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Smart Sensing in Structural Health Monitoring of Advanced Composite Structures

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 3796

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Special Issue Editors

Dr. Adnan Kefal

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Guest Editor

Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, Istanbul 34956, Turkey
Interests: computational mechanics; composite and sandwich structures; plate theories; structural health monitoring; shape sensing; fiber optic sensing; peridynamics; topology optimization; additive manufacturing

Dr. Claudio Sbarufatti

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Guest Editor

Politecnico di Milano, Department of Mechanical Engineering, via La Masa 1, 20156, Milan, Italy
Interests: digital-twin; development and application of statistical and numerical methods for the solution of inverse problems in the context of structural anomaly and structural load identification; inverse FEM; physics-informed machine learning; Bayesian inference and Monte-Carlo methods for model updating; damage prognosis of composite structures; sensor network optimization; active and passive impact monitoring; implementation of multifunctional composite materials with self-sensing and self-heating functions
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Special Issue Information

Dear Colleagues,

Today composite structural components are often used in many engineering vehicles ranging from aerospace to automobile structures. With the rapid technological advancements in the field of sensors and smart sensing, structural health monitoring (SHM) systems become ever so important to better understand the real-time mechanical behavior of engineering components made of composite materials. In this context, the development of new sensor-data-based algorithms for in-service monitoring/assessment has been the subject of intellectual pursuit by both the industrial and academic communities for the last decade. Specifically, among the recent technological advances, shape sensing (deformation reconstruction from sensor network) and digital models are put into practice to enhance integrated real-time monitoring systems as well as the performance and safety of the whole.

This Special Issue will report recent advances in key topics of emerging computational techniques for shape sensing and SHM of composite materials/structures. It will unite diverse research outcomes in numerical simulations and experimental implementations of new smart sensing methods for in situ full-field monitoring of deformations, strains, and stresses encountered within composites under operational conditions. Moreover, it will include damage diagnosis and prognosis methods based on the outcomes of the full-field data processing for crack detection/propagation, fatigue performance evaluation, modal model reconstruction, etc. Applications of these state-of-the-art methods can include smart sensing and in situ damage detection of laminated composites, sandwich structures, and hybrid composites using fiber optic sensors, resistive sensors, piezoelectric sensors, etc.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

- Numerical models for smart sensing and SHM of composites;

- Real-time shape and stress sensing of sandwich structures;

- Experimental investigation of deformation reconstruction algorithms;

- Theoretical/experimental developments in inverse finite element method (iFEM);

- Damage diagnosis and prognosis methods based on full-field data;

- Crack/damage/delamination localization in composites;

- Vibration/modal model reconstruction of composites based on discrete strains;

- New sensor placement models using optimization techniques;

- Structural digital twin for smart sensing of deformation/stress/damage.

We invite eager researchers and renowned experts to submit original research/review articles that describe modern computer methods, numerical algorithms, and experimental findings for smart sensing of advanced composites and further stimulate innovations in the relevant areas.

Dr. Adnan Kefal
Dr. Claudio Sbarufatti
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

shape sensing
inverse methods
structural health monitoring
structural digital twin
sensor technologies
composite materials
sandwich structures

Published Papers (4 papers)

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Research

23 pages, 10457 KiB

Open AccessArticle

A Critical Comparison of Shape Sensing Algorithms: The Calibration Matrix Method versus iFEM

by Cornelis de Mooij and Marcias Martinez

Sensors 2024, 24(11), 3562; https://doi.org/10.3390/s24113562 - 31 May 2024

Viewed by 286

Abstract

Two shape-sensing algorithms, the calibration matrix (CM) method and the inverse Finite Element Method (iFEM), were compared on their ability to accurately reconstruct displacements, strains, and loads and on their computational efficiency. CM reconstructs deformation through a linear combination of known load cases using the sensor data measured for each of these known load cases and the sensor data measured for the actual load case. iFEM reconstructs deformation by minimizing a least-squares error functional based on the difference between the measured and numerical values for displacement and/or strain. In this study, CM is covered in detail to determine the applicability and practicality of the method. The CM results for several benchmark problems from the literature were compared to the iFEM results. In addition, a representative aerospace structure consisting of a twisted and tapered blade with a NACA 6412 cross-sectional profile was evaluated using quadratic hexahedral solid elements with reduced integration. Both methods assumed linear elastic material conditions and used discrete displacement sensors, strain sensors, or a combination of both to reconstruct the full displacement and strain fields. In our study, surface-mounted and distributed sensors throughout the volume of the structure were considered. This comparative study was performed to support the growing demand for load monitoring, specifically for applications where the sensor data is obtained from discrete and irregularly distributed points on the structure. In this study, the CM method was shown to achieve greater accuracy than iFEM. Averaged over all the load cases examined, the CM algorithm achieved average displacement and strain errors of less than 0.01%, whereas the iFEM algorithm had an average displacement error of 21% and an average strain error of 99%. In addition, CM also achieved equal or better computational efficiency than iFEM after initial set-up, with similar first solution times and faster repeat solution times by a factor of approximately 100, for hundreds to thousands of sensors. Full article

