Matches in SemOpenAlex for { <https://semopenalex.org/work/W129222777> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W129222777 abstract "Empirical relationships are commonly used in geotechnical engineering design to estimate engineering properties of soils and evaluate the performance of geotechnical structures because the interacting factors and relationship between these factors are not precisely known. This chapter explores the use of a modified Bayesian back-propagation neural network to model the highly nonlinear relationships between interacting factors or variables. The main advantage of neural networks over conventional regression analysis techniques is that the neural network is able to find a best-fit solution without the need to specify the relationship or the form of the relationship between variables. It is, therefore, useful for analyzing problems where there is incomplete understanding of the problem to be solved, but where training data are available, as is the case for many geotechnical engineering problems. With the integration of the Bayesian framework into the back-propagation algorithm, error bars can be calculated for network output instead of just a single output value given in conventional back-propagation neural networks. These error bars indicate the confidence level of the predicted values in relation to the spatial density of the training data. This chapter describes the use of a hybrid neural network that incorporates the genetic algorithm search engine and Bayesian approach in quantifying the uncertainty of the learned model. Some practical examples of its application to pile foundation and retaining wall design are presented to demonstrate the usefulness of this hybrid model." @default.
- W129222777 created "2016-06-24" @default.
- W129222777 creator A5014973570 @default.
- W129222777 creator A5029275951 @default.
- W129222777 date "2013-01-01" @default.
- W129222777 modified "2023-10-05" @default.
- W129222777 title "Geotechnical Applications of Bayesian Neural Networks" @default.
- W129222777 cites W1524706172 @default.
- W129222777 cites W1964810204 @default.
- W129222777 cites W1967679224 @default.
- W129222777 cites W1976867185 @default.
- W129222777 cites W1990814723 @default.
- W129222777 cites W1993460862 @default.
- W129222777 cites W1999835881 @default.
- W129222777 cites W2002096058 @default.
- W129222777 cites W2008448036 @default.
- W129222777 cites W2012218190 @default.
- W129222777 cites W2033793034 @default.
- W129222777 cites W2035972664 @default.
- W129222777 cites W2040622309 @default.
- W129222777 cites W2056760934 @default.
- W129222777 cites W2058224488 @default.
- W129222777 cites W2060391650 @default.
- W129222777 cites W2062220391 @default.
- W129222777 cites W2066380554 @default.
- W129222777 cites W2087070363 @default.
- W129222777 cites W2096590200 @default.
- W129222777 cites W2117670920 @default.
- W129222777 cites W2122145224 @default.
- W129222777 cites W2135364467 @default.
- W129222777 cites W2141116748 @default.
- W129222777 cites W2256578114 @default.
- W129222777 cites W2292272747 @default.
- W129222777 cites W2341236714 @default.
- W129222777 cites W2356872141 @default.
- W129222777 cites W2508019236 @default.
- W129222777 cites W2522469665 @default.
- W129222777 cites W2558321228 @default.
- W129222777 cites W2766736793 @default.
- W129222777 cites W658409286 @default.
- W129222777 cites W88775338 @default.
- W129222777 doi "https://doi.org/10.1016/b978-0-12-398296-4.00011-8" @default.
- W129222777 hasPublicationYear "2013" @default.
- W129222777 type Work @default.
- W129222777 sameAs 129222777 @default.
- W129222777 citedByCount "1" @default.
- W129222777 countsByYear W1292227772020 @default.
- W129222777 crossrefType "book-chapter" @default.
- W129222777 hasAuthorship W129222777A5014973570 @default.
- W129222777 hasAuthorship W129222777A5029275951 @default.
- W129222777 hasConcept C107673813 @default.
- W129222777 hasConcept C127313418 @default.
- W129222777 hasConcept C154945302 @default.
- W129222777 hasConcept C187320778 @default.
- W129222777 hasConcept C33724603 @default.
- W129222777 hasConcept C41008148 @default.
- W129222777 hasConcept C50644808 @default.
- W129222777 hasConceptScore W129222777C107673813 @default.
- W129222777 hasConceptScore W129222777C127313418 @default.
- W129222777 hasConceptScore W129222777C154945302 @default.
- W129222777 hasConceptScore W129222777C187320778 @default.
- W129222777 hasConceptScore W129222777C33724603 @default.
- W129222777 hasConceptScore W129222777C41008148 @default.
- W129222777 hasConceptScore W129222777C50644808 @default.
- W129222777 hasLocation W1292227771 @default.
- W129222777 hasOpenAccess W129222777 @default.
- W129222777 hasPrimaryLocation W1292227771 @default.
- W129222777 hasRelatedWork W1502219449 @default.
- W129222777 hasRelatedWork W1988705452 @default.
- W129222777 hasRelatedWork W2348427740 @default.
- W129222777 hasRelatedWork W2361294036 @default.
- W129222777 hasRelatedWork W2370221588 @default.
- W129222777 hasRelatedWork W2386387936 @default.
- W129222777 hasRelatedWork W2394008745 @default.
- W129222777 hasRelatedWork W2589794759 @default.
- W129222777 hasRelatedWork W2752082456 @default.
- W129222777 hasRelatedWork W4236579886 @default.
- W129222777 isParatext "false" @default.
- W129222777 isRetracted "false" @default.
- W129222777 magId "129222777" @default.
- W129222777 workType "book-chapter" @default.