Conference Agenda

Monitoring of civil engineering structures and buildings is seeing increasingly widespread practical application.

Depending on the particular case, both long-term and short-term situative condition monitoring solutions are available.

Despite their prevalence, especially in structural steelwork, bolt connections are rarely monitored in practice. Still, in many cases periodic inspection is carried out, either through re-torquing or other methods of assessing the preload, such as a sounding hammer.

These methods have in common, that they require good physical access and skilled personnel to perform but are limited in their accuracy.

Combined with the fact that bolt preload can vary significantly upon installation due to uncertainties in friction conditions, digital force measurement is an attractive and reliable method to quickly and comprehensively assess the condition and performance of bolt connections.

Up until recently, digital bolt force measurement usually required an expert setup with costly hardware, complex data handling and in many cases a degradation or change in bolt performance due to the sensor integration.

With the fischer SensorBolt, an off-the-shelf bolt monitoring system is now available. By presenting several practical applications, the authors show how this system can help operators and owners of bolted installations to easily and reliably monitor the condition of their fixings, both immediately after installation and after an extended period of time.

To increase practical feasibility, a software solution complements the physical products and offers unified data and device management for projects of any scale. Alternatively, integration into existing systems is possible, to interpret the sensor data from bolts together with other SHM data.

This paper presents the main results obtained from field measurements carried out on a curved railway span composed of four large spans (45.0 m, 47.5 m, 95.0 m, and 57.5 m) between supports T2-133 and T2-137. The structure consists of steel I-girders, and several experimental tests were conducted, including Ambient Vibration Tests (AVT), Dynamic Load Tests (DLT), and Static or Pseudo-static Load Tests (SLT), using real train wagons loaded with sandbags. The objective was to assess and verify the structural safety of the original design.

To analyze the dynamic structural behavior of the selected span, vertical displacements at the decks, end beams, and bearings were recorded during train passages on both tracks at different speeds under design loading. Seventeen triaxial accelerometers were strategically positioned along and across the structure. The AVT provided the natural vibration periods (Ti) in the longitudinal, transverse, and vertical directions, as well as the predominant ground-motion period (Ts) at the site. The obtained fundamental vibration periods were consistent with the structural configuration.

Complementary topographic measurements were performed before, during, and after the DLT to determine deflection patterns under service loads. Analytical predictions from a three-dimensional finite-element model were compared with experimental data, showing good agreement. Both experimental and analytical results confirmed that the maximum vertical deflection occurred at mid-span of the 95.0 m main span.

Finally, the vertical deformation limits specified by national and international design codes (SICT–AASHTO) were verified and satisfied. Based on these results, the structural behavior of the analyzed curved railway span is considered adequate, and the relative vertical displacements in the superstructure are not significant.

The transport, energy and defense industries are currently facing the challenge of reducing maintenance costs while increasing structures availability. Today, structural inspections are carried out through periodic, systematic procedures that may lead to unnecessary ground immobilisation when no damage is ultimately detected. A key lever to address this limitation is to shift towards individualized maintenance, in which inspections are triggered only when required. Such an evolution presupposes that each structure is capable of reporting its structural health throughout its service life. Embedded Structural Health Monitoring (SHM) systems constitute a promising pathway to achieve this goal. However, despite extensive research and numerous prototypes, SHM solutions remain insufficiently mature for large-scale industrial deployment. One major obstacle lies in the difficulty of integrating sensors and associated systems (power supply, data transfer, optical or electrical linkages) within metallic and composite structures. In many current developments, sensors are conceived independently from their integration constraints, which are addressed only a posteriori. As a result, integration steps remain largely manual, intrusive, and highly dependent on operator expertise, preventing the repeatability, robustness, and scalability expected in industrial manufacturing chains.

The present work focuses on the development of non-intrusive integration strategies for SHM-related power and connectivity systems directly within manufacturing processes. The objective of the demonstrator initiated at IRT Jules Verne is to build technological and methodological competences enabling the functionalisation of structures during fabrication, as well as after assembly or during maintenance operations. The proposed approach aims to design and validate versatile, non-intrusive interconnection solutions, electrical, optical, or hybrid, that can be adapted to various sensors and manufacturing routes for both elementary parts and assembled components.

Insights gathered from several SHM workshops highlight strong industrial expectations toward solutions that are: non-intrusive; compatible with both in-process integration and end-of-line or repair operations; mature in terms of reliability, accuracy, durability, and measurement repeatability; easy to implement manually with robust procedures; and capable of being interrogated non-continuously in order to limit system complexity and operational costs.

Positioned at the interface between advanced manufacturing and structural monitoring technologies, IRT Jules Verne is developing processes and integration routes that seek to industrialise SHM embedding steps, transform current manual practices into controlled and repeatable operations, and address the persistent challenges associated with connectivity within structures. By reinforcing the manufacturability and durability of SHM integration, this work contributes to paving the way toward deployable, certifiable, and economically viable monitoring solutions for next-generation transport, energy and defense structures.

Wind turbines, being a source of renewable energy, are getting increased attention to meet the growing demand of green energy. The performance of a wind turbines can be significantly compromised due to the occurrence of various forms of damage within its components. Moreover, an undetected damage can grow rapidly over a period and may cause a failure of the entire wind turbine, resulting in a significant decrease in the output of a wind energy farm that may incur a sizable economic loss. However, the material anisotropy and curved surface of a composite wind turbine blade pose a significant challenge to apply a conventional time domain method for damage localization. To address this issue, a methodology has been developed that involves Hilbert Transform and correlation to analyse ultrasonic guided wave signals to localize and categorize damage in a composite wind turbine blade. The probing signal used is a Hanning window modulated 5- cycles sine signal. In t wind turbine his method, the targeted zone is discretized into several grid points which are assumed to be damage locations. The actual damage location is then identified through a correlation index of Hilbert transformed signals. The correlation index obtains the maximum value when the assumed damage location is a true damage location. Moreover, the results also show that the pattern in the Hilbert transform signal can be used for identifying the type of damage. An extensive numerical investigation has been performed considering various damage locations/types in the wind turbine blades to realize the effectiveness of the method. In the experiment, a few damage locations/types are considered. The method has localized and characterize damages with a high degree of accuracy in both the cases. Therefore, indicating a large-scale application of the method in the real wind energy systems for inspection of structural health and maintenance.

Guided waves are widely used for damage detection in composite structures due to their high sensitivity to defects and long propagation distances. However, strong material attenuation in composites limits the effective inspection range. This study employs multi point excitation over large and complex structures and introduces a time varying spatial filter to track S₀ mode and its convertion to A₀ mode, and mitigate mutual interference among sources. The filter achieves single mode isolation under multi point actuation and performs attenuation compensation to extend the inspection domain. Clean damage signatures are then extracted and used for imaging. The proposed method is validated through experimental tests on aircraft CFRP stiffened panel with barely visible impact damage, demonstrating wide area coverage, and improved detection reliability.

