Smart Sensing in Structural Health Monitoring of Advanced Composite Structures
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 2993
Special Issue Editors
Interests: computational mechanics; composite and sandwich structures; plate theories; structural health monitoring; shape sensing; fiber optic sensing; peridynamics; topology optimization; additive manufacturing
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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Keywords
- shape sensing
- inverse methods
- structural health monitoring
- structural digital twin
- sensor technologies
- composite materials
- sandwich structures
