This study experimentally and numerically investigates the structural behavior of prestressed concrete sleepers made of high-performance concrete. Particular attention is given to the formation of bending cracks and the associated deformation behavior. The objective is to analyze the structural response under bending stresses in the midspan and support regions and to validate the numerical model using experimental results.

An experimental program was developed in accordance with relevant standards to examine the bending crack formation and deformation behavior, supported by additional specimens for determining the material properties of the concrete and prestressing steel. Acoustic emission (AE) monitoring was employed to observe fracture processes in real time. To detect microcracking during loading broadband AE sensors with a measurement frequency of up to 200 kHz were used which were especially developed for the detection of microcracks in concrete. For automatic three-dimensional location of the AE sources, a network with 16 of these AE sensors were attached to the surface of the specimen. The AE activity started at approximately 43 kN. At this load the crack formation begins normatively uncritical, but with an influence on durability. Most of the located AE events (approximately 19.000) identifying a vertical macroscopic crack plane in the midspan region where the load was applied. Additional vertical side cracks developed symmetrically parallel to the main crack plane.

Based on experimental investigations with evaluation of the results from digital image correlation and the determination of material parameters, nonlinear finite element calculations were modeled and validated. These calculations were then used in a parametric study investigating the influence of reinforcement layout and bar diameter on the initiation of cracking. The results indicate that bending stiffness in both cracked and uncracked states can be enhanced, and that the onset of cracking can be delayed. However, due to the limited number of test specimens, the findings should be regarded as conditionally valid.

Wind turbines (WT) need to perform well to meet the ever-growing demand of green energy. However, the performance of a wind turbine can be jeopardized due to occurrence of damage within its components. Acoustic emission (AE) is highly sensitive to occurrence of damage and can be used for damage identification. However, the localization of an AE source is challenging using a time domain method for structures that involve material anisotropy and complex geometry (e.g., curved surfaces and variable thickness). With this view, this work presents an unsupervised framework that operates on narrow frequency bands (NFBs) for artificial AE source determination in composite wind turbine blades. Unlike the conventional time-domain methods that require a network of sensors and the time of arrival information, the new method requires only one sensor and the information contained in the reduced frequency bands within each AE signal to discern its source. The unsupervised framework is found to be highly efficient in finding pattern in the multi-dimensional frequency domain dataset extracted from AE signals and can easily cluster the AE signals as per their zone of occurrence in a WT blade. This method can be easily implemented in real and complex structures as it requires only one sensor. Due to the very high degree of accuracy, the method can be applied to real complex systems and structures where time domain methods are not feasible. The method can also be included in a digital twin model for accurate prediction of an AE source.

The long-term structural integrity of ships operating in harsh environments depends on the ability to detect and interpret early signs of fatigue damage under service conditions. Among available non-destructive techniques, Acoustic Emission (AE) monitoring has shown significant potential for detecting crack activity and local degradation processes in real time. However, the interpretation of AE data in full-scale environments remains complex, as ship structures are subjected to continuously varying mechanical, operational and environmental conditions. Demonstrating AE feasibility in realistic ship environments is therefore critical to advancing its integration within structural health monitoring frameworks.

This study presents results from an AE monitoring campaign conducted on board the Polar Supply and Research Vessel S. A. Agulhas II (SAAII) shown in Figure 1 (left), owned by the Department of Forestry, Fisheries and the Environment of South Africa. Two groups of AE sensors were installed: one on the port side near a visually inspected fatigue crack, as shown in Figure 1 (right), and one on the starboard side in a region with a previously repaired crack. This configuration enabled a relative assessment of AE behavior between an active and a repaired region subjected to comparable loading conditions during a single voyage.

In total, more than four million AE signals were collected, approximately 80% of which originated from the port-side sensor group, with amplitudes predominantly between 50 and 70 dB and exhibiting AE characteristics consistent with those observed in previous laboratory experiments.

The monitoring campaign additionally benchmarked the insights from AE measurements in combination with global structural measurements, including strain gauges and accelerometers, whilst considering the ship route and weather information. This combination of data enables an evaluation of how AE activity correlates to operational and environmental parameters.

The study reflects on aspects of sensor deployment, data acquisition, and correlation strategies with other measurements, demonstrating the feasibility and practical considerations of integrating AE monitoring within a multimodal sensing framework for in-service structural monitoring of steel ship structures.

