A Comparison of Metaheuristic Algorithms for Solving the Piezoresistive Inverse Problem in Self-Sensing Materials

Hassan, Hashim; Tallman, Tyler N.

doi:10.1109/jsen.2020.3014554

Cited by 18 publications

(14 citation statements)

References 42 publications

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“…Two important factors differentiated this study-experimental validation of piezoresistive inversion and successful application to materials undergoing finite strains. Later works looked at using metaheuristic algorithms for solving the strains-from-conductivity inverse problem in a CNFmodified epoxy [275,282]. The CNF/epoxy was moulded in the shape of a plate with a hole and loaded in tension, causing strain concentrations in the vicinity of the hole.…”

Section: (C) Piezoresistive Inversionmentioning

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: (C) Piezoresistive Inversionmentioning

confidence: 99%

Structural engineering from an inverse problems perspective

et al. 2022

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“…Two important factors differentiated this study -experimental validation of piezoresistive inversion and successful application to materials undergoing finite strains. Later works looked at utilising metaheuristic algorithms for solving the strains-from-conductivity inverse problem in a CNF-modified epoxy [283,285]. The CNF/epoxy was moulded in the shape of a plate with a hole and loaded in tension, causing strain concentrations in the vicinity of the hole.…”

Section: Piezoresistive Inversionmentioning

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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“…As a few examples of strain sensing via EIT, Hassan and Tallman used EIT to spatially map in-situ strain concentrations of a carbon nanofiber (CNF)-modified epoxy plate loaded in tension. 9,10 Inverse methods were Further author information: (Send correspondence to TNT) TNT: E-mail: ttallman@purdue.edu.edu, Telephone: then used to determine the stresses and strains in the plate for failure prediction purposes. Additionally, Loh and colleagues produced piezoresistive meshes on which they monitored strains via EIT.…”

Section: Introductionmentioning

confidence: 99%

Mixed regularization for damage mapping in three-dimensional self-sensing composites via EIT

Homa

Sannamani

Thomas

et al. 2023

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023

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Electrical impedance tomography (EIT) is a non-invasive method of spatially mapping the electrical conductivity distribution of a domain based on a limited number of externally collected voltage-current measurements. This modality has been widely explored in the state of the art for damage detection, shaping, and localization in conductive composites (e.g. continuous carbon fiber composites and various nanofiller-modified continuous glass fiber composites) for purposes such as nondestructive evaluation (NDE), structural health monitoring (SHM), and embedded sensing. Mathematically, EIT is an ill-posed inverse problem that requires regularization to solve. To date, materials-focused practitioners of EIT have used relatively simple forms of regularization including, among others, Tikhonov regularization and the discrete Laplace operator (i.e. a smoothness prior). This is limiting because much more advanced types of regularization exist and have potential to significantly improve EIT for material state awareness. Therefore, in this work we propose and experimentally validate a novel mixedform regularization for the EIT inverse problem. In this approach, the discrete Laplace operator or smoothness prior is combined with a conditionally Gaussian prior (i.e. a focal prior). This mixed formulation has the benefit of simultaneously filtering out oscillatory background conductivity perturbations (via the smoothness prior) while still permitting outliers in the solution space (via the focal prior), which is expected to be the case for highly localized damage features in a background of otherwise zero change. The proposed mixed formulation was experimentally validated on two different three-dimensional composites: a carbon black (CB)-modified glass fiber/epoxy tube and a carbon fiber/epoxy laminate shaped as a representative NACA airfoil. Both composite specimens were subject to low-velocity impact damage via a drop-tower rig. It was found that the mixed smoothness + conditionally Gaussian regularization approach markedly outperforms the traditional smoothnessonly regularization, which allows for much clearer visualization of the damaged state of the material. This work demonstrates the importance of researching advanced regularization methods for materials imaging via EIT.

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A Comparison of Metaheuristic Algorithms for Solving the Piezoresistive Inverse Problem in Self-Sensing Materials

Cited by 18 publications

References 42 publications

Structural engineering from an inverse problems perspective

Structural engineering from an inverse problems perspective

Structural engineering from an inverse problems perspective

Mixed regularization for damage mapping in three-dimensional self-sensing composites via EIT

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