0803, and performance index factor (PiF) 0.0061. In comparison, predicted results overall revealed better performance and accuracy for the RFT-developed models than for DTT and gene expression programming (GEP) models, as illustrated by their high R2 value, equal to 0.96, while the R2 value for DTT and GEP was found 0.94 and 0.86, respectively." @default.
• W4313338210 created "2023-01-06" @default.
• W4313338210 creator A5002835208 @default.
• W4313338210 creator A5003971462 @default.
• W4313338210 creator A5007452190 @default.
• W4313338210 creator A5023000265 @default.
• W4313338210 creator A5055216571 @default.
• W4313338210 creator A5058875606 @default.
• W4313338210 date "2022-12-07" @default.
• W4313338210 modified "2023-10-15" @default.
• W4313338210 title "Development of the New Prediction Models for the Compressive Strength of Nanomodified Concrete Using Novel Machine Learning Techniques" @default.
• W4313338210 cites W1965994158 @default.
• W4313338210 cites W1982759439 @default.
• W4313338210 cites W1985372952 @default.
• W4313338210 cites W1989788683 @default.
• W4313338210 cites W1998253841 @default.
• W4313338210 cites W2016903150 @default.
• W4313338210 cites W2023106675 @default.
• W4313338210 cites W2038808304 @default.
• W4313338210 cites W2052319596 @default.
• W4313338210 cites W2055124233 @default.
• W4313338210 cites W2062797413 @default.
• W4313338210 cites W2066452020 @default.
• W4313338210 cites W2128207034 @default.
• W4313338210 cites W2152063651 @default.
• W4313338210 cites W2171308009 @default.
• W4313338210 cites W2260876573 @default.
• W4313338210 cites W2334971295 @default.
• W4313338210 cites W2525592260 @default.
• W4313338210 cites W2531209390 @default.
• W4313338210 cites W2565428772 @default.
• W4313338210 cites W2619220912 @default.
• W4313338210 cites W2734632351 @default.
• W4313338210 cites W2740327486 @default.
• W4313338210 cites W2766550705 @default.
• W4313338210 cites W2769217008 @default.
• W4313338210 cites W2781807718 @default.
• W4313338210 cites W2801832296 @default.
• W4313338210 cites W2810849241 @default.
• W4313338210 cites W2886514679 @default.
• W4313338210 cites W2887104628 @default.
• W4313338210 cites W2888180049 @default.
• W4313338210 cites W2895610436 @default.
• W4313338210 cites W2902261025 @default.
• W4313338210 cites W2905023038 @default.
• W4313338210 cites W2911964244 @default.
• W4313338210 cites W2917355953 @default.
• W4313338210 cites W2921499992 @default.
• W4313338210 cites W2921992265 @default.
• W4313338210 cites W2925700532 @default.
• W4313338210 cites W2946148730 @default.
• W4313338210 cites W2947325973 @default.
• W4313338210 cites W2964938350 @default.
• W4313338210 cites W2966786596 @default.
• W4313338210 cites W2970188861 @default.
• W4313338210 cites W2977290224 @default.
• W4313338210 cites W2998585397 @default.
• W4313338210 cites W3007270451 @default.
• W4313338210 cites W3007888137 @default.
• W4313338210 cites W3009520784 @default.
• W4313338210 cites W3044078176 @default.
• W4313338210 cites W3045021431 @default.
• W4313338210 cites W3081342661 @default.
• W4313338210 cites W3091887699 @default.
• W4313338210 cites W3094556934 @default.
• W4313338210 cites W3097683692 @default.
• W4313338210 cites W3113995462 @default.
• W4313338210 cites W3118440994 @default.
• W4313338210 cites W3126997349 @default.
• W4313338210 cites W3136814736 @default.
• W4313338210 cites W3155180629 @default.
• W4313338210 cites W3181335461 @default.
• W4313338210 cites W3184697131 @default.
• W4313338210 cites W3192391422 @default.
• W4313338210 cites W3200673652 @default.
• W4313338210 cites W3204631755 @default.
• W4313338210 cites W3212595790 @default.
• W4313338210 cites W4205531097 @default.
• W4313338210 cites W4214941753 @default.
• W4313338210 cites W4220919884 @default.
• W4313338210 cites W4221019965 @default.
• W4313338210 cites W4225006194 @default.
• W4313338210 cites W4281768943 @default.
• W4313338210 cites W4283031888 @default.
• W4313338210 cites W4284969977 @default.
• W4313338210 cites W4288699230 @default.
• W4313338210 cites W608402566 @default.
• W4313338210 cites W2243486372 @default.
• W4313338210 doi "https://doi.org/10.3390/buildings12122160" @default.
• W4313338210 hasPublicationYear "2022" @default.
• W4313338210 type Work @default.
• W4313338210 citedByCount "10" @default.
• W4313338210 countsByYear W43133382102023 @default.
• W4313338210 crossrefType "journal-article" @default.
• W4313338210 hasAuthorship W4313338210A5002835208 @default.
• W4313338210 hasAuthorship W4313338210A5003971462 @default.
• W4313338210 hasAuthorship W4313338210A5007452190 @default.
• W4313338210 hasAuthorship W4313338210A5023000265 @default.

• next