Corrosion is a leading damage mechanism in the degradation of marine assets. Organic barrier coatings are widely used as a corrosion mitigation measure, because their application suppresses the interaction between the metallic structure and the corrosive environment. Electrochemical impedance spectroscopy (EIS) is a well-established technique for the evaluation of coating performance. Acoustic emission (AE) monitoring has gained increasing interest as a technique for continuous monitoring of corrosion damage. In this experimental study, EIS measurements are combined with AE monitoring to investigate the degradation of different organic barrier coatings. Laboratory experiments were performed on 7 aluminum specimens covered with different types of coatings. Water uptake was achieved by immersing the samples in a salt solution for 24 hours prior to each test. Each sample was equipped with an electrochemical cell (Ag/AgCl reference electrode, platinum mesh counter electrode, acidic NaCl solution; pH=2) and an AE sensor (85-180kHz). The acidic salt solution was used to accelerate the degradation of the coatings for a period of 24 hours during which periodic EIS measurements were performed as well as continuous AE monitoring. AE signals detected during the test could be associated with a measured reduction in coating resistance and, in some cases, visible signs of surface degradation. The preliminary results from the study indicate that coating degradation generates AE signals in the same frequency range as that of typical AE from corrosion degradation. This is a promising perspective towards expanding the capabilities of AE based structural health monitoring systems

The increasing adoption of lithium-ion batteries (LIBs) in electric vehicles, energy storage systems, and marine applications has amplified the demand for advanced health monitoring technologies that ensure operational safety and reliability. Among the various failure modes of LIBs, thermal runaway (TR) poses the most severe safety hazard, as it involves uncontrollable exothermic reactions that can lead to fire or explosion. Current monitoring systems based on voltage, temperature, or gas venting typically provide warning only after irreversible internal degradation has occurred, offering limited time for intervention. To address this challenge, this study presents an acoustic emission (AE)-based early warning system capable of detecting the onset of thermal runaway at its initial stage, well before conventional indicators become evident.

The proposed system employs piezoelectric AE sensors coupled to the battery casing to capture transient acoustic signals generated by early-stage internal phenomena such as micro-cracking, electrode deformation, separator failure, and gas evolution. The signals are acquired through a high-speed data acquisition unit equipped with preamplification and band-pass filtering. Real-time data processing includes noise filtering, event detection, and feature extraction across time, frequency, and statistical domains. A threshold is set based on baseline noise characterization to ensure robust event identification.

Experiments were performed on NCM prismatic cells and modules under controlled thermal abuse conditions. A heating plate was used to induce localized heating and trigger TR, while thermocouples monitored temperature changes across the cell surfaces. In the single-cell tests, AE activity was observed approximately 210 seconds after heating began, when the cell temperature was still near 40 °C, far below the onset of venting or visible deformation. As heating continued, AE events increased in both amplitude and frequency, signaling intensified internal reactions. The activation of the safety valve, indicating gas venting, occurred roughly three minutes later, confirming that AE monitoring provides a significant early-warning window.

In the module-level experiments, one AE sensor was installed on the surface of one cell while the other cell was heated. Remarkably, the system detected precursor AE signals from both cells, demonstrating effective coverage of multi-cell structures with very few sensors. This highlights the scalability and cost-efficiency of the method for large-format battery packs.

The results confirm that AE sensing can serve as a sensitive, non-invasive, and scalable approach for early-stage detection of thermal instability in lithium-ion batteries. Compared with temperature or voltage monitoring, AE provides earlier and more direct insight into internal degradation mechanisms. This work extends the application of structural health monitoring (SHM) concepts to the field of battery safety, offering a new direction for integrating acoustic sensing and intelligent diagnostics into next-generation Battery Management Systems (BMS).

Natural fiber reinforced polymers (NFRPs) offer high specific strength and sustainability. However, moisture uptake alters the NFRP’s mechanical properties, such as reduced flexural stiffness, and threatens its durability due to irreversible damage, including microcracks and fiber-matrix debonding. Both effects pose a challenge for NFRPs in structural applications, requiring stable mechanical properties throughout the entire product life cycle.
Acoustic emission (AE) tests are considered a promising non-destructive method for real-time monitoring of micromechanical damage processes and distinguishing damage modes. However, little is known about the extent to which moisture content alters the AE signature in NFRPs. Hence, it is important to clarify how moisture affects the AE measurement before determining damage modes. Therefore, this study systematically investigates the influence of the moisture content on AE signals during tensile tests on hemp/epoxy samples. The objective is to clarify whether and how moisture affects characteristic AE parameters, such as amplitude and frequency spectrum, for a defined damage mode, such as matrix cracks and fiber breakage.
For this purpose, unidirectional tensile specimens with different moisture contents were tested. 0° and 90° laminates were chosen to obtain damage modes dominated by fiber breakage and matrix cracks, respectively. Moreover, multiple specimens of each orientation were stored in water for varying durations to achieve defined moisture contents. Moisture uptake was determined by measuring the increase in mass. During tensile testing, AE sensors continuously recorded the acoustic activity. Subsequently, both parameter-based evaluations and frequency analysis methods were used to identify changes in the AE signature due to different moisture contents. The mechanical load-deformation data were correlated with the AE activity to derive the onset and development of micromechanical damage as a function of moisture content.
The results show the extent to which AE measurements are sensitive to moisture-induced changes in hemp/epoxy composites for given damage modes (fiber breakage or matrix cracks). The results provide a better understanding of moisture-induced changes in the AE signatures and lay the foundation for using AE as a monitoring tool for sustainable, moisture-sensitive fiber composites.