Guided wave tomography based on least-squares reverse-time migration

He, Jiaze; Rocha, Daniel Guimarães Pedro; Sava, Paul

doi:10.1177/1475921719880296

Cited by 26 publications

(13 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Owing to a number of numerical challenges, conventional solutions to related inverse-guided wave problems are generally not feasible [183]. Consequentially, alternative methodologies have been proposed, including, for example, inverse filtering [189], reverse time migration [190,191] and Bayesian/probabilistic [192,193], among emerging inversion approaches.…”

Section: (C) Dynamical Inverse Problems In Shmmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: (C) Dynamical Inverse Problems In Shmmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Owing to a number of numerical challenges, conventional solutions to related inverse guided waves problems are generally not feasible [177]. Consequentially, alternative methodologies have been proposed including, for example, inverse filtering [183], reverse time migration [184,185], Bayesian/probabilistic [186,187], amongst emerging inversion approaches.…”

Section: Dynamical Inverse Problems In Shmmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

Gallet,

Rigby,

Tallman

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…4–7 Interrogating large areas with a small number of sensors is the common system configuration using UGW, and then damage location and severity, based on the propagation distances 8 and guided wave (GW) amplitude, 9 can be obtained from the received signals. Numerous UGW-based damage localization methods have been developed in recent years, such as the triangulation method, 10 focusing array algorithm, 11 GW phased array, 12 migration technique, 13–17 and time-reversal imaging method. 18 All of these methods ultimately estimate the damage locations based on the propagation distance.…”

Section: Introductionmentioning

confidence: 99%

Sparse Bayesian learning approach for propagation distance recognition and damage localization in plate-like structures using guided waves

Zhao

Zhou

Huang

et al. 2020

Structural Health Monitoring

View full text Add to dashboard Cite

In ultrasonic guided wave–based damage detection, the propagation distance recognition of wave packets is an essential step. However, it is difficult to perform direct distance extraction from guided wave signals since the multimode, mode conversion, and dispersion effects typically lead to wave packet overlapping and distortion. In addition, the identified damage location may be incorrect due to inevitable uncertainties in the procedure of propagation distance recognition and damage localization. Motivated by these difficulties, a novel two-stage approach for propagation distance recognition and damage localization is proposed based on sparse Bayesian learning framework. In the first stage, prior knowledge of a small number of wave packets contained in a signal is exploited to sparsely represent the guided wave signal and then the corresponding propagation distance and amplitude information of each wave packet can be obtained. In the next stage, only a small number of damages occurring in a structure are exploited and a vector consisting of the propagation distances extracted from the previous stage is used to match the atoms in a pre-defined over-complete distance dictionary matrix, to achieve our goal of localizing structural damage. Both procedures of the two stages are realized by the sparse Bayesian learning algorithm, which obtains the most probable value and the corresponding uncertainty. A sampling strategy is presented to transfer the uncertainty of the propagation distance recognition to the subsequent damage localization. Finally, the effectiveness of the proposed method is validated using numerical simulation and experimental investigation on aluminum plates. The proposed method is only valid for single damage localization in the present form, but it has the potential to be extended for multiple damage localization.

show abstract

Guided wave tomography based on least-squares reverse-time migration

Cited by 26 publications

References 81 publications

Structural engineering from an inverse problems perspective

Structural engineering from an inverse problems perspective

Structural engineering from an inverse problems perspective

Sparse Bayesian learning approach for propagation distance recognition and damage localization in plate-like structures using guided waves

Contact Info

Product

Resources

About