Structural Health Monitoring Applications Research Articles

Towards reliability assessment of ultra-wideband microwave leakage monitoring in detecting stringer debondings in composite airframes

Ultra-wideband guided electromagnetic waves have been proposed recently for non-destructive testing and structural health monitoring applications. Among the multitude of possible microwave waveguides, recent works explored the concept of microwave leakage monitoring to detect stringer debondings in hollow carbon composite structures. The principle is to monitor the wave propagation characteristics within the cavity formed by the stringer and the airframe skin using transmission measurements. Changes in the transmission path, e.g. occurring from microwave leakage in the debonded region, can be assessed through a damage indicator approach. To proof the reliability of this concept, an experimental campaign was carried out according to the building block approach typically used in aeronautics. First, a small-scale specimen with a debonded stringer was investigated in the laboratory to proof the methodology. Then, a large-scale fuselage segment was used for technology demonstration enabling stringer debonding with increasing severity through static and fatigue loading. The paper shows that the microwave characteristics in the waveguide (stringer tunnel) is affected by the debonding conditions according to its severity and depth. In addition, extending the same approach on a number of test cases, the probability of detection was carried out showing the minimum defect size which can be identify reliably. This allows designing a reliable structural health monitoring system based on guided electromagnetic waves trapped in the tunnel.

Cumulative second harmonic Lamb wave generation and propagation in composite laminates: an insight into the influence of material anisotropy and damping

Significant strides have been taken in the structural design of fibrous composites in recent decades. Early detection of material degradation and damage in such materials is crucial for various industrial applications, yet poses challenges. Second harmonic Lamb waves have demonstrated their superior sensitivity to microstructural defects, making them an effective tool for structural health monitoring (SHM). However, understanding of the second harmonic Lamb wave generation and propagation in composite laminates remains inadequate due to the complexities introduced by material anisotropy and damping, which impede the practicality of SHM applications. This study investigates the cumulative second harmonic Lamb waves in composite laminates at low frequencies, considering both material anisotropy and damping. Theoretical analyses were carried out to explore how anisotropy and damping affect the generation and propagation of second harmonic Lamb waves. A nonlinear material constitutive model was proposed. It was used to conduct numerical simulations that verified the properties of second harmonic Lamb wave generation and propagation at different wave propagation angles, with a focus on the cumulative effect. Experiments were carried out to further validate the properties of the second harmonic Lamb waves in a carbon fiber reinforced panel. The findings indicate that the Lamb waves propagating at 0° showcase exceptional cumulative effect, rendering them suitable for SHM applications. Additionally, material damping induces a cumulative second harmonic Lamb wave with an initial increase followed by a decrease, creating a predictable “sweet” zone of larger amplitude that is easy to measure for SHM applications. The elucidation of the generation and propagation characteristics of the second harmonic Lamb waves provides guidance for structural damage detection and monitoring applications.

Impact of cryogenic environment on piezoelectric transducers used in SHM applications

A high number of actors of the space industry, including ESA (European Space Agency) and CNES (French national space agency), are engaged in a program aiming at developing future European reusable space launchers. In this context, PYTHEAS Technology has developed an embedded Non-Destructive Testing (eNDT) solution to ensure that strategic structural elements of the launcher are free of damage before a new launch, performing Structural Health Monitoring (SHM). The sensors used for this application are subject to severe temperature variations, including cryogenic temperatures. Cryogenic conditions are present in other fields requiring SHM solutions, such as liquid hydrogen reservoirs, increasing the importance of this study. Results of experimental tests are presented, where piezoelectric transducers are glued with two types of glue on samples of aluminum plates and carbon composite plates. A reference measurement is carried out before plunging the plates in a climatic chamber, down to -196°C. Pulse-echo and admittance measurements are performed during the temperature decrease and after the temperature rise. The admittance measurements show no deterioration of the samples after being immersed in liquid nitrogen: the transducers, wires, glue, and samples themselves have resisted to the temperature cycle. The pulse-echo signals’ time and frequency analysis show a linear attenuation of the signals relative to temperature. A difference in signal intensity and wave behavior has also been observed relative to the sample’s material: composite or aluminum. No difference regarding the type of glue is witnessed. These results pave the way for damage detection in harsh environments, since the system’s performance is maintained through a whole temperature cycle in cryogenic conditions.