Detecting concealed detachments and localized adhesion failures in historical plasters—especially in stratified or layered architectural surfaces—remains a central challenge in the non-invasive diagnostics of cultural heritage. Conventional auscultation, relying on manual tapping and auditory assessment, is fast and intuitive but inherently subjective, leading to inconsistencies in acoustic classification and, consequently, diagnostic reproducibility.
The advent of instrumented acoustic probes has facilitated the objective collection of impact response data, capturing both time-domain and frequency-domain features suitable for algorithmic interpretation. Yet, applying supervised learning in situ is limited by the persistent absence of richly labeled and spatially resolved ground truth data, a common constraint in real-world conservation contexts.

To address this, the present study proposes a novel methodology that combines minimal supervised input with temporal and spectral feature analysis to allow reliable and repeatable adhesion classification. The central innovation is a two-point training approach, wherein the neural network is trained using data from just two manually selected reference sites within the target area—one firmly adhered (label 0), the other clearly detached (label 1). Each site provides repeated impact responses using the PICUS probe, a handheld probe that can combine accelerometer and microphone sampled signals, establishing the acoustic boundary conditions for classification. The trained model then processes new impact signals acquired through a systematic surface scan, classifying each according to its proximity to the known states. The outputs are spatially interpolated to generate adhesion maps delineating intact and detached zones.

This localized, two-point strategy introduces a flexible AI-driven diagnostic model that adapts to site-specific conditions, bridging the gap between controlled laboratory datasets and variable field environments. It enables each survey area to function as its own calibration space, ensuring context-sensitive reliability without the need for large-scale labeling or external reference models.

To enhance interpretability and improve model generalization, each impact record from the reference datasets is analyzed through the extraction of a compact suite of spectral and temporal features that characterize the mechanical and acoustic behavior of the material.

These physical descriptors serve as input to a convolutional neural network (CNN). The network effectively discriminates between the two acoustic states, demonstrating robust performance across different materials such as lime and pozzolanic based mortars like in heritage structures or nowadays synthetic materials. The model is further extendable via transfer learning to unlabeled datasets, allowing autonomous classification and mapping where ground truth is unattainable—a critical capability for practical conservation work.

The approach proves robust against sensor and environmental variability, as the learned features are grounded in spectral signatures rather than absolute amplitude values.

This approach offers a practical and scalable solution for on-site, data-driven diagnostics, reducing dependence on expert subjectivity while maintaining physical interpretation through feature-based learning. Future developments will focus on extending the method toward multi-class classification, integrating transitional adhesion states, refining spatial interpolation algorithms for high-resolution adhesion mapping and lightweight deployment compatible with TinyML environments.

This paper investigates processing methods for Lamb wave signals intended for wireless structural health monitoring systems. In such systems, data acquisition is limited by reduced sampling rate compared to standard wired configurations, which introduces challenges in accurately extracting damage-sensitive features. Conventional approaches based on time-of-flight (ToF) delay estimation may become unreliable under these conditions due to waveform distortion and insufficient temporal resolution. Therefore, alternative processing strategies are required to ensure accurate damage detection and localization. In this study, the feasibility of reconstructing the original high-resolution Lamb wave signal from low-sampling rate wireless measurements is evaluated, enabling the continued use of ToF-based damage detection. Different signal-reconstruction techniques are examined and compared in terms of accuracy, robustness and computational efficiency. The results demonstrate that signal reconstruction significantly improves ToF estimation reliability, making it a promising solution for future wireless Lamb-wave-based monitoring platforms.

Filament-wound glass fiber reinforced polymer (GFRP) vessels are widely deployed in water management systems for the collection, separation, and treatment of stormwater and industrial effluents. During installation as well as long-term operation, these composite structures may be exposed to complex loading scenarios, including localized stress concentrations that can initiate damage and subsequently compromise structural integrity. Reliable structural health monitoring (SHM) methods are therefore essential for detecting strain concentrations and early-stage damage in order to ensure long-term operational safety. Carbon Fiber Sensors (CFS), which utilize changes in electrical resistance to quantify mechanical strain, represent a promising low-cost sensing technology suitable for direct integration into composite structures, see (1). This contribution extends previously published work (2) focused on the strain measurement of GFRP plate-type specimens with a central hole subjected to four-point bending. In the earlier study, CFS and distributed fiber optic sensing (DFOS) were used to evaluate strain distribution and validate sensor performance, with the results showing good agreement between both measurement techniques and finite element method (FEM) simulations. Building on these findings, the present study investigates the sensitivity of CFS sensors to artificially introduced damage in the form of controlled notches of increasing depth representing crack-like defects in the GFRP plates.

New experiments are conducted on filament-wound GFRP specimens instrumented with surface-mounted CFS and DFOS sensors. Each specimen is subjected to repeated four-point bending cycles for multiple predefined damage levels. The progressive increase in notch depth enables a systematic evaluation of the relationship between structural response and sensor output. The resulting data shows the determination of functional dependencies between specimen deflection, local strain, CFS resistance change, and crack depth. Particular attention is given to the repeatability of measurements, the detectability threshold of damage, and the influence of sensor placement relative to the defect.

Numerical simulations of the experiment are performed using a detailed shell-based FE model consistent with the geometry and material properties of the tested specimens. Strain values are extracted along the paths corresponding to sensor locations and compared with both DFOS and CFS measurements. This comparison provides insight into the accuracy, limitations, and potential application of CFS technology for detecting and quantifying damage in GFRP composites.

The experimental and numerical findings confirm that carbon fiber sensors possess adequate sensitivity not only for monitoring global deformation but also for detecting localized damage, such as crack-like defects. The clear correlation between notch depth and electrical resistance response shows their potential for tracking damage evolution in filament-wound GFRP structures.

[1] Horoschenkoff, A., & Christner, C. (2012). Carbon Fibre Sensor: Theory and Application. In N. Hu (Ed.), Composites and Their Applications. InTech. https://doi.org/10.5772/50504

[2] Schmidová, N., Doubrava, K., Padovec, Z., Blaha, D., Novotný, C., & Růžička, M. (2024). Bending Analysis of GFRP Composite Plate with a Hole Using Carbon Fiber Sensors. In 62nd Conference on Experimental Stress analysis - Book of Extended Abstracts. CTU.

