Matches in SemOpenAlex for { <https://semopenalex.org/work/W622365706> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W622365706 endingPage "1156" @default.
- W622365706 startingPage "1149" @default.
- W622365706 abstract "A neural-network-based damage detection approach with the direct use of actual incomplete time series of earthquake response is developed for a suspension bridge. Two neural networks are constructed and trained using the segment of the timeseries of seismic responses on several locations of the bridge, when earthquakeis in small level, to identify the transversal and vertical velocities responsesat the deck in the middle of the main span of the suspension bridge. The two neural networks are assumed as nonp rametric models for the bridge in health condition before the earthquake occurred. The performance of the trained emulator neural network models for the suspension bridge is evaluated by numerical simulation. The RRMS error between the forecast responses and the measurements in different stages are decided. Results show that the RRMS errors corresponding to the transversal and vertical velocities in the middle of the m in span have a variance in different segments. This results mean that occurrence of damages in some structural members is possible. This analysis result is testified by inspection result that broken stay cables have been found after the earthquake. The proposed approach is a non-parametric damage detection strategy, in which a prior information about the exact model of the suspension bridge is not needed. The proposed strategy has a significant advantage when dealing with large-scale structures inreal-word." @default.
- W622365706 created "2016-06-24" @default.
- W622365706 creator A5044158013 @default.
- W622365706 creator A5050335907 @default.
- W622365706 creator A5064530705 @default.
- W622365706 date "2003-01-01" @default.
- W622365706 modified "2023-10-16" @default.
- W622365706 title "A Post-Seismic Damage Detection Strategy in Time Domain for a Suspension Bridge with Neural Networks" @default.
- W622365706 cites W1979131162 @default.
- W622365706 cites W2007250360 @default.
- W622365706 cites W2045831595 @default.
- W622365706 cites W2047759278 @default.
- W622365706 cites W2140171944 @default.
- W622365706 cites W2320596008 @default.
- W622365706 cites W3148895607 @default.
- W622365706 cites W642395047 @default.
- W622365706 doi "https://doi.org/10.2208/journalam.6.1149" @default.
- W622365706 hasPublicationYear "2003" @default.
- W622365706 type Work @default.
- W622365706 sameAs 622365706 @default.
- W622365706 citedByCount "1" @default.
- W622365706 countsByYear W6223657062022 @default.
- W622365706 crossrefType "journal-article" @default.
- W622365706 hasAuthorship W622365706A5044158013 @default.
- W622365706 hasAuthorship W622365706A5050335907 @default.
- W622365706 hasAuthorship W622365706A5064530705 @default.
- W622365706 hasBestOaLocation W6223657061 @default.
- W622365706 hasConcept C100776233 @default.
- W622365706 hasConcept C103824480 @default.
- W622365706 hasConcept C105341887 @default.
- W622365706 hasConcept C105795698 @default.
- W622365706 hasConcept C117251300 @default.
- W622365706 hasConcept C119857082 @default.
- W622365706 hasConcept C126322002 @default.
- W622365706 hasConcept C127413603 @default.
- W622365706 hasConcept C151406439 @default.
- W622365706 hasConcept C154945302 @default.
- W622365706 hasConcept C202444582 @default.
- W622365706 hasConcept C2776247918 @default.
- W622365706 hasConcept C2778753569 @default.
- W622365706 hasConcept C2778966251 @default.
- W622365706 hasConcept C31972630 @default.
- W622365706 hasConcept C33923547 @default.
- W622365706 hasConcept C41008148 @default.
- W622365706 hasConcept C50644808 @default.
- W622365706 hasConcept C5961521 @default.
- W622365706 hasConcept C66938386 @default.
- W622365706 hasConcept C71924100 @default.
- W622365706 hasConceptScore W622365706C100776233 @default.
- W622365706 hasConceptScore W622365706C103824480 @default.
- W622365706 hasConceptScore W622365706C105341887 @default.
- W622365706 hasConceptScore W622365706C105795698 @default.
- W622365706 hasConceptScore W622365706C117251300 @default.
- W622365706 hasConceptScore W622365706C119857082 @default.
- W622365706 hasConceptScore W622365706C126322002 @default.
- W622365706 hasConceptScore W622365706C127413603 @default.
- W622365706 hasConceptScore W622365706C151406439 @default.
- W622365706 hasConceptScore W622365706C154945302 @default.
- W622365706 hasConceptScore W622365706C202444582 @default.
- W622365706 hasConceptScore W622365706C2776247918 @default.
- W622365706 hasConceptScore W622365706C2778753569 @default.
- W622365706 hasConceptScore W622365706C2778966251 @default.
- W622365706 hasConceptScore W622365706C31972630 @default.
- W622365706 hasConceptScore W622365706C33923547 @default.
- W622365706 hasConceptScore W622365706C41008148 @default.
- W622365706 hasConceptScore W622365706C50644808 @default.
- W622365706 hasConceptScore W622365706C5961521 @default.
- W622365706 hasConceptScore W622365706C66938386 @default.
- W622365706 hasConceptScore W622365706C71924100 @default.
- W622365706 hasLocation W6223657061 @default.
- W622365706 hasOpenAccess W622365706 @default.
- W622365706 hasPrimaryLocation W6223657061 @default.
- W622365706 hasRelatedWork W2022967116 @default.
- W622365706 hasRelatedWork W2027378453 @default.
- W622365706 hasRelatedWork W2353516228 @default.
- W622365706 hasRelatedWork W2353639925 @default.
- W622365706 hasRelatedWork W2367043156 @default.
- W622365706 hasRelatedWork W2383132239 @default.
- W622365706 hasRelatedWork W2384248468 @default.
- W622365706 hasRelatedWork W2390032252 @default.
- W622365706 hasRelatedWork W2393678086 @default.
- W622365706 hasRelatedWork W575219528 @default.
- W622365706 hasVolume "6" @default.
- W622365706 isParatext "false" @default.
- W622365706 isRetracted "false" @default.
- W622365706 magId "622365706" @default.
- W622365706 workType "article" @default.