FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3198543989> ?p ?o ?g. }

• W3198543989 endingPage "110101" @default.
• W3198543989 startingPage "110101" @default.
• W3198543989 abstract "• A method is proposed for damage identification under nonstationary excitations. • UARDM is a new type of RP that represents dynamic characteristics of the structure. • Multi-label CNN model decouples the identification of damage locations and levels. • The proposed method performs with higher identification accuracy and efficiency. Civil engineering structures inevitably suffer from nonstationary ambient excitations in practice, which make conventional damage identification methods relying on the stationary assumption ineffective. This study presents a novel method based on unthresholded assembled recurrence distance matrix (UARDM) and multi-label convolutional neural network (CNN) for structural damage identification under nonstationary excitations. UARDM is a new type of recurrence plot (RP) that is proposed to integrate information of multiple channels and dispense with the artificially selected threshold. It reveals intrinsic dynamic characteristics of the structure using its vibration responses from the perspective of global probabilistic autocorrelation. After that, CNN is applied to automatically extract damage-sensitive features of UARDMs and classify them for the identification of damage cases. Instead of the traditional single-label CNN model that labels each combination of damage location and level as an objective class, the multi-label CNN model is developed to decouple the identification processes of damage locations and levels in order to improve the identification accuracy and computational efficiency. It evaluates the damage level at each location through a sub-branch with an independent set of labels and detects the damage locations by fusing information of all the sub-branches. A comprehensive comparison was conducted among single-label and multi-label CNN models input with raw accelerations, unthresholded multivariate recurrence plots (UMRPs), unthresholded recurrence plots (URPs) and UARDMs through numerical simulation and experimental test. It was demonstrated that the proposed structural damage identification method based on UARDM and multi-label CNN was able to identify multiple damage locations and levels under various stationary and nonstationary excitations with higher accuracy, efficiency and robustness, and even able to detect multiple-damage cases that were not measured beforehand and involved in the training dataset." @default.
• W3198543989 created "2021-09-13" @default.
• W3198543989 creator A5004145725 @default.
• W3198543989 creator A5032856218 @default.
• W3198543989 creator A5035416756 @default.
• W3198543989 creator A5070266422 @default.
• W3198543989 creator A5073472411 @default.
• W3198543989 creator A5080004899 @default.
• W3198543989 date "2021-12-01" @default.
• W3198543989 modified "2023-10-16" @default.
• W3198543989 title "Structural damage identification under nonstationary excitations through recurrence plot and multi-label convolutional neural network" @default.
• W3198543989 cites W151389796 @default.
• W3198543989 cites W1567302070 @default.
• W3198543989 cites W1968899503 @default.
• W3198543989 cites W1969315273 @default.
• W3198543989 cites W1971546637 @default.
• W3198543989 cites W1982594842 @default.
• W3198543989 cites W1986186574 @default.
• W3198543989 cites W2000602632 @default.
• W3198543989 cites W2006127591 @default.
• W3198543989 cites W2011302366 @default.
• W3198543989 cites W2014683958 @default.
• W3198543989 cites W2018672383 @default.
• W3198543989 cites W2021696390 @default.
• W3198543989 cites W2027266161 @default.
• W3198543989 cites W2031365860 @default.
• W3198543989 cites W2040704490 @default.
• W3198543989 cites W2050574141 @default.
• W3198543989 cites W2056942882 @default.
• W3198543989 cites W2067211778 @default.
• W3198543989 cites W2068591107 @default.
• W3198543989 cites W2069720696 @default.
• W3198543989 cites W2080927245 @default.
• W3198543989 cites W2081681829 @default.
• W3198543989 cites W2083361829 @default.
• W3198543989 cites W2087784802 @default.
• W3198543989 cites W2098312962 @default.
• W3198543989 cites W2099593264 @default.
• W3198543989 cites W2118804324 @default.
• W3198543989 cites W2149765849 @default.
• W3198543989 cites W2163332310 @default.
• W3198543989 cites W2166111845 @default.
• W3198543989 cites W2283608459 @default.
• W3198543989 cites W2404692435 @default.
• W3198543989 cites W2420632839 @default.
• W3198543989 cites W2483458289 @default.
• W3198543989 cites W2556345765 @default.
• W3198543989 cites W2598457882 @default.
• W3198543989 cites W2767522444 @default.
• W3198543989 cites W2769939476 @default.
• W3198543989 cites W2777200870 @default.
• W3198543989 cites W2791957585 @default.
• W3198543989 cites W2792629766 @default.
• W3198543989 cites W2808943413 @default.
• W3198543989 cites W2885520844 @default.
• W3198543989 cites W2889640721 @default.
• W3198543989 cites W2903155537 @default.
• W3198543989 cites W2919115771 @default.
• W3198543989 cites W2919946988 @default.
• W3198543989 cites W2930347983 @default.
• W3198543989 cites W2969752305 @default.
• W3198543989 cites W3011055781 @default.
• W3198543989 cites W3014638716 @default.
• W3198543989 cites W3026124411 @default.
• W3198543989 cites W3028845928 @default.
• W3198543989 cites W3033621544 @default.
• W3198543989 cites W3081857328 @default.
• W3198543989 cites W635199472 @default.
• W3198543989 doi "https://doi.org/10.1016/j.measurement.2021.110101" @default.
• W3198543989 hasPublicationYear "2021" @default.
• W3198543989 type Work @default.
• W3198543989 sameAs 3198543989 @default.
• W3198543989 citedByCount "13" @default.
• W3198543989 countsByYear W31985439892022 @default.
• W3198543989 countsByYear W31985439892023 @default.
• W3198543989 crossrefType "journal-article" @default.
• W3198543989 hasAuthorship W3198543989A5004145725 @default.
• W3198543989 hasAuthorship W3198543989A5032856218 @default.
• W3198543989 hasAuthorship W3198543989A5035416756 @default.
• W3198543989 hasAuthorship W3198543989A5070266422 @default.
• W3198543989 hasAuthorship W3198543989A5073472411 @default.
• W3198543989 hasAuthorship W3198543989A5080004899 @default.
• W3198543989 hasConcept C105795698 @default.
• W3198543989 hasConcept C11413529 @default.
• W3198543989 hasConcept C116834253 @default.
• W3198543989 hasConcept C153180895 @default.
• W3198543989 hasConcept C154945302 @default.
• W3198543989 hasConcept C33923547 @default.
• W3198543989 hasConcept C41008148 @default.
• W3198543989 hasConcept C49937458 @default.
• W3198543989 hasConcept C5297727 @default.
• W3198543989 hasConcept C59822182 @default.
• W3198543989 hasConcept C81363708 @default.
• W3198543989 hasConcept C86803240 @default.
• W3198543989 hasConceptScore W3198543989C105795698 @default.
• W3198543989 hasConceptScore W3198543989C11413529 @default.
• W3198543989 hasConceptScore W3198543989C116834253 @default.
• W3198543989 hasConceptScore W3198543989C153180895 @default.

• next