Particle Filter for temperature compensation of FBG strain sensors for event segmentation

Structural Health Monitoring (SHM) plays a crucial role in assessing the integrity and performance of structures. Fibre-Bragg-Grating (FBG) technology has emerged as a valuable tool for strain measurements in SHM applications. However, FBG strain sensors are susceptible to temperature variations that can confound accurate strain measurements, potentially leading to errors in fatigue damage calculations and event detection. This research addresses the challenge of temperature compensation in FBG-based SHM setups. Instead of the traditional approach of placing temperature sensors near every strain sensor, which can escalate costs and complexity with a growing number of sensors, we propose a filter-based alternative. The proposed approach leverages the slower strain changes induced by temperature effects compared to those caused by external loads, e.g. a passing train. The key innovation lies in a filtering method that isolates temperature-induced strain variations as filtered data while treating load-induced strain as process noise. We explore the application of state-based filters such as the Kalman Filter (KF) and Particle Filter (PF). While the KF is limited to normally distributed noise, which does not represent load-induced strain effectively, the PF can handle various, adjustable noise distributions, that can be tailored to specific requirements. The research showcases the PF's ability for temperature compensation on multiple structures, such as a pedestrian bridge equipped with only 8 temperature sensors for 82 strain sensors loaded either in tension or compression. This novel approach enables more reliable strain measurements of events for detection and analysis in SHM systems.

A multivocal literature review of digital twins, architectures, and elements in civil engineering

Recent structural health monitoring (SHM) strategies in civil engineering increasingly leverage digital twins, which digitally represent the structures being monitored as well as the SHM systems installed on the structures. Despite the widespread adoption of digital twin applications in recent years in SHM, there is a lack of agreement on a common definition of digital twins. Furthermore, there is no consensus on digital twin architectures and on the internal elements that constitute digital twins. A common digital twin definition would advance digital twin implementations and operability, and insights into digital twin architectures and digital twin elements would be vital to enhance the reliability and performance of digital-twin-based SHM systems. A significant number of digital twin definitions have been proposed and a plethora of reviews have been published; however, little emphasis has been given to digital twin architectures and internal elements. This paper presents a multivocal review of digital twins in civil engineering, aiming to provide a panorama of the digital twin landscape in civil engineering with explicit insights into the architectures and internal elements used in digital twin applications. From a methodological standpoint, the review follows a twofold approach that encompasses (i) peer-reviewed, indexed literature (“white literature”) as well as (ii) non-indexed sources (“gray literature”) that include industrial digital twin applications. Besides the multivocal review, a generic digital twin reference architecture is drawn from the review results and a digital twin definition is formulated both in an informal and formal (i.e., mathematical) manner. It is expected that the generic reference architecture and the definitions proposed in this study may serve as a blueprint for digital-twin-based SHM applications, with significant implications for researchers, practitioners, and policymakers in structural health monitoring.

Sensitivity analysis of model parameters in a nonlinear model-updating approach for prestressed concrete bridges

Automated monitoring systems in structural health monitoring (SHM) using finite element models can measure system responses of the structure and provide information about behavior and damage scenarios. To describe the reactions and damages of the physical structure, the parameters of the FE-model have to be adjusted accordingly. A common approach therefore is model-updating, an iterative modification of unknown parameters of a finite element model by adapting the response of the model to the measurement results of the physical bridge. The adapted model can be used for assessments and predictions of the structure and provides accurate structural properties to a digital twin. The proposed monitoring concept includes damage detection, based on nonlinear model-updating using Artificial Intelligence (AI) methods and is applied to a prestressed, reinforced concrete bridge based on the observation of static response. For a practical application in long-term SHM, system properties and load values are identified simultaneously by using optimization methods based on Evolutionary Algorithms (EA). Due to the high dimensionality and complexity of the optimization results, Principal Component Analysis (PCA) including autoencoder networks for dimensionality reduction are required to evaluate and visualize the identified structural properties for their reliability. Based on physical 3D nonlinear finite element sensitivity analysis, the influence of damage scenarios and load situations on the bearing behavior of the bridge will be analyzed. The generated data and the results are presented and serve as the basis for the reliability analysis and evaluating the proposed concept. Furthermore, by comparing the measurements of the parameterized investigated models, conclusions about the behavior of the structure and the sensor placement can be obtained.

