Integrated Structural Health Monitoring of Flax Fiber Reinforced Composites Using Nonlinear Resonance Acoustics, Acoustic Emission and Data-Driven Damage Identification

Othmane Achouham, C. Mechri, R. El Guerjouma, S. Allagui, Zeineb Kesentini, A. El Mahi

TL;DR: This work demonstrates that the combined use of nonlinear acoustics, acoustic emission, and machine learning constitutes a robust and highly sensitive SHM framework for composite structures.

This paper presents an integrated Structural Health Monitoring (SHM) strategy for flax fiber reinforced thermoplastic composites, combining Nonlinear Resonance Acoustic Spectroscopy (NLRAS), Acoustic Emission (AE), and data-driven damage identification based on machine learning. The proposed approach aims to provide both early damage detection and physical interpretation of damage mechanisms by coupling global nonlinear acoustic indicators with local acoustic emission activity, in line with recent SHM developments for composite materials (Bentahar and El Guerjouma, 2008; Allagui et al., 2023). The experimental investigation was conducted under progressive three-point bending loading, with the material characterized at successive damage levels. NLRAS measurements were performed using integrated piezoelectric transducers to excite and monitor a resonance mode of the specimen under controlled fast and slow dynamics. The evolution of nonlinear acoustic parameters, including resonance frequency shift and hysteretic behavior, was analyzed as a function of excitation amplitude. Such nonlinear resonance-based indicators are known to be highly sensitive to damage-induced contact and hysteresis effects in heterogeneous materials (Bentahar and El Guerjouma, 2008; Mechri, et al 2019, Dolbachian et al., 2023). The results demonstrate a strong sensitivity of nonlinear resonance parameters to damage accumulation, with measurable stiffness degradation detected at very early stages, even when conventional acoustic emission activity remains limited. In parallel, acoustic emission signals were continuously recorded during mechanical loading in order to capture local damage events. A large dataset of AE waveforms was processed using advanced signal processing techniques to extract temporal, spectral, and time–frequency features. An unsupervised machine learning framework was then applied, including feature selection, anomaly detection, and clustering. Based on multiple cluster validity criteria, Spectral Clustering was identified as the most suitable algorithm for the AE dataset, following recent advances in AE-based damage classification using machine learning approaches (Almeida, 2023). This approach enabled the identification of four distinct families of AE signals, which were physically interpreted and associated with the main damage mechanisms occurring in flax fiber composites: matrix cracking, fiber/matrix debonding, fiber pull-out, and fiber breakage. The temporal evolution of the identified AE clusters revealed a clear and sequential activation of damage mechanisms, consistent with previous observations on progressive damage in polymer-based composites (Bentahar and El Guerjouma, 2008; Allagui et al., 2023). The Felicity Ratio was further employed to quantify the loss of elastic memory and to correlate AE activity with damage severity. A significant decrease in the Felicity Ratio was observed for precursor mechanisms, indicating early reactivation of micro-damage, whereas critical mechanisms remained close to the Kaiser effect until advanced damage stages, as commonly reported in AE-based SHM studies. Overall, this work demonstrates that the combined use of nonlinear acoustics, acoustic emission, and machine learning constitutes a robust and highly sensitive SHM framework for composite structures. By linking global nonlinear indicators to local, physically interpretable damage mechanisms, the proposed approach offers significant potential for early damage detection, damage mechanism identification, and long-term monitoring of bio-based composite materials.