Inflatable structures are promising for application in large spacecraft due to their lightweight nature, low cost, and high deployment reliability. However, the flexible textile composites used in such structures are vulnerable to hyper-velocity impacts from micrometeoroids and orbital debris (MMOD) during service, which poses a serious threat to both spacecraft safety and astronaut lives. To address this issue, this paper proposes an integrated sensing approach in which a carbon nanotube (CNT) sensing layer is embedded into the flexible textile composite, combined with electrical impedance tomography (EIT), to enable continuous online monitoring of MMOD hyper-velocity impacts. This allows for timely detection of impact events and identification of damage location and approximate size. Firstly, CNT films synthesized via floating catalytic chemical vapor deposition (FCCVD) and associated circuitry are integrated into the textile composite using hot-press lamination, endowing the composite with self-sensing functionality. Then, low-amplitude currents are periodically injected into the CNT sensing layer, while boundary voltage data are recorded. The occurrence of a hyper-velocity impact event is detected by monitoring changes in the boundary voltage. Finally, the EIT algorithm is then employed to reconstruct the conductivity distribution within the sensing layer, visualizing variations caused by impact damage. This process yields image-based information on the location and approximate size of the damage. To validate the method, flexible self-sensing textile composite specimens are fabricated and subjected to high-velocity projectile tests to produce damage similar to that caused by MMOD impacts. Experimental results are used to confirm the feasibility and effectiveness of the proposed technique.

Spaceborne Synthetic Aperture Radar (SAR) is a non-contact remote sensing technology that detects surface deformation by analyzing the phase differences between radar images acquired over the same area at different times. Due to its extensive coverage, high spatial resolution, and all-weather operational capability, spaceborne SAR has become an established technique for large-scale, continuous monitoring of civil infrastructure. Transportation networks constitute a fundamental component of urban infrastructure, playing a pivotal role in enabling efficient mobility and fostering regional economic development. Extreme weather events severely threaten the durability and operational safety of transportation networks. However, limited funding restricts the deployment of traditional sensors for detailed and comprehensive monitoring of the entire transportation network system. In this research, a stack of Sentinel SAR images acquired over a two-years period is collected from the Copernicus Data Space Ecosystem, and subsequently processed with Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology. A dedicated post-processing procedure, consisting of PS points refinement and clustering analysis, is applied to the displacement time series derived from the PS-InSAR processing. Then, statistical control limits method is employed to evaluate the risk levels across transportation network. Finally, the reliability and effectiveness of the proposed risk assessment framework are validated through a specific bridge case study. These findings demonstrate the potential of the proposed framework for large-scale, risk-informed assessment of transportation networks, thereby contributing to more proactive and data-driven transportation network management strategies.

A wide variety of metallic structures are extensively used in aircraft. However, these metallic structures inevitably develop fatigue cracks during long-term service, which seriously threatens structural safety. With the advancement of structural health monitoring and digital twin technologies, promptly detecting the initiation and growth of cracks and making informed decisions are of great significance for ensuring flight safety. This paper presents an experimental study on the online monitoring of crack growth in metallic structures under fatigue loading using a piezoelectric sensor array and ultrasonic guided wave technology. A center-cracked aluminium alloy plate is prepared, and a network of piezoelectric wafer sensors is deployed on its surface. During the fatigue test, while observing crack growth, the piezoelectric wafers were used to excite and receive ultrasonic guided wave signals respectively, monitoring the fatigue crack growth behaviour between the actuator and sensor pairs. Through time-domain signal processing, original guided wave signals and energy characteristics of wave packets post-Hilbert transform are extracted. An energy damage index (EDI) was defined to evaluate its variation with crack growth. Finally, by integrating EDIs obtained from all actuator-sensor paths in the piezoelectric sensor network using a damage probability imaging method, the change in crack length is visually characterized in the form of images. Experimental results have verified the feasibility and effectiveness of the proposed method.

This paper introduces a integrates compressive sensing with a physics-guided reconstruction method for high-speed full-field laser scanning–based structural damage detection. Conventional full-field laser scanning requires high-resolution spatial sampling, which leads to excessive measurement time and computational cost in large-scale inspections. To address this limitation, the proposed approach employs pattern-based compressive ultrasonic scanning, in which the inspection area is partially sampled using two-dimensional random patterns, and steady-state responses are efficiently acquired using a Laser Doppler vibrometer (LDV) under single-frequency excitation. The sparsely measured data are then reconstructed into full-field wave responses using a neural network–based method that incorporates physical constraints governing wave propagation. By simultaneously leveraging the physical wave model and the measured data, the proposed reconstruction scheme is designed to recover spatially coherent wavefields even under high compression ratios. The reconstructed wavefield is subsequently analyzed using wavenumber-based damage visualization techniques to estimate the location and size of structural damage. The proposed framework was validated through experiments on aluminum and composite plates with various types of damage, including corrosion and delamination. The results demonstrate that the combination of compressive sensing and physics-guided reconstruction yields higher accuracy and improved physical consistency compared to conventional methods, significantly enhancing the efficiency of full-field ultrasonic damage detection and visualization.

Out-of-Autoclave (OoA) techniques are emerging as sustainable alternatives to the traditional autoclave-based production of high-value composite structures for safety-critical applications. Although they provide the controlled environment required to minimise porosity defects and ensure structural soundness, the operation of autoclaves is expensive, energy-intensive, and limits the size of the final component. On the other hand, OoA methods, particularly resin infusion of dry fibres, followed by cure using mould-integrated heating systems, offer substantial reductions in operating costs and more flexibility in terms of component sizing. However, in the absence of the high consolidation pressures associated with autoclaves, the likelihood of porosity defects increases, restricting the adoption of OoA in high-performance sectors. This limitation requires reliable in-situ monitoring of the resin flow to identify deviations in the process at an early stage.

This study proposes an Ultrasonic Guided-Wave (UGW) sensing array integrated into the upper lid of an infusion mould as a non-intrusive method for mapping the progression of the resin in real time. The main objective is to establish a robust and scalable approach that can capture the fluid distribution over large areas. To achieve this, piezoelectric ultrasonic transducers are used to excite guided waves through the lid and interact with the advancing resin. Through combined theoretical, simulation-based, and experimental observations, the fundamental antisymmetric mode is identified as optimal for liquid propagation monitoring due to its amplitude variation with respect to the proportion of the solid medium covered by fluid.

To evaluate and validate the method, a custom experimental setup is developed to allow the collection of repeatable fluid infusions within a mould, under controlled flow conditions. Time stamped ground-truth data for resin-front location are obtained using a machine-vision system, enabling direct correlation with the collected UGW data. Sensor positioning is optimised to balance measurement sensitivity with increased coverage of the monitoring region, ensuring that the results remain accurate with a sparse sensor network.

