FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1560731656> ?p ?o ?g. }

• W1560731656 endingPage "66" @default.
• W1560731656 startingPage "59" @default.
• W1560731656 abstract "Over the last few years, artificial neural networks (ANNs) have been used successfully for modelling all aspects of geotechnical engineering problems. ANNs are a form of artificial intelligence which attempt to mimic the function of the human brain and nervous system. ANNs are well suited to model the complex behaviour of most geotechnical engineering problems. The purpose of this paper was to assess the layering of subsurface soil using ANNs. Assessing the structure of soil layers on a site, depending on the extent of the study area, requires drilling several boreholes and performing several tests which demand considerable time and money. Increasing the knowledge of soil layer properties between boreholes leads to improved understanding of the near-surface geology. ANNs learn from data examples presented to them in order to capture the subtle functional data relationships, even if the underlying relationships are unknown or the physical meaning is difficult to explain. This paper focuses on the information gathered from the boreholes in a range of 40 square kilometres of Babol City in the north of Iran. The data was collected and classified in order to determine the characteristics of the soil layers. To later classify the different layers at different depths and to determine the thickness of each layer at a specified depth, multi-layer neural networks were trained separately. To quantify the neural network performance in estimating the changes of soil layers, some data from the test boreholes was presented to the network for the first time, and the results of neural networks were compared with actual data obtained from site investigations. The results show a high degree of accuracy in prediction by ANN models." @default.
• W1560731656 created "2016-06-24" @default.
• W1560731656 creator A5005227585 @default.
• W1560731656 creator A5005826997 @default.
• W1560731656 creator A5009300738 @default.
• W1560731656 creator A5076950213 @default.
• W1560731656 date "2015-01-01" @default.
• W1560731656 modified "2023-09-27" @default.
• W1560731656 title "Mapping of soil layers using artificial neural network (case study of Babol, northern Iran)" @default.
• W1560731656 cites W1765523475 @default.
• W1560731656 cites W1977168277 @default.
• W1560731656 cites W2002067303 @default.
• W1560731656 cites W2002923373 @default.
• W1560731656 cites W2049426600 @default.
• W1560731656 cites W2052007416 @default.
• W1560731656 cites W2064200713 @default.
• W1560731656 cites W2080961912 @default.
• W1560731656 cites W2091336970 @default.
• W1560731656 cites W2096885477 @default.
• W1560731656 cites W2117244044 @default.
• W1560731656 cites W2122492962 @default.
• W1560731656 cites W2171409491 @default.
• W1560731656 cites W2217658055 @default.
• W1560731656 cites W2741025094 @default.
• W1560731656 cites W2763276763 @default.
• W1560731656 doi "https://doi.org/10.17159/2309-8775/2015/v57n1a6" @default.
• W1560731656 hasPublicationYear "2015" @default.
• W1560731656 type Work @default.
• W1560731656 sameAs 1560731656 @default.
• W1560731656 citedByCount "8" @default.
• W1560731656 countsByYear W15607316562018 @default.
• W1560731656 countsByYear W15607316562020 @default.
• W1560731656 countsByYear W15607316562021 @default.
• W1560731656 countsByYear W15607316562022 @default.
• W1560731656 countsByYear W15607316562023 @default.
• W1560731656 crossrefType "journal-article" @default.
• W1560731656 hasAuthorship W1560731656A5005227585 @default.
• W1560731656 hasAuthorship W1560731656A5005826997 @default.
• W1560731656 hasAuthorship W1560731656A5009300738 @default.
• W1560731656 hasAuthorship W1560731656A5076950213 @default.
• W1560731656 hasBestOaLocation W15607316561 @default.
• W1560731656 hasConcept C127313418 @default.
• W1560731656 hasConcept C150560799 @default.
• W1560731656 hasConcept C154945302 @default.
• W1560731656 hasConcept C176055353 @default.
• W1560731656 hasConcept C178790620 @default.
• W1560731656 hasConcept C185592680 @default.
• W1560731656 hasConcept C187320778 @default.
• W1560731656 hasConcept C2779227376 @default.
• W1560731656 hasConcept C41008148 @default.
• W1560731656 hasConcept C50644808 @default.
• W1560731656 hasConcept C59822182 @default.
• W1560731656 hasConcept C86803240 @default.
• W1560731656 hasConceptScore W1560731656C127313418 @default.
• W1560731656 hasConceptScore W1560731656C150560799 @default.
• W1560731656 hasConceptScore W1560731656C154945302 @default.
• W1560731656 hasConceptScore W1560731656C176055353 @default.
• W1560731656 hasConceptScore W1560731656C178790620 @default.
• W1560731656 hasConceptScore W1560731656C185592680 @default.
• W1560731656 hasConceptScore W1560731656C187320778 @default.
• W1560731656 hasConceptScore W1560731656C2779227376 @default.
• W1560731656 hasConceptScore W1560731656C41008148 @default.
• W1560731656 hasConceptScore W1560731656C50644808 @default.
• W1560731656 hasConceptScore W1560731656C59822182 @default.
• W1560731656 hasConceptScore W1560731656C86803240 @default.
• W1560731656 hasIssue "1" @default.
• W1560731656 hasLocation W15607316561 @default.
• W1560731656 hasOpenAccess W1560731656 @default.
• W1560731656 hasPrimaryLocation W15607316561 @default.
• W1560731656 hasRelatedWork W1560731656 @default.
• W1560731656 hasRelatedWork W2020160277 @default.
• W1560731656 hasRelatedWork W2129841676 @default.
• W1560731656 hasRelatedWork W2159771631 @default.
• W1560731656 hasRelatedWork W2170482168 @default.
• W1560731656 hasRelatedWork W2185170806 @default.
• W1560731656 hasRelatedWork W2515435281 @default.
• W1560731656 hasRelatedWork W2548882633 @default.
• W1560731656 hasRelatedWork W2804097049 @default.
• W1560731656 hasRelatedWork W77873814 @default.
• W1560731656 hasVolume "57" @default.
• W1560731656 isParatext "false" @default.
• W1560731656 isRetracted "false" @default.
• W1560731656 magId "1560731656" @default.
• W1560731656 workType "article" @default.