FEM-Supported and Embedded Sensing-Enhanced Health Monitoring of Composite Structures

Dear Colleagues,

The structural integrity of composite materials is a critical issue for safety, since certain types of damage, such as delamination, are almost undetectable through visual inspection. Structural Health Monitoring (SHM) systems address this challenge by integrating various types of sensors—such as fiber optic sensors and piezoelectric transducers—into a structure to monitor its integrity through strain measurements. Advanced data processing algorithms are developed to detect and localize potential anomalies and to predict the remaining useful life (RUL) of a component.

The continuous advancement of Finite Element Method (FEM) techniques enables physically based reliable models for residual strength and damage propagation in composite structures. These models play a crucial role in training Machine Learning (ML) algorithms for damage identification, load and shape reconstruction, residual strength, and RUL prediction. They provide the foundation for constructing digital twins, virtual replicas of physical structures that continuously integrate sensor data to assess performance and degradation in real time.

This Special Issue encourages authors to submit high-quality theoretical and experimental papers demonstrating how advanced FEM techniques, coupled with ML techniques, are used to support SHM systems for diagnostics and prognostics in damage-tolerant composite structures and the relevant impact on their maintenance. Such a topic aligns with the objectives of Sensors, exploring how FEM-based models and embedded sensing can jointly enhance anomaly detection and RUL prediction.

Dr. Alessandro Airoldi
Prof. Dr. Giuseppe Sala
Guest Editors

Dr. Pietro Ballarin
Guest Editor Assistant

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