Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix

Hosseinzadeh, Ali Zare; Amiri, Gholamreza Ghodrati; Koo, Ki-Young

doi:10.1080/0305215x.2015.1017476

Cited by 25 publications

(9 citation statements)

References 39 publications

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“…Many examples are available in the literature illustrating the efficacy of static inversion methodologies for applications in a suite of SHM implementations. For example, the use of displacement measurements for capturing SHM causalities in various structural geometries was reported in [99][100][101]. Of note, the specific applications using displacement fields to reconstruct elastic and elasto-plastic properties (and corresponding damage characteristics) have been the source of significant research [102][103][104][105][106][107][108][109][110][111][112][113][114][115][116][117].…”

Section: (B) Static Inverse Problems In Shmmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

et al. 2022

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The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural responses given data including external forces, geometry, member sizes, restraint, etc.—characterizing a forward problem (structural causalities → structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realizations also hold true in structural health monitoring and a number of structural engineering sub-fields (response → structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.

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Section: (B) Static Inverse Problems In Shmmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

et al. 2022

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“…Many examples are available in the literature illustrating the efficacy of static inversion methodologies for applications in a suite of SHM implementations. For example, the use of displacement measurements for capturing SHM causalities in various structural geometries was reported in [98,99,100]. Of note, the specific applications using displacement fields to reconstruction elastic ans elasto-plastic properties (and corresponding damage characteristics) has been the source of significant research [101,102,103,104,105,106,107,108,109,110,111,112,113,114,115].…”

Section: Static Inverse Problems In Shmmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

Gallet,

Rigby,

Tallman

et al. 2021

Preprint

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The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural response given data including external forces, geometry, member sizes, restraint, etc. -characterising a forward problem (structural causalities → structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realisations also hold true in structural health monitoring and a number of structural engineering sub-fields (response → structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.

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“…The first part is the element analysis, i.e., the exploration of the mechanical properties of the element. It includes the selection of the trial functions of the element, the derivation of the element stiffness (Feng, 2018;Feng et al, 2018;Luo and Yang, 2021) that characterizes the stiffness or flexibility properties of the element, or the flexibility matrix (Doebling et al, 1998;Yang et al, 2013;Zare Hosseinzadeh et al, 2016;Katebi et al, 2018;Stutz et al, 2018;LI et al, 2020). The second part is the structural analysis, where the discrete elements are assembled into an overall full-structure computational model, which ultimately enables the matrix equations representing the full structural equilibrium (or coordination) to be obtained (Pindera, 1991;Mignolet et al, 2013;Luo et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Influence of the Order Exchange of the Node Connection in the Force Analysis of Steel Structures

et al. 2022

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In the mechanical analysis of steel structures, whether it is static analysis or dynamic analysis, it is necessary to establish the structural stiffness matrix first. In the process of building the structural stiffness matrix, the same element usually has different node code connection orders, and it has never been argued whether the different connection orders of the same element will have an effect on the building of the stiffness matrix. In this study, the influence of the difference in the node connection order on the construction of the element stiffness matrix is studied. First, the structural element stiffness matrix in the global coordinate system is established when the node connection order is different. It is found that the element stiffness matrix in the global coordinate system is indeed inconsistent for the same element with different connection orders. In this study, the elements of the established element stiffness matrix are extracted into the global stiffness matrix of the structural system based on the law of energy conservation; it is found that the global stiffness matrix finally established by using two different connection relationships is the same. The research results of the example show that in the stress analysis of steel structures, selecting different node connection sequences to establish the structural stiffness matrix will obtain the element stiffness matrix under different global coordinate systems. However, through the aggregation process of the global stiffness matrix of the structural system, the global stiffness matrix obtained is consistent, so the different connection sequences of nodes will not affect the stress analysis of steel structures. The example further analyzes the static stress and dynamic responses of the steel structure. The conclusions of this study provide a reliable theoretical basis for the situation that the order of node connections need not be consistent in the finite element modeling of steel structures and are of reference value for the finite element modeling of steel structures.

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Optimization-based method for structural damage localization and quantification by means of static displacements computed by flexibility matrix

Cited by 25 publications

References 39 publications

Structural engineering from an inverse problems perspective

Structural engineering from an inverse problems perspective

Structural engineering from an inverse problems perspective

Influence of the Order Exchange of the Node Connection in the Force Analysis of Steel Structures

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