The collected dataset is used to assess several predictive modelling strategies. Firstly, an analytical model based on functional approximations of the evolution of the UGW energy provides a baseline prediction accuracy with a best error of approximately 10% of the distance between the sensors. Subsequently, a machine learning model reduces the best error to 5%, demonstrating the potential of the method, even when the training set is limited in size. Lastly, to address data scarcity for industrial deployment, a high-fidelity COMSOL digital twin is used to generate synthetic UGW data. This data is used to augment the training experimental datasets and domain-adaptation techniques are explored.

This research demonstrates that UGW could be a tool for reliable resin infusion front tracking in OoA processes, offering a practical method that could prevent the formation of void defects during manufacturing. Further validation on larger parts, with more complex geometries and different infusion conditions, is required to ensure robust industrial deployment.

Long-span cable-supported bridges are highly susceptible to windstorm-induced excitations due to their flexible structures, low damping ratios, and large aerodynamic surfaces. Extreme wind events can trigger excessive vibrations, loss of serviceability, and even structural instability, posing serious risks to both safety and traffic operations. Timely bridge closure during severe windstorms is therefore critical to prevent accidents and ensure operational resilience. However, manual decision-making may suffer from delays and inaccuracies under rapidly evolving windstorm conditions. To address this challenge, this study proposes an automated AI-aided alert framework for bridge closure during windstorms based on the concept of unsupervised anomaly detection. Building upon this, an unsupervised anomaly detector is trained by solely using acceleration time histories recorded under normal wind conditions. In this case, the developed anomaly detector can autonomously learn the baseline dynamic behaviour of the bridge being monitored. When windstorm-induced vibrations occur, the trained anomaly detector identifies deviations from the learned normal patterns in real time based on a decision threshold and triggers automated closure alerts. The proposed method is validated using measured acceleration responses measured from a long-span cable-stayed bridge under both normal and windstorm conditions. Results demonstrate that the proposed model can effectively detects windstorm-induced anomalies with high sensitivity and low false-alarm rates, enabling timely and reliable bridge closure decisions.

Vibrational monitoring of structures traditionally relies on contact sensors such as accelerometers, displacement transducers, and strain gauges, which provide reliable physical information for structural health assessment. However, these sensors require manual installation and direct access to the structure, resulting in practical limitations in terms of installation time, safety, and accessibility, while long-term maintenance may compromise the overall Structural Health Monitoring (SHM) system reliability. These constraints often hinder the systematic implementation of SHM, particularly for large-scale infrastructures such as bridges. Alternative approaches present complementary limitations: vision-based techniques, such as digital image correlation, are sensitive to lighting conditions, camera calibration, and line-of-sight occlusions, whereas fibre-optic sensing systems, despite their high accuracy and distributed capabilities, require permanent installation and physical integration within the structure, limiting their suitability for rapid or temporary monitoring campaigns.

To address these challenges, Ground-Based Interferometric RADAR (GB-InRA) technology has emerged as a promising non-contact alternative for vibration monitoring, enabling safe measurements where visual inspection or contact sensor deployment are impractical. Interferometric radar enables the detection of sub-millimetric displacements by measuring the phase difference between transmitted and received signals. In this context, Real Aperture Radar (RAR), which provides one-dimensional line-of-sight measurements, is typically preferred for vibration monitoring over Synthetic Aperture Radar (SAR), which enables two-dimensional imaging. Nevertheless, radar-based measurements remain sensitive to instrument-to-target distance and antenna tilt, which must be carefully calibrated to ensure reliable results.

In the perspective of bridge monitoring, this work presents an experimental study conducted on a simply supported flexible steel beam (Figure 1). Vibrations were measured using an IBIS-FS microwave interferometric radar and compared against conventional displacement transducers adopted as ground truth. Three corner reflectors were installed at distinct beam locations to enable the estimation of the first three vibration mode frequencies and shapes. Impulsive hammer excitations induced sub-millimetric displacements, allowing a quantitative assessment of radar sensitivity and measurement accuracy. Preliminary results indicate displacement amplitude errors ranging between 0.02 mm and 0.2 mm for reference amplitudes between 0.5 mm and 12 mm, when compared to displacement transducers. Different configurations of vertical tilt and radar-to-target distance were also investigated to evaluate their influence on displacement estimation and modal identification. Overall, the findings demonstrate the effectiveness and practical applicability of GB-InRA as a robust, non-invasive tool for vibration monitoring and modal identification of civil structures.

Accurate reconstruction of complex structural deformation is essential for understanding transient mechanical responses in hybrid and multi-material systems. This study presents a vision-based approach for three-dimensional deformation reconstruction of a composite–wood hybrid structure subjected to transient load release. The investigated specimen consists of a U-shaped carbon-fiber-reinforced composite beam connected perpendicularly to a wooden cantilever beam, forming a configuration that exhibits strong coupling between bending and torsion during load removal.

A single-camera imaging system was employed to capture the structural response throughout the release process. Surface motion was extracted from image sequences using feature tracking and temporal filtering techniques. Based on the known structural geometry and camera calibration parameters, the three-dimensional deformation field was recovered from single-view data. This method enables non-contact and full-field reconstruction without requiring embedded or attached sensors, making it well suited for hybrid or geometrically irregular structures.

Preliminary experiments demonstrate that the proposed approach can effectively capture the evolution of deformation and reveal coupled twisting and bending behaviors during the transient release event. The reconstructed deformation patterns qualitatively agree with visual observations, highlighting the capability of single-view vision methods to characterize dynamic structural responses.

The proposed framework provides a practical and low-cost solution for dynamic deformation monitoring of complex and multi-material structures. It underscores the potential of vision-based sensing as a valuable complement to conventional techniques in structural health monitoring applications where sensor deployment or accessibility is limited.

Composite structures are widely used in the aerospace industry as important load-carrying components due to their excellent performance. However, composite structures are susceptible to impact damage and manufacturing flaws, compelling the designers and airline operators to ensure continuous safety. Lamb wave-based techniques are gaining immense popularity for structural health monitoring (SHM) applications to detect internal and surface damage in thin-walled structures, such as aerospace structures, using embedded and surface-bonded transducers. The damage detection using Lamb waves conventionally relies on a comparison of the damage features of the current response with the baseline response previously obtained from the same structure in its pristine condition. However, the same Lamb wave features vary by a change in the operating temperature of the structure, leading to possible false alarms for damage. To overcome this problem, baseline-free damage detection methods have been pursued in the recent past. Among various techniques, the one based on the time-reversal process (TRP) of Lamb waves has emerged as the most promising candidate for damage detection in thin-walled structures. For a system of sensor arrays to be used for SHM following TRP of Lamb waves, it is essential that a single frequency should be used to probe the structure along all the paths of the sensor array and the time reversibility in an undamaged structure should be the same for all the paths. However, to the best of the author's knowledge, no study has reported how the layup configuration of a composite structure affects the time reversibility of Lamb waves in different paths of a sensor array.