A Framework for Condition Survey and Structural Health Monitoring of Masonry Arch Bridges

Masonry arch bridges are essential elements of the road and railway networks of Europe, as they pertain to cultural heritage and play an important role in transportation. The integrity and safety of these centuries-old structures must be ensured. Therefore, there is great interest in understanding the behavior of masonry arch bridges and monitoring their health. Efficient methods for assessing the condition of structures, including inspections and monitoring, constitute a fundamental element of structural health monitoring (SHM) applications. Given the multitude of surveying techniques available, it becomes crucial to establish cost-effective sensor placement strategies capable of detecting structural failures. These strategies can involve identifying particularly sensitive areas, optimizing the number of sensors used, as well as refining the number and duration of monitoring setups, among other considerations. As part of the PONT3 project, which aims to develop a comprehensive and cost-effective approach to anticipate failure propagation in ageing bridges, this research focusses on developing interoperability-driven inspection and monitoring strategies to provide data that will allow anticipating the evolution of possible failure scenarios. In this regard, an extensive summary of the key characteristics of inspection and monitoring technologies, and typical system configurations in existing literature for masonry arch bridges will be provided. Then, the measurable physical parameters of interest for a specific failure propagation scenario will be identified using numerical simulations and applicable monitoring techniques for each of the physical parameters will be determined. Monitoring requirements in terms of measurement locations, accuracy, range, and temporal resolution will also be identified. The recommendations for interoperability-driven data collection to exchange and employ acquired information for the purpose of structural diagnosis will conclude the framework. The study will finally discuss a future outlook on the development of integrated SHM-oriented digital twins.

Structural Health Monitoring on Road Bridge: Application Cases of Optical Strand Sensor in JAPAN

In Japan, many of the major civil infrastructures such as road bridges, railways and tunnels was built during our high-growth period from the mid-1950s to the early 1970s. The number of these structures that has been more than 50 years old after construction, has been rapidly increasing in recent years. In this context, expectations for Structural Health Monitoring (SHM) have been rising to optimize its management and maintenance to extend its lifespan and ensure the safety of users. Although a variety of IoT sensors are developing and their precision is improving day by day, SHM still has crucial challenges, e.g., if it can really identify critical damages to structures, determine quantitative assessment of the serviceability limit, evaluate the safety of a structure quickly after major events such as earthquakes, or so on. In order to achieve our original goals, it is essential to accumulate practical applications of SHM by dedicating a specific sensor solution to a specific issue. The authors have introduced a unique and competitive Optical Strand Technology developed by OSMOS Groupe in France and have been providing the service of SHM mainly on civil infrastructures for more than 20 years in Japan. This paper presents some application cases of SHM to road bridges by means of Optical Strand Sensor which provide for different types of purposes and future prospects.

