An overview of 38 least squares–based frameworks for structural damage tomography

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An overview of 38 least squares–based frameworks for structural damage tomography. / Smyl, Danny; Bossuyt, Sven; Ahmad, Waqas; Vavilov, Anton; Liu, Dong.

In: Structural Health Monitoring, 15.04.2019.

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@article{98423b070c844f6cb7a10544d0c2ebdb,
title = "An overview of 38 least squares–based frameworks for structural damage tomography",
abstract = "The ability to reliably detect damage and intercept deleterious processes, such as cracking, corrosion, and plasticity are central themes in structural health monitoring. The importance of detecting such processes early on lies in the realization that delays may decrease safety, increase long-term repair/retrofit costs, and degrade the overall user experience of civil infrastructure. Since real structures exist in more than one dimension, the detection of distributed damage processes also generally requires input data from more than one dimension. Often, however, interpretation of distributed data—alone—offers insufficient information. For this reason, engineers and researchers have become interested in stationary inverse methods, for example, utilizing distributed data from stationary or quasi-stationary measurements for tomographic imaging structures. Presently, however, there are barriers in implementing stationary inverse methods at the scale of built civil structures. Of these barriers, a lack of available straightforward inverse algorithms is at the forefront. To address this, we provide 38 least-squares frameworks encompassing single-state, two-state, and joint tomographic imaging of structural damage. These regimes are then applied to two emerging structural health monitoring imaging modalities: Electrical Resistance Tomography and Quasi-Static Elasticity Imaging. The feasibility of the regimes are then demonstrated using simulated and experimental data.",
keywords = "Elasticity imaging, electrical imaging, inverse problems, structural health monitoring",
author = "Danny Smyl and Sven Bossuyt and Waqas Ahmad and Anton Vavilov and Dong Liu",
year = "2019",
month = "4",
day = "15",
doi = "10.1177/1475921719841012",
language = "English",
journal = "Structural Health Monitoring",
issn = "1475-9217",
publisher = "SAGE Publications Ltd",

}

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TY - JOUR

T1 - An overview of 38 least squares–based frameworks for structural damage tomography

AU - Smyl, Danny

AU - Bossuyt, Sven

AU - Ahmad, Waqas

AU - Vavilov, Anton

AU - Liu, Dong

PY - 2019/4/15

Y1 - 2019/4/15

N2 - The ability to reliably detect damage and intercept deleterious processes, such as cracking, corrosion, and plasticity are central themes in structural health monitoring. The importance of detecting such processes early on lies in the realization that delays may decrease safety, increase long-term repair/retrofit costs, and degrade the overall user experience of civil infrastructure. Since real structures exist in more than one dimension, the detection of distributed damage processes also generally requires input data from more than one dimension. Often, however, interpretation of distributed data—alone—offers insufficient information. For this reason, engineers and researchers have become interested in stationary inverse methods, for example, utilizing distributed data from stationary or quasi-stationary measurements for tomographic imaging structures. Presently, however, there are barriers in implementing stationary inverse methods at the scale of built civil structures. Of these barriers, a lack of available straightforward inverse algorithms is at the forefront. To address this, we provide 38 least-squares frameworks encompassing single-state, two-state, and joint tomographic imaging of structural damage. These regimes are then applied to two emerging structural health monitoring imaging modalities: Electrical Resistance Tomography and Quasi-Static Elasticity Imaging. The feasibility of the regimes are then demonstrated using simulated and experimental data.

AB - The ability to reliably detect damage and intercept deleterious processes, such as cracking, corrosion, and plasticity are central themes in structural health monitoring. The importance of detecting such processes early on lies in the realization that delays may decrease safety, increase long-term repair/retrofit costs, and degrade the overall user experience of civil infrastructure. Since real structures exist in more than one dimension, the detection of distributed damage processes also generally requires input data from more than one dimension. Often, however, interpretation of distributed data—alone—offers insufficient information. For this reason, engineers and researchers have become interested in stationary inverse methods, for example, utilizing distributed data from stationary or quasi-stationary measurements for tomographic imaging structures. Presently, however, there are barriers in implementing stationary inverse methods at the scale of built civil structures. Of these barriers, a lack of available straightforward inverse algorithms is at the forefront. To address this, we provide 38 least-squares frameworks encompassing single-state, two-state, and joint tomographic imaging of structural damage. These regimes are then applied to two emerging structural health monitoring imaging modalities: Electrical Resistance Tomography and Quasi-Static Elasticity Imaging. The feasibility of the regimes are then demonstrated using simulated and experimental data.

KW - Elasticity imaging

KW - electrical imaging

KW - inverse problems

KW - structural health monitoring

UR - http://www.scopus.com/inward/record.url?scp=85064674004&partnerID=8YFLogxK

U2 - 10.1177/1475921719841012

DO - 10.1177/1475921719841012

M3 - Article

JO - Structural Health Monitoring

JF - Structural Health Monitoring

SN - 1475-9217

ER -

ID: 33577253