The present work aims to numerically study the effect of the layup configuration of the composite structure on the time reversibility of the Lamb waves. The numerical study is conducted using three-dimensional (3D) finite element (FE) simulation in CIVA, which uses a transient spectral finite element. The simulation is conducted on two configurations of composites, namely, quasi-isotropic and cross-ply. A composite plate of 2 mm thickness with 16 laminae of 0.125 mm thickness each is considered. Five transducer locations are considered on each plate in such a way that one transducer (T1) is located at the center and others (T2, T3, T4, and T5) are located on a quarter circle of radius 200 mm. The transducer system constitutes four paths T1-T2, T1-T3, T1-T4, T1-T5 which makes 0°, 22.5°, 45°, and 90° to the global x-axis of the system. The simulation is conducted by first actuating the transducer T1 with a 5-cycle tone burst signal and emitting back the time-reversed version of the response obtained at T2. Then, the normalized version of the reconstructed signal obtained at T1 is compared with the normalized version of the input signal to quantify the degree of the Lamb wave reconstruction. The simulation is being repeated for all the paths and for both plate configurations. The result shows that while in the case of a quasi-isotropic configuration, the variation of the quality of time reversibility obtained for different paths is minimal. Its variation in the case of cross-ply laminate is significant.

A critical challenge in implementing sensors for Structural Health Monitoring (SHM) is minimising the intrusiveness of the sensors and cabling implied. Low-profile and lightweight sensors can be manufactured using printed electronics technologies, which are able to print scalable and flexible devices. By printing the connection wires all-together, heavy extensive cabling is also avoided. This solution would allow integrating larger number of sensors over large area without reducing the mechanical performances of the structure.

In this context, printed strain gauges were developed using conductive inks (silver-based and carbon-based) and evaluated as an alternative to conventional strain sensors. The developed silver-based printed sensors were first attached to metal and composite plates using thermoplastic films. Commercially available strain gauges were also attached on the other side of the plates for comparison. Under fatigue testing, the silver-based printed strain gauges showed good sensitivity to strain with resistance change similar to that of commercial strain gauges, despite a very low untrained resistance of 10 Ω. They also showed good repeatability, and fatigue resistance under 1000 cycles of tensile loading. The resulting strain gauges were estimated about 30 times thinner and 30 times cheaper than commercial strain gauges.

In addition to improved integration to the structure, using printing technology allows to in-situ fabricate a cheap network of sensors more accurately and more efficiently than manually bonding commercial sensors. To demonstrate adaptability, tailored designs of strain gauges in different directions, i.e. strain rosette, along with connection wires were printed which demonstrated their feasibility to achieve full-field multi-axial strain map. In the attached figure, silver-based printed strain gauges are compared to carbon-based printed strain gauges attached to composite plates. The developed carbon-based printed strain gauges have higher untrained resistance of about 1 kΩ. Under manual bending of the composite plate, they showed considerably higher sensitivity. They were also able to detect strain direction, as the higher strain was for the strain gauge aligned with strain direction; half-strain was detected by the strain gauge at 45°; almost no strain was detected by the strain gauge at 90°.

On-going work is further evaluating the monitoring performances of the carbon-based printed strain gauges. These sensors show good potential for embedding within composite structures to create smart structures. The development of other types of printed sensors is also considered to diversify the monitoring performances.

Structural Health Monitoring involves identifying and assessing structural changes or damage with or without relying on predefined numerical models. Model-free methods exploit only damage-sensitive dynamic characteristics obtained directly from measured responses. Some of these are based on hypotheses regarding expected damage, which turn into recognizable footprints. Alternatively, model-free methods require the availability of a baseline that refers to an intact condition. The proposed damage identification methods fall into the latter category because they enable efficient and reliable monitoring of structural integrity under operational conditions using an array of accelerometers. The scope of this study is to validate both methods experimentally by applying them to scale-model tests simulating the onset of damage on a real ship.

The first method is based on a macro index, mapping the probability of damage over the investigated structure. It incorporates several damage indices that process the sampled modal curvatures. These modal curvatures, linearly averaged or squared over the sensor mesh (geometric strain energy), are computed in both the reference-intact and current-damage conditions and fed into the different functions characterizing the various indices. If damage is effectively present in one element of the sensor mesh, damage severity can be inferred from the index value. Nonetheless, damage localization can be improved if the indices are Z-score normalized and combined into the macro-index using ensembling strategies. Thus, thresholds for damage existence can be properly set low on the index average to have sufficient sensitivity, while agreement conditions among indices can be exploited to reduce false warnings.

The second method is a novelty detection approach based on a histogram score. It shares with the previous method also the use of one of the indices considered above (the Cornwell’s formulation of the Modal Strain Energy Index) though in a different way. The operational modal analysis provides the vibration modes from the tests, which are characterized by noise from different sources. To train the method with a sufficiently large ‘intact’ population, the mode shapes are first averaged and then contaminated with Gausisan noise, experimentally modelled. The statistical distribution of the Cornwell’s damage index provides the baseline to evaluate whether the same quantitity, computed directly from the experimental data, can be attributed to an underlying structural modification. The threshold to separate the intact and damaged classes plays again a crucial role.

The damage identification techniques are validated using data collected in scale-model tests of a navy vessel within the “Digital Ship Structural Health Monitoring project” (dTHOR), granted by the European Defence Fund, aimed at developing a system based on innovative utilization of extensive on-board measurements, a comprehensive digital framework, and hybrid analysis and modelling. The ship longitudinal bending stiffness is reproduced by an elastic backbone connecting the hull portions. The damage is artificially generated by removing the plate elements on top and side faces of the aluminium backbone and affects the operational modes identified while the physical model is towed at the CNR-INM wave basin. Results, though quite promising, are critically reviewed in the perspective of further increasing their accuracy.

This paper explores guided wave propagation in a square aluminum beam through both numerical and experimental methods. The damaged and healthy conditions of the aluminum beam were considered for the analysis. Damage was modeled experimentally and numerically in the form of an open crack. The guided waves are excited by a piezoelectric disc bonded to the beam at the front face. The aim is to determine the optimal placement of fibre optic strain sensors to enable the separation of propagating guided wave modes. Mode separation is crucial because it simplifies the signal processing required for damage detection. To achieve high sensitivity to guided waves with amplitudes in the nanometre range, fibre Bragg grating (FBG) sensors combined with an edge filtering method were utilised. The advantage of FBG sensors in comparison to piezoelectric transducers (PZTs) is that FBGs can be embedded into composite laminates without a detrimental effect on their strength. Complementary measurements were taken using scanning laser Doppler vibrometry, allowing full wavefield analysis. Additionally, numerical simulations of elastic wave generation and sensing were conducted using COMSOL software. A 3D model is applied with electromechanical coupling, which includes asymmetry caused by the shape of PZT (simulating wrap-around electrode). The focus of this research is on the potential for sensing symmetric and antisymmetric guided wave modes with mode separation capabilities. For this purpose, a series of FBG sensors positioned on the perimeter of the specimen are employed.

