Matches in SemOpenAlex for { <https://semopenalex.org/work/W3034166189> ?p ?o ?g. }
- W3034166189 abstract "Identifying damage of structural systems is typically characterized as an inverse problem which might be ill-conditioned due to aleatory and epistemic uncertainties induced by measurement noise and modeling error. Sparse representation can be used to perform inverse analysis for the case of sparse damage. In this paper, we propose a novel two-stage sensitivity analysis-based framework for both model updating and sparse damage identification. Specifically, an $ell_2$ Bayesian learning method is firstly developed for updating the intact model and uncertainty quantification so as to set forward a baseline for damage detection. A sparse representation pipeline built on a quasi-$ell_0$ method, e.g., Sequential Threshold Least Squares (STLS) regression, is then presented for damage localization and quantification. Additionally, Bayesian optimization together with cross validation is developed to heuristically learn hyperparameters from data, which saves the computational cost of hyperparameter tuning and produces more reliable identification result. The proposed framework is verified by three examples, including a 10-story shear-type building, a complex truss structure, and a shake table test of an eight-story steel frame. Results show that the proposed approach is capable of both localizing and quantifying structural damage with high accuracy." @default.
- W3034166189 created "2020-06-12" @default.
- W3034166189 creator A5021555728 @default.
- W3034166189 creator A5089604961 @default.
- W3034166189 date "2020-06-06" @default.
- W3034166189 modified "2023-10-16" @default.
- W3034166189 title "Sparse representation for damage identification of structural systems" @default.
- W3034166189 cites W1131737499 @default.
- W3034166189 cites W1502922572 @default.
- W3034166189 cites W1502949418 @default.
- W3034166189 cites W1573264798 @default.
- W3034166189 cites W1663973292 @default.
- W3034166189 cites W1969076160 @default.
- W3034166189 cites W1973263387 @default.
- W3034166189 cites W1990568935 @default.
- W3034166189 cites W2019158656 @default.
- W3034166189 cites W2023965427 @default.
- W3034166189 cites W2024292495 @default.
- W3034166189 cites W2027623535 @default.
- W3034166189 cites W2044990306 @default.
- W3034166189 cites W2053347931 @default.
- W3034166189 cites W2053834050 @default.
- W3034166189 cites W2056871913 @default.
- W3034166189 cites W2064633509 @default.
- W3034166189 cites W2070007446 @default.
- W3034166189 cites W2114057255 @default.
- W3034166189 cites W2116326094 @default.
- W3034166189 cites W2129131372 @default.
- W3034166189 cites W2131241448 @default.
- W3034166189 cites W2135046866 @default.
- W3034166189 cites W2140064412 @default.
- W3034166189 cites W2166670624 @default.
- W3034166189 cites W2168090960 @default.
- W3034166189 cites W2206709312 @default.
- W3034166189 cites W2221175250 @default.
- W3034166189 cites W2292587126 @default.
- W3034166189 cites W2470344286 @default.
- W3034166189 cites W2498064653 @default.
- W3034166189 cites W2516809051 @default.
- W3034166189 cites W2525748878 @default.
- W3034166189 cites W2547960182 @default.
- W3034166189 cites W2556429591 @default.
- W3034166189 cites W2577761826 @default.
- W3034166189 cites W2747754661 @default.
- W3034166189 cites W2901306041 @default.
- W3034166189 cites W291670947 @default.
- W3034166189 cites W2950656175 @default.
- W3034166189 cites W2963113802 @default.
- W3034166189 cites W3006359685 @default.
- W3034166189 cites W3033983511 @default.
- W3034166189 cites W3167551172 @default.
- W3034166189 cites W60278794 @default.
- W3034166189 cites W619928621 @default.
- W3034166189 doi "https://doi.org/10.48550/arxiv.2006.03929" @default.
- W3034166189 hasPublicationYear "2020" @default.
- W3034166189 type Work @default.
- W3034166189 sameAs 3034166189 @default.
- W3034166189 citedByCount "0" @default.
- W3034166189 crossrefType "posted-content" @default.
- W3034166189 hasAuthorship W3034166189A5021555728 @default.
- W3034166189 hasAuthorship W3034166189A5089604961 @default.
- W3034166189 hasBestOaLocation W30341661891 @default.
- W3034166189 hasConcept C107673813 @default.
- W3034166189 hasConcept C11413529 @default.
- W3034166189 hasConcept C116834253 @default.
- W3034166189 hasConcept C119857082 @default.
- W3034166189 hasConcept C124066611 @default.
- W3034166189 hasConcept C126042441 @default.
- W3034166189 hasConcept C127413603 @default.
- W3034166189 hasConcept C134306372 @default.
- W3034166189 hasConcept C135252773 @default.
- W3034166189 hasConcept C153180895 @default.
- W3034166189 hasConcept C154945302 @default.
- W3034166189 hasConcept C160234255 @default.
- W3034166189 hasConcept C17744445 @default.
- W3034166189 hasConcept C199539241 @default.
- W3034166189 hasConcept C21200559 @default.
- W3034166189 hasConcept C24326235 @default.
- W3034166189 hasConcept C2776359362 @default.
- W3034166189 hasConcept C32230216 @default.
- W3034166189 hasConcept C33923547 @default.
- W3034166189 hasConcept C41008148 @default.
- W3034166189 hasConcept C59822182 @default.
- W3034166189 hasConcept C76155785 @default.
- W3034166189 hasConcept C8642999 @default.
- W3034166189 hasConcept C86803240 @default.
- W3034166189 hasConcept C94625758 @default.
- W3034166189 hasConceptScore W3034166189C107673813 @default.
- W3034166189 hasConceptScore W3034166189C11413529 @default.
- W3034166189 hasConceptScore W3034166189C116834253 @default.
- W3034166189 hasConceptScore W3034166189C119857082 @default.
- W3034166189 hasConceptScore W3034166189C124066611 @default.
- W3034166189 hasConceptScore W3034166189C126042441 @default.
- W3034166189 hasConceptScore W3034166189C127413603 @default.
- W3034166189 hasConceptScore W3034166189C134306372 @default.
- W3034166189 hasConceptScore W3034166189C135252773 @default.
- W3034166189 hasConceptScore W3034166189C153180895 @default.
- W3034166189 hasConceptScore W3034166189C154945302 @default.
- W3034166189 hasConceptScore W3034166189C160234255 @default.
- W3034166189 hasConceptScore W3034166189C17744445 @default.