Matches in SemOpenAlex for { <https://semopenalex.org/work/W2896697524> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W2896697524 endingPage "137" @default.
- W2896697524 startingPage "130" @default.
- W2896697524 abstract "This study is focused on determining the potential of using Deep Neural Networks (DNNs) to predict the ultimate bearing capacity of shallow foundation in situations when the experimental data which may be used to train networks is scarce. Two experiments involving testing over 17,000 networks were conducted. The first experiment was aimed at comparing the accuracy of shallow neural networks and DNNs predictions. It shows that when the experimental dataset used for preparing models is small then DNNs have a significant advantage over shallow networks. The second experiment was conducted to compare the performance of DNNs consisting of different number of neurons and layers. Obtained results indicate that the optimal number of layers varies between 5 to 7. Networks with less and–surprisingly–more layers obtain lower accuracy. Moreover, the number of neurons in DNN has a lower impact on the prediction accuracy than the number of DNN’s layers. DNNs perform very well, even when trained with only 6 samples. Basing on the results it seems that when predicting the ultimate bearing capacity with Artificial Neural Network (ANN) models obtaining small but high-quality experimental training datasets instead of large training datasets affected by a higher error is an advisable approach." @default.
- W2896697524 created "2018-10-26" @default.
- W2896697524 creator A5036183693 @default.
- W2896697524 creator A5056704316 @default.
- W2896697524 date "2018-12-03" @default.
- W2896697524 modified "2023-10-16" @default.
- W2896697524 title "The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations trained on Scarce Data" @default.
- W2896697524 cites W1702751964 @default.
- W2896697524 cites W1758138418 @default.
- W2896697524 cites W1971222658 @default.
- W2896697524 cites W1972417871 @default.
- W2896697524 cites W1978820282 @default.
- W2896697524 cites W1980321538 @default.
- W2896697524 cites W1985579839 @default.
- W2896697524 cites W1987742500 @default.
- W2896697524 cites W1988115241 @default.
- W2896697524 cites W1999004211 @default.
- W2896697524 cites W2017159066 @default.
- W2896697524 cites W2020277534 @default.
- W2896697524 cites W2020738457 @default.
- W2896697524 cites W2021744425 @default.
- W2896697524 cites W2024761716 @default.
- W2896697524 cites W2035941278 @default.
- W2896697524 cites W2037942342 @default.
- W2896697524 cites W2045996350 @default.
- W2896697524 cites W2055054195 @default.
- W2896697524 cites W2069468343 @default.
- W2896697524 cites W2076063813 @default.
- W2896697524 cites W2077426635 @default.
- W2896697524 cites W2078137712 @default.
- W2896697524 cites W2081626793 @default.
- W2896697524 cites W2103496339 @default.
- W2896697524 cites W2112510034 @default.
- W2896697524 cites W2115754486 @default.
- W2896697524 cites W2139990357 @default.
- W2896697524 cites W2607076864 @default.
- W2896697524 cites W2751094408 @default.
- W2896697524 cites W2754894823 @default.
- W2896697524 cites W2790482354 @default.
- W2896697524 cites W2790921492 @default.
- W2896697524 cites W2794716106 @default.
- W2896697524 cites W2795024812 @default.
- W2896697524 cites W2911546748 @default.
- W2896697524 doi "https://doi.org/10.1007/s12205-018-2636-4" @default.
- W2896697524 hasPublicationYear "2018" @default.
- W2896697524 type Work @default.
- W2896697524 sameAs 2896697524 @default.
- W2896697524 citedByCount "18" @default.
- W2896697524 countsByYear W28966975242019 @default.
- W2896697524 countsByYear W28966975242020 @default.
- W2896697524 countsByYear W28966975242021 @default.
- W2896697524 countsByYear W28966975242022 @default.
- W2896697524 countsByYear W28966975242023 @default.
- W2896697524 crossrefType "journal-article" @default.
- W2896697524 hasAuthorship W2896697524A5036183693 @default.
- W2896697524 hasAuthorship W2896697524A5056704316 @default.
- W2896697524 hasBestOaLocation W28966975242 @default.
- W2896697524 hasConcept C119857082 @default.
- W2896697524 hasConcept C153180895 @default.
- W2896697524 hasConcept C154945302 @default.
- W2896697524 hasConcept C199978012 @default.
- W2896697524 hasConcept C2984842247 @default.
- W2896697524 hasConcept C41008148 @default.
- W2896697524 hasConcept C50644808 @default.
- W2896697524 hasConcept C51632099 @default.
- W2896697524 hasConceptScore W2896697524C119857082 @default.
- W2896697524 hasConceptScore W2896697524C153180895 @default.
- W2896697524 hasConceptScore W2896697524C154945302 @default.
- W2896697524 hasConceptScore W2896697524C199978012 @default.
- W2896697524 hasConceptScore W2896697524C2984842247 @default.
- W2896697524 hasConceptScore W2896697524C41008148 @default.
- W2896697524 hasConceptScore W2896697524C50644808 @default.
- W2896697524 hasConceptScore W2896697524C51632099 @default.
- W2896697524 hasIssue "1" @default.
- W2896697524 hasLocation W28966975241 @default.
- W2896697524 hasLocation W28966975242 @default.
- W2896697524 hasLocation W28966975243 @default.
- W2896697524 hasOpenAccess W2896697524 @default.
- W2896697524 hasPrimaryLocation W28966975241 @default.
- W2896697524 hasRelatedWork W2773120646 @default.
- W2896697524 hasRelatedWork W2791691546 @default.
- W2896697524 hasRelatedWork W2950066684 @default.
- W2896697524 hasRelatedWork W3012234327 @default.
- W2896697524 hasRelatedWork W3203168320 @default.
- W2896697524 hasRelatedWork W4288853838 @default.
- W2896697524 hasRelatedWork W4298388782 @default.
- W2896697524 hasRelatedWork W4322008322 @default.
- W2896697524 hasRelatedWork W4379255972 @default.
- W2896697524 hasRelatedWork W1629725936 @default.
- W2896697524 hasVolume "23" @default.
- W2896697524 isParatext "false" @default.
- W2896697524 isRetracted "false" @default.
- W2896697524 magId "2896697524" @default.
- W2896697524 workType "article" @default.