FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4290803925> ?p ?o ?g. }

• W4290803925 abstract "Despite continuous evolution and development of structural health monitoring (SHM) technology, interpreting a huge amount of sensed data from a sophisticated SHM system to extract useful information about structural health condition remains a challenge. Aiming to resolve this problem, a novel application of probabilistic data-driven damage detection method was proposed in the context of Sparse Bayesian Learning (SBL) scheme. The framework involves constructing a new structural damage index and establishing SBL regression model as reference base only using the data acquired in health state. The construction of the structural damage index is based on damage-sensitive frequency band, which is determined by NExT using vibration monitoring data. The structure will be classified to be damaged as the structural damage index based on new data deviates from the index predicted by SBL regression reference model, and further, the Bayes factor is adopted to quantify the damage degree. In addition, the relationship between the Bayes factors and the resonance frequency change rate is investigated in detail. The proposed methodology features the following merits: (i) It is probabilistic data-driven method exempting from physical model of the structure, excitation/loading information, and (ii) it belongs to the unsupervised model in need for structural damage detection, which can be formulated using only monitoring data from health state in the absence of monitoring data from damaged state. Damage detection and discrimination capabilities of the proposed methodology are verified using field monitoring data acquired from a cable-stayed bridge. Finally, a discussion of the SBL-based approach is made and further challenges pertaining to damage detection processes in the context of SHM are identified." @default.
• W4290803925 created "2022-08-12" @default.
• W4290803925 creator A5005320022 @default.
• W4290803925 creator A5011189705 @default.
• W4290803925 creator A5015956299 @default.
• W4290803925 creator A5026651321 @default.
• W4290803925 creator A5037736384 @default.
• W4290803925 creator A5052802818 @default.
• W4290803925 creator A5071686066 @default.
• W4290803925 date "2022-08-11" @default.
• W4290803925 modified "2023-10-01" @default.
• W4290803925 title "Towards probabilistic data‐driven damage detection in SHM using sparse Bayesian learning scheme" @default.
• W4290803925 cites W1925971948 @default.
• W4290803925 cites W1966887825 @default.
• W4290803925 cites W1975902562 @default.
• W4290803925 cites W1977196868 @default.
• W4290803925 cites W1983819088 @default.
• W4290803925 cites W2023965427 @default.
• W4290803925 cites W2045831595 @default.
• W4290803925 cites W2062258832 @default.
• W4290803925 cites W2063443463 @default.
• W4290803925 cites W2130006111 @default.
• W4290803925 cites W2132112746 @default.
• W4290803925 cites W2148154358 @default.
• W4290803925 cites W2477294980 @default.
• W4290803925 cites W2539200877 @default.
• W4290803925 cites W2547960182 @default.
• W4290803925 cites W2577761826 @default.
• W4290803925 cites W2582468660 @default.
• W4290803925 cites W2765700762 @default.
• W4290803925 cites W2789736956 @default.
• W4290803925 cites W2888180323 @default.
• W4290803925 cites W2896365917 @default.
• W4290803925 cites W2915864118 @default.
• W4290803925 cites W2967551316 @default.
• W4290803925 cites W2990008229 @default.
• W4290803925 cites W3012453308 @default.
• W4290803925 cites W3030391880 @default.
• W4290803925 cites W3033856829 @default.
• W4290803925 cites W3039546557 @default.
• W4290803925 cites W3045559501 @default.
• W4290803925 cites W3095719049 @default.
• W4290803925 cites W3122976881 @default.
• W4290803925 cites W3124399172 @default.
• W4290803925 cites W3159305903 @default.
• W4290803925 cites W3199513339 @default.
• W4290803925 cites W4231406165 @default.
• W4290803925 cites W77275778 @default.
• W4290803925 doi "https://doi.org/10.1002/stc.3070" @default.
• W4290803925 hasPublicationYear "2022" @default.
• W4290803925 type Work @default.
• W4290803925 citedByCount "9" @default.
• W4290803925 countsByYear W42908039252023 @default.
• W4290803925 crossrefType "journal-article" @default.
• W4290803925 hasAuthorship W4290803925A5005320022 @default.
• W4290803925 hasAuthorship W4290803925A5011189705 @default.
• W4290803925 hasAuthorship W4290803925A5015956299 @default.
• W4290803925 hasAuthorship W4290803925A5026651321 @default.
• W4290803925 hasAuthorship W4290803925A5037736384 @default.
• W4290803925 hasAuthorship W4290803925A5052802818 @default.
• W4290803925 hasAuthorship W4290803925A5071686066 @default.
• W4290803925 hasConcept C100776233 @default.
• W4290803925 hasConcept C107673813 @default.
• W4290803925 hasConcept C119857082 @default.
• W4290803925 hasConcept C12267149 @default.
• W4290803925 hasConcept C124101348 @default.
• W4290803925 hasConcept C126322002 @default.
• W4290803925 hasConcept C127413603 @default.
• W4290803925 hasConcept C151730666 @default.
• W4290803925 hasConcept C154945302 @default.
• W4290803925 hasConcept C160234255 @default.
• W4290803925 hasConcept C207201462 @default.
• W4290803925 hasConcept C2776247918 @default.
• W4290803925 hasConcept C2779343474 @default.
• W4290803925 hasConcept C33724603 @default.
• W4290803925 hasConcept C37903108 @default.
• W4290803925 hasConcept C41008148 @default.
• W4290803925 hasConcept C49937458 @default.
• W4290803925 hasConcept C52001869 @default.
• W4290803925 hasConcept C66938386 @default.
• W4290803925 hasConcept C71924100 @default.
• W4290803925 hasConcept C86803240 @default.
• W4290803925 hasConceptScore W4290803925C100776233 @default.
• W4290803925 hasConceptScore W4290803925C107673813 @default.
• W4290803925 hasConceptScore W4290803925C119857082 @default.
• W4290803925 hasConceptScore W4290803925C12267149 @default.
• W4290803925 hasConceptScore W4290803925C124101348 @default.
• W4290803925 hasConceptScore W4290803925C126322002 @default.
• W4290803925 hasConceptScore W4290803925C127413603 @default.
• W4290803925 hasConceptScore W4290803925C151730666 @default.
• W4290803925 hasConceptScore W4290803925C154945302 @default.
• W4290803925 hasConceptScore W4290803925C160234255 @default.
• W4290803925 hasConceptScore W4290803925C207201462 @default.
• W4290803925 hasConceptScore W4290803925C2776247918 @default.
• W4290803925 hasConceptScore W4290803925C2779343474 @default.
• W4290803925 hasConceptScore W4290803925C33724603 @default.
• W4290803925 hasConceptScore W4290803925C37903108 @default.
• W4290803925 hasConceptScore W4290803925C41008148 @default.
• W4290803925 hasConceptScore W4290803925C49937458 @default.
• W4290803925 hasConceptScore W4290803925C52001869 @default.

• next