FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386413251> ?p ?o ?g. }

• W4386413251 endingPage "101390" @default.
• W4386413251 startingPage "101390" @default.
• W4386413251 abstract "Sustainable development of infrastructure requires the efficient and optimized utilization of nano-silica in concrete applications. Thus, current study aims to explore how the dosages of colloidal nano-silica (CNS) and its surface area affect the mechanical performance of cementitious mortar composites. To assess the compressive strength with aging (7, 28 and 56 days), CNS with different surface areas was used as cement substitutes at three different dosages levels. Additionally, to optimize nano-silica dosages and evaluate the outcomes of the experiment, statistical modelling based on Response Surface Methodology (RSM) and machine learning-based Artificial Neural Network (ANN) were applied. In comparison to the control and all other mixtures, the compressive strength was improved (51.5 MPa, 70.2 MPa, and 73.2 MPa) at 7, 28, and 56 days, respectively, by the CNS with higher surface area (CNS500) at their lower dosages (3%). However, at medium (5%) and high dosages (10%), CNS with a lower surface area (CNS80) resulted in better compressive strength (71.9 MPa, 87 MPa, and 90 MPa) at 7, 28, and 56 days, respectively, comparatively to control as well as all other mixtures. These findings were supported by both the RSM and ANN models, with the ANN model outperforming the RSM model in terms of performance. The ANN model was trained and validated using 70% and 30% of the data, respectively. For the validation data of ANN model, the Coefficient of Determination (R), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE) were revealed as 0.925, 2.263 and 2.974 MPa, respectively. Whereas, for the quadratic RSM models, these values were determined as 0.86, 4.485, and 5.45 MPa, respectively. The results of statistical models validated the experimental results, thus showing that the developed RSM and ANN can be used as reliable prediction model to optimize CNS use in producing stronger and more sustainable concrete." @default.
• W4386413251 created "2023-09-05" @default.
• W4386413251 creator A5016747418 @default.
• W4386413251 creator A5053593782 @default.
• W4386413251 creator A5076760648 @default.
• W4386413251 creator A5077858435 @default.
• W4386413251 creator A5086127179 @default.
• W4386413251 date "2023-12-01" @default.
• W4386413251 modified "2023-10-13" @default.
• W4386413251 title "Optimization of colloidal nano-silica based cementitious mortar composites using RSM and ANN approaches" @default.
• W4386413251 cites W1450679494 @default.
• W4386413251 cites W1972368062 @default.
• W4386413251 cites W1993355127 @default.
• W4386413251 cites W1997154979 @default.
• W4386413251 cites W2000282519 @default.
• W4386413251 cites W2001705326 @default.
• W4386413251 cites W2001718996 @default.
• W4386413251 cites W2023322905 @default.
• W4386413251 cites W2034549154 @default.
• W4386413251 cites W2036669843 @default.
• W4386413251 cites W2060012482 @default.
• W4386413251 cites W2061061894 @default.
• W4386413251 cites W2068357931 @default.
• W4386413251 cites W2074624200 @default.
• W4386413251 cites W2076669121 @default.
• W4386413251 cites W2094666564 @default.
• W4386413251 cites W2166941621 @default.
• W4386413251 cites W2182257758 @default.
• W4386413251 cites W2199570905 @default.
• W4386413251 cites W2288173420 @default.
• W4386413251 cites W2308338642 @default.
• W4386413251 cites W2571013152 @default.
• W4386413251 cites W2587935505 @default.
• W4386413251 cites W2729020886 @default.
• W4386413251 cites W2756345397 @default.
• W4386413251 cites W2763124867 @default.
• W4386413251 cites W2769719797 @default.
• W4386413251 cites W2796903017 @default.
• W4386413251 cites W2801963403 @default.
• W4386413251 cites W2803641162 @default.
• W4386413251 cites W2809383412 @default.
• W4386413251 cites W2809828967 @default.
• W4386413251 cites W2810093490 @default.
• W4386413251 cites W2886265879 @default.
• W4386413251 cites W2896896664 @default.
• W4386413251 cites W2900094937 @default.
• W4386413251 cites W2911784953 @default.
• W4386413251 cites W2922302395 @default.
• W4386413251 cites W2944349841 @default.
• W4386413251 cites W2946336797 @default.
• W4386413251 cites W2952693924 @default.
• W4386413251 cites W2965172889 @default.
• W4386413251 cites W2970588503 @default.
• W4386413251 cites W2973274055 @default.
• W4386413251 cites W3013358932 @default.
• W4386413251 cites W3017964923 @default.
• W4386413251 cites W3044451272 @default.
• W4386413251 cites W3084085223 @default.
• W4386413251 cites W3106412577 @default.
• W4386413251 cites W3110694479 @default.
• W4386413251 cites W3115720582 @default.
• W4386413251 cites W3133178558 @default.
• W4386413251 cites W3134831403 @default.
• W4386413251 cites W3147191068 @default.
• W4386413251 cites W3162629243 @default.
• W4386413251 cites W3166078858 @default.
• W4386413251 cites W3175272552 @default.
• W4386413251 cites W3176234837 @default.
• W4386413251 cites W3184697131 @default.
• W4386413251 cites W3201401131 @default.
• W4386413251 cites W4200542590 @default.
• W4386413251 cites W4200550025 @default.
• W4386413251 cites W4210319269 @default.
• W4386413251 cites W4282578608 @default.
• W4386413251 cites W4283643497 @default.
• W4386413251 cites W4309214857 @default.
• W4386413251 cites W4382725861 @default.
• W4386413251 doi "https://doi.org/10.1016/j.rineng.2023.101390" @default.
• W4386413251 hasPublicationYear "2023" @default.
• W4386413251 type Work @default.
• W4386413251 citedByCount "2" @default.
• W4386413251 crossrefType "journal-article" @default.
• W4386413251 hasAuthorship W4386413251A5016747418 @default.
• W4386413251 hasAuthorship W4386413251A5053593782 @default.
• W4386413251 hasAuthorship W4386413251A5076760648 @default.
• W4386413251 hasAuthorship W4386413251A5077858435 @default.
• W4386413251 hasAuthorship W4386413251A5086127179 @default.
• W4386413251 hasBestOaLocation W43864132511 @default.
• W4386413251 hasConcept C105795698 @default.
• W4386413251 hasConcept C119857082 @default.
• W4386413251 hasConcept C128990827 @default.
• W4386413251 hasConcept C130767629 @default.
• W4386413251 hasConcept C139945424 @default.
• W4386413251 hasConcept C150077022 @default.
• W4386413251 hasConcept C159985019 @default.
• W4386413251 hasConcept C162156334 @default.
• W4386413251 hasConcept C192562407 @default.
• W4386413251 hasConcept C2779666059 @default.

• next