(This article belongs to the Special Issue Smart Sensing in Structural Health Monitoring of Advanced Composite Structures)

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17 pages, 9082 KiB

Open AccessArticle

A Two-Dimensional Eight-Node Quadrilateral Inverse Element for Shape Sensing and Structural Health Monitoring

by Mingyang Li, Erkan Oterkus and Selda Oterkus

Sensors 2023, 23(24), 9809; https://doi.org/10.3390/s23249809 - 14 Dec 2023

Cited by 1 | Viewed by 816

Abstract

The inverse finite element method (iFEM) is a powerful tool for shape sensing and structural health monitoring and has several advantages with respect to some other existing approaches. In this study, a two-dimensional eight-node quadrilateral inverse finite element formulation is presented. The element is suitable for thin structures under in-plane loading conditions. To validate the accuracy and demonstrate the capability of the inverse element, four different numerical cases are considered for different loading and boundary conditions. iFEM analysis results are compared with regular finite element analysis results as the reference solution and very good agreement is observed between the two solutions, demonstrating the capability of the iFEM approach. Full article

(This article belongs to the Special Issue Smart Sensing in Structural Health Monitoring of Advanced Composite Structures)

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23 pages, 7486 KiB

Open AccessArticle

Delamination Detection and Localization in Vibrating Composite Plates and Shells Using the Inverse Finite Element Method

by Faraz Ganjdoust, Adnan Kefal and Alexander Tessler

Sensors 2023, 23(18), 7926; https://doi.org/10.3390/s23187926 - 15 Sep 2023

Cited by 1 | Viewed by 928

Abstract

Delamination damage is one of the most critical damage modes of composite materials. It takes place through the thickness of the laminated composites and does not show subtle surface effects. In the present study, a delamination detection approach based on equivalent von Mises strains is demonstrated for vibrating laminated (i.e., unidirectional fabric) composite plates. In this context, the governing relations of the inverse finite element method were recast according to the refined zigzag theory. Using the in situ strain measurements obtained from the surface and through the thickness of the composite shell, the inverse analysis was performed, and the strain field of the composite shell was reconstructed. The implementation of the proposed methodology is demonstrated for two numerical case studies associated with the harmonic and random vibrations of composite shells. The findings of this study show that the present damage detection method is capable of real-time monitoring of damage and providing information about the exact location, shape, and extent of the delamination damage in the vibrating composite plate. Finally, the robustness of the proposed method in response to resonance and extreme load variations is shown. Full article

(This article belongs to the Special Issue Smart Sensing in Structural Health Monitoring of Advanced Composite Structures)

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24 pages, 6678 KiB

Open AccessArticle

Acoustic Emission-Based Analysis of Damage Mechanisms in Filament Wound Fiber Reinforced Composite Tubes

by Parsa Ghahremani, Mehdi Ahmadi Najafabadi, Sajad Alimirzaei and Mohammad Fotouhi

Sensors 2023, 23(15), 6994; https://doi.org/10.3390/s23156994 - 7 Aug 2023

Viewed by 1316

Abstract

This study investigates the mechanical behavior and damage mechanisms of thin-walled glass/epoxy filament wound tubes under quasi-static lateral loads. The novelty is that the tubes are reinforced in critical areas using strip composite patches to provide a topology-optimized tube, and their damage mechanisms and mechanical performance are compared to that of un-reinforced (reference) tubes. To detect the types of damage mechanisms and their progression, the Acoustic Emission (AE) method is employed, accompanied by data clustering analysis. The loading conditions are simulated using the finite element method, and the results are validated through experimental testing. The findings confirm that the inclusion of reinforcing patches improves the stress distribution, leading to enhanced load carrying capacity, stiffness, and energy absorption. Compared to the reference tubes, the reinforced tubes exhibit a remarkable increase of 23.25% in the load carrying capacity, 33.46% in the tube’s stiffness, and 23.67% in energy absorption. The analysis of the AE results reveals that both the reference and reinforced tubes experience damage mechanisms such as matrix cracking, fiber-matrix debonding, delamination, and fiber fracture. However, after matrix cracking, delamination becomes dominant in the reinforced tubes, while fiber failure prevails in the reference tubes. Moreover, by combining the AE energy and mechanical energy using the Sentry function, it is observed that the reinforced tubes exhibit a lower rate of damage propagation, indicating superior resistance to damage propagation compared to the reference tubes. Full article

(This article belongs to the Special Issue Smart Sensing in Structural Health Monitoring of Advanced Composite Structures)

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