Methodology

An aluminium beam measuring 2 m × 0.01 m × 0.01 m was used for both experimental and numerical investigations. A crack with a depth of 2.5 mm was introduced at a location 1.2 m from the left end of the beam. In the experimental setup, an actuator was mounted on the front face of the beam to excite guided waves using a PZT transducer. It should be noted that the wrap-around electrode and solder material used in the PZT actuator introduce asymmetries. Wave responses along the beam were captured using scanning laser Doppler vibrometry. The acquired signals were band-pass filtered around the respective carrier frequencies. A five-cycle Hann-windowed sinusoidal tone burst with a carrier frequency of 50 kHz was generated using a waveform generator, and the excitation signal was amplified tenfold using a voltage amplifier. The sampling rate was set to 1.28 MHz.

Results

The experimental results were compared with the numerical simulations. The waterfall plot for guided wave propagation from experimental measurements and numerical simulations show two guided wave modes, A0 and S0. A very good match of velocities between experiment and numerical simulation is observed.

Acknowledgment

The authors would like to gratefully acknowledge the support given by the National Science Centre, Poland, under grant agreement no. 2020/39/B/ST8/01753 in the frame of the OPUS project entitled: "Study of elastic wave mode sensing and separation using FBG sensors for structural health monitoring".

The Structural Health Monitoring (SHM) of aerospace vehicles faces extreme
challenges due to harsh operating conditions, stringent latency requirements, high reliability
constraints, and limited energy resources. While conventional wired SHM architectures remain
reliable, they significantly increase the total weight and complexity of the vehicle’s electrical
system. This weight penalty is particularly pronounced in commercial and military aircraft, where
the extensive wiring infrastructure has become a critical design constraint. According to the
Wireless Avionics Intra-Communications (WAIC) initiative, modern aircraft can have
approximately 100,000 wires totaling 470 km in length and weighing around 5,700 kg, with about
30% of these wires being potential candidates for wireless substitution to reduce weight and
simplify installation. Consequently, there is a growing trend toward adopting wireless sensor
networks (WSNs) to maintain reliability while reducing wiring mass. While WSNs offer
promising weight reduction benefits, current implementations lack the adaptive intelligence
required to satisfy the complex, multi-dimensional QoS constraints of aerospace systems,
particularly when facing simultaneous demands for minimal latency, maximum reliability, and
optimal energy consumption. These multi-objective constraints necessitate more intelligent
network management, that can autonomously adapt to changing mission requirements and
environmental conditions.
This work presents an embedded Artificial Intelligence (AI) framework for autonomous WSNs
dedicated to aerospace vehicle SHM. The proposed system leverages Time Slotted Channel
Hopping (TSCH) and RPL routing protocols, enhanced by an adaptive AI-based controller
(CEREBRO), that dynamically optimizes communication schedules based on real-time network
conditions and environmental variations. The proposed AI framework combines convolutional
layers for capturing local spatial structures with specialized temporal processing blocks designed
to model sequential dependencies and temporal dynamics in the data. The combined model
outputs adaptive routing and scheduling vectors, which CEREBRO uses to adjust slot allocations
and routing paths based on network operations and conditions, ensuring that critical
measurements are delivered reliably and efficiently while extending network lifetime in complex
aerospace environments.
The proposed approach has been experimentally validated on a real testbed using the FIT IoT-
LAB platform, showing significant improvements in reliability, latency, and energy consumption
compared to static TSCH configurations. The resulting architecture provides a scalable,
lightweight, and energy-efficient foundation for embedded SHM in aerospace structures. Overall,
this work highlights the potential of embedded intelligence to transform SHM systems into fully
autonomous and adaptive diagnostic networks, capable of operating reliably and efficiently in the
extreme environments of aerospace systems. To accommodate different deployment scenarios,
CEREBRO is available in both a CPU implementation for resource-constrained nodes and a GPU-
accelerated version on NVIDIA Jetson platforms for high-performance edge computing.
The framework advances the paradigm of edge-based diagnostics by bringing intelligence directly
to the acquisition system, enabling real-time analytics and adaptive decision-making without
relying on centralized cloud processing.

High-density heat exchangers play a pivotal role in advanced thermal systems, yet precise internal monitoring remains challenging due to the invasiveness and limited access of conventional sensing methods. Accurate characterization of local fluid temperature and heat transfer coefficients (HTC) is essential for evaluating thermal performance and ensuring structural integrity in complex micro-channels. In this study, we introduce a new design variable, 'Fiber Drag Length (LD)', to resolve the inherent cross-sensitivity between temperature and strain in Fiber Bragg Grating (FBG) sensors. By employing a self-compensating differential measurement technique—combining a strain-free reference sensor with active measurement sensors—the system effectively decouples mechanical flow drag from thermal loads without the need for bulky packaging. Validated in a shell-and-tube heat exchanger environment under turbulent flow conditions (Re 5889~20769), the sensor array successfully captured real-time internal flow behaviors. Additionally, an integrated analysis using the LMTD and Wilson Plot methods allowed for the precise separation of the internal convective heat transfer coefficient (hi), yielding a correlation of hi=3370*V^0.8 that aligns well with theoretical predictions. To overcome the flow disturbances caused by existing invasive sensors and the limitations of surface thermography, the proposed FBG system provides a scalable and minimally invasive solution for the real-time structural health monitoring and design optimization of next-generation heat exchangers. Furthermore, it presents a new paradigm of 'thermo-hydraulic integrity monitoring' to ensure both optimal heat transfer efficiency and structural safety.

