Sensor placement optimization and damage identification in a fuselage structure using inverse modal problem and firefly algorithm

Gomes, Guilherme Ferreira; Pereira, João Vitor Purcino

doi:10.1007/s12065-020-00372-1

Cited by 32 publications

(21 citation statements)

References 59 publications

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“…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]. In the pervasive case where displacement/strain measurements are discretely measured from strain gauges/fibre-optics, inverse methodologies have also been fruitfully employed for damage characterization, pressure and strain mapping, and shape sensing [118][119][120][121][122][123][124][125][126][127][128][129]. Perhaps one measure illustrating the success of such inverse approaches is highlighted by the recent interest in optimizing the related sensing schemes [118,[130][131][132].…”

Section: (B) Static Inverse Problems In Shmmentioning

confidence: 99%

“…In the pervasive case where displacement/strain measurements are discretely measured from strain gauges/fibre-optics, inverse methodologies have also been fruitfully employed for damage characterization, pressure and strain mapping, and shape sensing [118][119][120][121][122][123][124][125][126][127][128][129]. Perhaps one measure illustrating the success of such inverse approaches is highlighted by the recent interest in optimizing the related sensing schemes [118,[130][131][132].…”

Section: (B) Static Inverse Problems In Shmmentioning

confidence: 99%

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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%

Section: (B) Static Inverse Problems In Shmmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

et al. 2022

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“…Most methods that use signal processing are based on the relationship between the structural condition and the symptom given by the collected signal. This relationship is not simple, and the complexity found to analyze the most diverse mechanical structures in use today requires the use of advanced systems (Stepinski et al, 2013;Gomes and Pereira, 2020). According to Rytter (1993), the inspection of a structure in relation to a damage can be divided into five levels: identification, location, evaluation, structural life prediction and intelligent prognosis of the damage.…”

Section: Structural Health Monitoringmentioning

confidence: 99%

Lichtenberg optimization algorithm applied to crack tip identification in thin plate-like structures

Pereira

Chuman

Cunha

et al. 2020

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Purpose This study aims to develop a numerical identification and characterization of crack propagation through the use of a new optimization metaheuristics called Lichtenberg optimization. Design/methodology/approach The damage-identification problem is treated as an inverse problem, which combines finite element methods with intelligent computational methods to obtain the best possible response. To optimize the objectives, the Lichtenberg algorithm is applied, which includes concepts of random cluster growth in nature. Findings The simulations show that it is possible to determine the Lichtenberg spectrum algorithm a part of the structure to be removed and replaced in this case to stop the propagation. Originality/value The results show a very good crack identification in plates-like structures using the Lichtenberg algorithm (LA) based only in strain fields. Although many studies have reported on damage-identification-based optimization methods, very few have focused on the crack tip modeling and LA as the main solver.

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“…Zhou et al [12,19] have used the firefly algorithm for the optimal arrangement of sensors and improved the coding method of the algorithm to obtain the solution of the firefly algorithm to solve the discrete optimization problem. Gomes et al [20] have used the firefly algorithm to solve the inverse problem to identify structural damage (location and severity). Yi et al [21,22] have proposed several improved monkey algorithms (the immune and adaptive monkey algorithms); these algorithms solve optimal sensor placement optimization problems by using a dual-structure coding method instead of the traditional coding method.…”

Section: Introductionmentioning

confidence: 99%

Optimal sensor placement of bridge structure based on sensitivity‐effective independence method

Yang

et al. 2021

IET Circuits, Devices & Syst

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Taking the optimal sensor placement problem in bridge structural health monitoring as the study object and relying on the engineering example of a simply supported steel truss bridge, the improved optimal sensor placement method based on sensitivity-effective independence method was proposed. Using the sensitivity coefficient reflecting structural damage, the proposed method could modify the effective independence method reflecting the maximum linear independence. The proposed method further optimizes the sensor placement method. A method for selecting the number of models based on modal closeness was proposed and makes the selection of the number of modes more objective. The example analysis shows that evaluations using this method are effective for multiple evaluation criteria. The method ensures observability of the mode vector and identifiability of structural damage. It is an effective optimal sensor placement algorithm for the bridge structure.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Sensor placement optimization and damage identification in a fuselage structure using inverse modal problem and firefly algorithm

Cited by 32 publications

References 59 publications

Structural engineering from an inverse problems perspective

Structural engineering from an inverse problems perspective

Lichtenberg optimization algorithm applied to crack tip identification in thin plate-like structures

Optimal sensor placement of bridge structure based on sensitivity‐effective independence method

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