Matches in SemOpenAlex for { <https://semopenalex.org/work/W2783208634> ?p ?o ?g. }
Showing items 1 to 90 of
90
with 100 items per page.
- W2783208634 endingPage "517" @default.
- W2783208634 startingPage "509" @default.
- W2783208634 abstract "In the present study, relationships between in-place density using SPT N-value, compression index (Cc) using liquid limit (LL) and void ratio (e), and cohesion (c) and angle of internal friction (ϕ) using SPT N-value have been established using machine learning techniques. Geotechnical data up to a depth of 50 m from 1053 borehole locations covering almost every district in the state of Haryana have been considered to develop models and statistical correlations. A general trend has been recorded in the observed data and accordingly, the outliers have been excluded. Several models have been developed to establish functional correlations. These correlations have been ranked on the basis their coefficient of determination (R2) value and mean absolute error (MAE). Subsequently, the model with the highest R2 value and minimum mean absolute error has been considered for the development of correlations. Analysis has also been carried out for all the developed models to assess their individual performance. For this purpose, all the developed models have been evaluated by fitting a straight line between observed and modelled values, and in all the cases, a good value of R2 has been observed. The R2 values obtained for all the models range from 0.798 to 0.988. On comparison, it has been observed that the values of geotechnical parameters obtained are in close agreement with the existing work." @default.
- W2783208634 created "2018-01-26" @default.
- W2783208634 creator A5055692727 @default.
- W2783208634 creator A5069042930 @default.
- W2783208634 creator A5083107731 @default.
- W2783208634 date "2018-01-01" @default.
- W2783208634 modified "2023-09-29" @default.
- W2783208634 title "Prediction of Geotechnical Parameters Using Machine Learning Techniques" @default.
- W2783208634 cites W1977886272 @default.
- W2783208634 cites W2047898003 @default.
- W2783208634 cites W2061959659 @default.
- W2783208634 cites W2063629125 @default.
- W2783208634 cites W2066309818 @default.
- W2783208634 cites W2075296044 @default.
- W2783208634 cites W2341916350 @default.
- W2783208634 cites W2513650310 @default.
- W2783208634 cites W2533558339 @default.
- W2783208634 cites W2733535563 @default.
- W2783208634 doi "https://doi.org/10.1016/j.procs.2017.12.066" @default.
- W2783208634 hasPublicationYear "2018" @default.
- W2783208634 type Work @default.
- W2783208634 sameAs 2783208634 @default.
- W2783208634 citedByCount "47" @default.
- W2783208634 countsByYear W27832086342018 @default.
- W2783208634 countsByYear W27832086342019 @default.
- W2783208634 countsByYear W27832086342020 @default.
- W2783208634 countsByYear W27832086342021 @default.
- W2783208634 countsByYear W27832086342022 @default.
- W2783208634 countsByYear W27832086342023 @default.
- W2783208634 crossrefType "journal-article" @default.
- W2783208634 hasAuthorship W2783208634A5055692727 @default.
- W2783208634 hasAuthorship W2783208634A5069042930 @default.
- W2783208634 hasAuthorship W2783208634A5083107731 @default.
- W2783208634 hasBestOaLocation W27832086341 @default.
- W2783208634 hasConcept C104054115 @default.
- W2783208634 hasConcept C105795698 @default.
- W2783208634 hasConcept C127313418 @default.
- W2783208634 hasConcept C128990827 @default.
- W2783208634 hasConcept C139945424 @default.
- W2783208634 hasConcept C150217764 @default.
- W2783208634 hasConcept C150560799 @default.
- W2783208634 hasConcept C159985019 @default.
- W2783208634 hasConcept C164374781 @default.
- W2783208634 hasConcept C178790620 @default.
- W2783208634 hasConcept C185592680 @default.
- W2783208634 hasConcept C187320778 @default.
- W2783208634 hasConcept C188154048 @default.
- W2783208634 hasConcept C192562407 @default.
- W2783208634 hasConcept C204323151 @default.
- W2783208634 hasConcept C33923547 @default.
- W2783208634 hasConcept C41008148 @default.
- W2783208634 hasConcept C79337645 @default.
- W2783208634 hasConceptScore W2783208634C104054115 @default.
- W2783208634 hasConceptScore W2783208634C105795698 @default.
- W2783208634 hasConceptScore W2783208634C127313418 @default.
- W2783208634 hasConceptScore W2783208634C128990827 @default.
- W2783208634 hasConceptScore W2783208634C139945424 @default.
- W2783208634 hasConceptScore W2783208634C150217764 @default.
- W2783208634 hasConceptScore W2783208634C150560799 @default.
- W2783208634 hasConceptScore W2783208634C159985019 @default.
- W2783208634 hasConceptScore W2783208634C164374781 @default.
- W2783208634 hasConceptScore W2783208634C178790620 @default.
- W2783208634 hasConceptScore W2783208634C185592680 @default.
- W2783208634 hasConceptScore W2783208634C187320778 @default.
- W2783208634 hasConceptScore W2783208634C188154048 @default.
- W2783208634 hasConceptScore W2783208634C192562407 @default.
- W2783208634 hasConceptScore W2783208634C204323151 @default.
- W2783208634 hasConceptScore W2783208634C33923547 @default.
- W2783208634 hasConceptScore W2783208634C41008148 @default.
- W2783208634 hasConceptScore W2783208634C79337645 @default.
- W2783208634 hasLocation W27832086341 @default.
- W2783208634 hasOpenAccess W2783208634 @default.
- W2783208634 hasPrimaryLocation W27832086341 @default.
- W2783208634 hasRelatedWork W1967332855 @default.
- W2783208634 hasRelatedWork W1986344740 @default.
- W2783208634 hasRelatedWork W2059808914 @default.
- W2783208634 hasRelatedWork W2087927026 @default.
- W2783208634 hasRelatedWork W2625413331 @default.
- W2783208634 hasRelatedWork W2783208634 @default.
- W2783208634 hasRelatedWork W3005178824 @default.
- W2783208634 hasRelatedWork W3121540092 @default.
- W2783208634 hasRelatedWork W4229016259 @default.
- W2783208634 hasRelatedWork W4286719103 @default.
- W2783208634 hasVolume "125" @default.
- W2783208634 isParatext "false" @default.
- W2783208634 isRetracted "false" @default.
- W2783208634 magId "2783208634" @default.
- W2783208634 workType "article" @default.