In recent years, composite materials have assumed a crucial role in advanced engineering sectors such as automotive, aerospace, and wind energy, owing to their outstanding specific mechanical properties. However, composite components subjected to cyclic loads experience a progressive degradation of their mechanical performance due to the accumulation and interaction of multiple damage mechanisms, including off-axis cracking, delamination, and fibre breakage. Consequently, continuous monitoring of the structural integrity of such components is essential in order to enable timely intervention when the functional requirements defined at the design stage are no longer satisfied. In the case of electrically conductive laminates, Structural Health Monitoring (SHM) can be effectively achieved through electrical methods, as damage phenomena induce detectable changes in electrical conductivity. This work presents an analytical framework capable of accurately describing damage evolution and the associated stiffness degradation in multidirectional conductive laminates affected by off-axis cracks and delamination in multiple layers, using the increase in electrical resistance as input parameter. The accuracy of the proposed framework is first assessed through comparison with finite element analyses, demonstrating excellent agreement. Furthermore, fatigue tests are conducted on CNT-modified quasi-isotropic GFRP laminates, with continuous monitoring of damage evolution, stiffness degradation, and electrical resistance variation. The experimental results closely match the analytical predictions, thereby confirming the accuracy of the framework and demonstrating its effectiveness as a reliable tool for the Structural Health Monitoring of composite materials.

The development of an accessible electronic circuit for analyzing Structural Health Monitoring (SHM) algorithms using piezoelectric elements is a relevant research topic in current structural health monitoring projects [1]. This is because the development of SHM requires harmony between its mathematical methods and low-cost technological design [1]. Moreover, removing the analog-to-digital converter (ADC) in SHM systems can address key technical hurdles, particularly in reducing overall power consumption for practical applications, among other important issues [1]. In this presentation, we introduce a novel electronic circuit for SHM that employs analog reference signals, building upon the suggestion in [1]. We also integrated an electrically manipulable boost DC-DC converter into our electronic system to manage the main 12V voltage source. This voltage management was possible because our DC-DC converter is a controllable power source that utilizes a hysteresis loop circuit via a microcontroller device of 8-bits from Microchip manufacturer [2]. See Figure 1. A photograph of the experimental setup is also provided. Our experimental setup comprises three aluminum bars representing different structural states: Case A (healthy), Case B (moderate damage via a single perforation), and Case C (severe damage via two perforations). Three piezoelectric transducers (PZTs) mounted on each bar base serve as actuators; these are connected in parallel and driven by a single power exciter. Three additional PZTs are positioned at each bar top to serve as receiving elements. Figure 1 also displays the experimental residual signal, which was acquired via a powerful digital oscilloscope (Picoscope technology) and displayed on a computer. This device was configured to ensure the residual signal was clearly visible. For each measurement scenario, the boost converter was deactivated to allow for the establishment of a new sensor connection point (the sensor selector switch on Figure 1) before being restarted. The resulting residual signal clearly shows that a stable average voltage can effectively distinguish among each testing case (A, B or C case). From a SHM data processing perspective, our closed-loop design relies on analog circuitry, with the exception of a PIC microcontroller. This microcontroller functions as the primary interface among analog control signals and the gate drive of the boost DC-DC converter. The converter requires a PWM (Pulse Width Modulation) signal to regulate its operation. The PIC microcontroller is configured to generate a PWM signal with a nominal frequency of about 15 kHz. Consequently, our system processes signals in the analog domain, eliminating the sampling rate requirements typically imposed by digital-to-analog conversion. Additionally, each piezoelectric actuators is driven by an analog signal ranging from 10V to 60V (these levels adjustable via the P₁ and P₂ trimmers) with a 30 Hz harmonic content, a direct result of the NE555N timer’s operational frequency. Therefore, our SHM approach, which employs analog signals for damage detection, aligns with established procedures in residual signal analysis [3].

References

[1] Owen, R., et al. (2011). (No. NASA/CR-2011-217153).

[2] Acho, L. (2025). Cybernetics and Physics, 14(4), 315-319.

[3] Vidal, Y., et al. (2012). Mechanical Systems and Signal Processing, 29, 447-456.

To identify the cracking mechanisms that occur when trucks and cars pass over a structure, this study introduces a new approach to analyzing acoustic emission (AE) signals from a prestressed concrete viaduct. This method utilizes the information contained within the narrow partial power (PP) bands of the acquired AE signals. These bands have proven to be extremely sensitive to changes in concrete deterioration modes in both field tests and laboratory experiments. The results demonstrate the ability to classify trucks based on the cracking information contained within the narrow PP bands of the AE signals acquired when trucks pass by. Additionally, laboratory bending tests with AE monitoring were conducted on reinforced concrete (RC) T-beams. The narrow PP strips' effectiveness in distinguishing between modes of degradation in the beams was remarkable. The proposed method uniquely offers the advantage of visualizing the frequency composition of a large number of AE signals using a heat map based on narrow PP bands. This study involves simultaneously measuring deformation and AE signals as trucks and cars pass by. The correlation between these two types of data is also presented. This research demonstrates that frequency information from AE signals can be used to monitor prestressed and reinforced concrete structures.

Traditional structural health monitoring (SHM) systems relying on conventional piezoelectric transducers (PZTs), such as phased arrays, often face significant hurdles due to hardware complexity, high power consumption, and prohibitive integration costs. To address these challenges, this study explores the development of a Steerable Acoustic Transducer (SAT) designed with inherent directional capabilities for generating and sensing elastic waves.

Unlike omnidirectional sensors, the SAT leverages a frequency-dependent spatial filtering effect. The device’s response is governed by the intersection of the medium’s dispersion relation and the wavenumber representation of the transducer’s electrode geometry (Fig. 1). By strategically distributing electrode material, a unique relationship is established between the signal frequency and the direction of wave propagation. This allows for precise steering of guided waves by simply tuning the frequency, drastically simplifying the necessary excitation hardware.

While initial studies have demonstrated the potential of SATs in isotropic structures, alternative technologies for their development have received limited attention. This proposal investigates the feasibility of SATs for advanced SHM applications. Specifically, a novel manufacturing approach utilizing high-powered, pulsed laser beam is proposed to achieve the required electrode precision. This method is demonstrated and verified through experimental wavefield characterization.

Ensuring the structural integrity of fiber-reinforced polymer (FRP) composites throughout their entire lifecycle—from manufacturing to in-service operation—remains a major challenge for Structural Health Monitoring (SHM). This issue is critical because the inability to reliably detect and monitor damage in composite structures throughout their lifecycle can compromise structural safety, reduce operational reliability, and lead to increased maintenance and lifecycle costs. Existing approaches typically address this issue in a fragmented manner, with distinct solutions for curing monitoring (e.g., calorimetry), post-manufacturing non-destructive testing (e.g., ultrasonics, X-ray imaging), and in-service SHM (e.g., strain gauges, optical fibers). This separation limits the continuity of information and prevents a comprehensive understanding of defect initiation and evolution.

To overcome these limitations, this work presents ComposIE, a patented non-destructive testing technology based on electrical impedance measurements, developed at Institut Clément Ader (Toulouse).

The proposed method enables the inline monitoring of composite structures throughout their lifecycle, including during curing, after manufacturing and during operational use. It relies on measuring the impedance of the bulk of the material with a low-cost impedance sensor, dedicated instrumentation and data processing methods.

The core innovation of ComposIE lies in its ability to provide a unified monitoring approach across these three critical stages at competitive cost. By tracking variations in electrical impedance signatures, the technology enables the detection and investigation of both progressive and sudden structural defects, such as delamination, cracking, and out-of-plane fiber failures. Unlike conventional techniques, the solution is compact, cost-effective, and minimally intrusive, making it compatible with industrial constraints and integration requirements.

Initial experimental investigations conducted on aeronautical grade stratified carbon fiber-reinforced polymer specimens demonstrate the capability of the technology to monitor draping and curing processes. The results highlight a high sensitivity to structural changes and good repeatability of measurements, supporting the relevance of impedance-based monitoring for continuous health assessment.

The technology is currently undergoing a maturation phase supported by Toulouse Tech Transfer, the Technology Transfer Office for the western Occitanie region in France, with the objective of preparing its industrial deployment. Ongoing work focuses on extending the approach to additional composite materials, developing alternative instrumentation to facilitate integration into manufacturing processes, and enhancing data interpretation through dedicated software tools. These developments aim to improve the reliability, scalability, and industrial compatibility of the solution.

ComposIE is particularly relevant for sectors where composite reliability is critical, such as wind energy, aerospace, marine, and transportation. By enabling continuous monitoring from manufacturing to operation, the technology supports improved process control, predictive maintenance strategies, and reduced inspection costs.

In this context, the project is actively exploring industrial partnerships to accelerate its transfer to market. Two valorization pathways are considered: the creation of a dedicated startup to develop and commercialize the technology, and/or licensing agreements with established industrial players seeking to integrate advanced SHM capabilities into their products or processes.

Concrete structures often develop distributed micro-cracks long before visible damage appears under mechanical loading conditions. These micro-cracks are generally initiated within the cement matrix and weak interfacial transition zones (ITZs), and progressively evolve in a distributed manner throughout the material. Detecting such early-stage distributed damage remains difficult for conventional ultrasonic testing because direct-wave measurements show limited sensitivity in strongly scattering heterogeneous concrete media. In contrast, diffuse ultrasonic coda waves undergo multiple scattering interactions throughout the concrete volume, allowing small perturbations caused by evolving micro-cracks to accumulate over long propagation paths. Due to this behaviour, coda-wave analysis has strong potential for early-stage damage monitoring in concrete structures. However, most conventional coda wave interferometry (CWI) approaches require a pristine baseline signal acquired from the undamaged state of the structure, which is often unavailable in practical structural health monitoring applications.

To address this limitation, the present study experimentally investigates a reference-free nonlinear coda wave interferometry (NCWI) framework for monitoring progressive damage evolution in concrete cubes subjected to uniaxial compressive loading. The proposed method uses amplitude-dependent nonlinear ultrasonic excitation, where increasing excitation energy enhances nonlinear crack-wave interactions that become more detectable within the multiply scattered diffuse coda-wave region. During different loading stages, ultrasonic measurements were performed using piezoelectric transducers under multiple excitation amplitudes while maintaining stable transducer positioning to minimize artificial waveform variations. The recorded waveforms were analysed using coda-wave decorrelation analysis within the late diffuse wave regime to evaluate progressive damage evolution. Within the selected excitation range, higher excitation amplitudes enhanced the sensitivity of nonlinear crack-wave interactions, resulting in increased cross-voltage coda-wave decorrelation in the diffuse wave regime. The proposed approach showed clear sensitivity toward micro-crack initiation and contact acoustic nonlinearity during progressive loading. Compared with conventional waveform analysis, the diffuse coda-wave response exhibited earlier waveform perturbations associated with microstructural deterioration. The observed nonlinear behaviour was further supported through acoustic emission activity measured during loading. Overall, the study demonstrates the potential of reference-free nonlinear diffuse coda-wave analysis for practical structural health monitoring of concrete structures without requiring pristine baseline measurements.

Lamb-wave total focusing method (TFM) imaging is widely used for non-destructive testing (NDT) of plate-like structures because of its long-range inspection capability, high spatial coverage, and compatibility with phased-array or scanning-based measurements. However, its localization performance remains constrained by diffraction and is further complicated by the multi-modal and dispersive nature of Lamb waves. In particular, different modes possess different wavelengths, group velocities, and defect sensitivities, making it difficult to quantitatively interpret how modal content influences practical imaging resolution.

This communication presents a Cramér-Rao lower bound (CRLB)-based statistical framework for interpreting diffraction-related localization uncertainty in classical envelope-based Lamb-wave TFM imaging. The objective is not to redefine the diffraction limit as a universal physical bound, but to provide a model-conditioned benchmark that links finite aperture point-spread-function (PSF) broadening with estimator performance under a specified envelope-domain measurement model.

The proposed formulation starts from a group-delay TFM imaging model, in which the received mode-separated Lamb-wave signals are represented by their envelopes. The Fisher information matrix (FIM) is then derived with respect to the unknown defect location. The analysis shows that the available localization information is governed by the spatial delay-gradient structure of the array. This is the same physical mechanism that controls diffraction-related focusing sharpness in TFM. Consequently, the CRLB follows the same principal wavelength-aperture trend as diffraction-limited PSF broadening, providing a statistical interpretation of localization uncertainty in envelope-based guided-wave imaging. The framework is further extended to multi-mode Lamb-wave measurements. Theoretical results indicate that modal combinations with shorter effective wavelengths and stronger temporal compactness provide increased Fisher information and reduced localization uncertainty. In particular, the antisymmetric mode combination A0 + A1 is predicted to outperform single-mode S0 or A0 imaging. Hilbert-envelope analysis supports this interpretation

by showing that A0+A1 produces a narrower effective wave packet, corresponding to higher temporal bandwidth and improved arrival-time sensitivity. Finite-element (FE) simulations and scanning Laser Doppler Vibrometry (sLDV) experiments are conducted on aluminum plates to validate the theoretical prediction. Closely spaced defects are imaged using S0, A0, and A0 +A1 modal configurations. Both simulation and experimental results show that S0 produces the broadest focal response, A0 improves localization due to its shorter wavelength, and A0 + A1 provides the most concentrated focal spot and the clearest defect separability. These observations are consistent with the CRLB predicted reduction in localization uncertainty. Overall, this work provides a physically interpretable CRLB-based benchmark for evaluating multi-mode Lamb-wave TFM imaging. The proposed framework clarifies how wavelength, aperture, envelope compactness, and modal content jointly affect localization performance, and may support future development of mode-aware and super-resolution guided wave imaging methods.