Elastodynamic shape derivative: focus on the sampling of a circular distribution

The detection and quantification of defects plays a crucial role in several industries. Assessing the severity enables to schedule maintenance appropriately, optimizing costs and reducing risks. Guided elastic waves are particularly suitable for Structural Health Monitoring applications on thin structures thanks to their long range and high sensitivity. Guided wave tomography, based on an acoustic propagation hypothesis, has been studied over the last decade to quantitatively image defects in waveguides. The acoustic approximation of this family of methods allows fast computation and good estimation of defects for simple geometries such as plates or pipes. However, this hypothesis induces that a single guided mode is considered, without mode conversion, which limits its use to defects larger than two wavelengths in structures close to ideal waveguides. Hence the need of new imaging algorithms for smaller defects in less ideal structures such as pipes with welded supports. We present here an adaptation of the shape derivative method to ultrasounds in order to reconstruct surface defect on structures with complex geometries. The physical model used is the full elastodynamic problem. It allows taking into account all physical phenomena occuring during wave propagation. Iterative methods such as shape derivative are well known to give precise results but at a high computational cost as the method needs to solve the full elastodynamic problem at each iteration, and a sensitivity to local minima, which may prevent convergence toward the true defect. To suppress these two drawbacks, a spectral finite element method is used to reduce the computation and memory costs. The result of acoustic guided wave tomography is also used as a first guess to reduce the number of iterations and the sensitivity to local minima. After explaining the principle of the shape derivative, validations on simulated and experimental data will be presented in this talk.

A piezoelectric vibration sensor with excellent performance based on Bi12SiO20 crystal for high-temperature applications

High-temperature piezoelectric vibration sensors play a crucial role in the accurate monitoring of the dynamic mechanical conditions in aerospace, automotive, and energy generation systems. However, the use of conventional piezoelectric materials in high-temperature environments is restricted owing to their limited Curie temperatures. In this study, we grew a piezoelectric crystal Bi12SiO20 (BSO) with the crystal cut optimized for high longitudinal piezoelectric coefficient and low piezoelectric crosstalk behaviors. Subsequently, a compression-type piezoelectric vibration sensor utilizing the BSO bulk crystal was developed and fabricated for structural health monitoring under high temperatures. The impact of pre-tightening torques on the sensor performance was investigated. Moreover, the sensor performance was analyzed under temperatures up to 650 °C. The BSO-based sensor exhibited an average sensitivity of ∼3.89 pC/g between 25 and 650 °C under 160 Hz frequency, with a variation of 5.5%. Additionally, the BSO-based sensor demonstrated ultra-stable sensitivity at 600 °C, highlighting its strong sensing capabilities and reliability under high temperatures. Thus, the BSO-based vibration sensor is a promising option for structural health monitoring applications under high temperatures.

Prediction of directivity characteristics of piezoelectric macro-fiber composite interdigital transducers: a semi-analytical approach

The employment of guided waves for structural health monitoring applications has attracted substantial attention in recent years. Piezoelectric transducers are frequently employed in these systems for elastic wave generation and acquisition. Their dynamic properties, i.e., direction-dependent frequency characteristics, are an important feature for designing a reliable monitoring strategy. Although analysis of the transducer directivity pattern can be performed using finite element models, these usually tend to be computationally very expensive, especially, for parametric and optimization studies. In this paper, a semi-analytical methodology is proposed that can predict the far-field responses of complex transducers of arbitrary shape and structure mounted on elastic plates. The approach reported here couples analytical far-field predictions with a local FE model evaluating the traction field generated by the transducer. Thus, the FE model captures the near-field responses, while the analytical part predicts the far-field responses. The implementation of this semi-analytical method requires development of analytical and numerical frameworks. The former includes computation of dispersion curves of the inspected plate, the stress and displacement profiles of wave modes and the excitability curves, while the latter includes estimation of the traction field between the transducer and the plate using the FE model. Owing to the popularity of piezoelectric macro-fiber composite (MFC) interdigital transducers, in this paper, we apply the proposed method to simulate the directivity patterns of the MFC IDTs of different configurations. To validate the reliability of the proposed method the simulated patterns are compared with experimental results obtained using Laser Doppler Vibrometer (LDV).

Validation of an Abaqus impact wave propagation model

Structural Health Monitoring (SHM) is an important topic in aircraft industry to reduce the maintenance costs and increase the availability of an aircraft fleet through automating the determination of the health of the aircraft structure and systems using on-site sensors, like piezoelectric (PZT) and optic fibre Bragg grating (FBG) sensors that are often applied in SHM sensor networks. A finite element model for wave propagation due to an impact is very desirable to enable the design of optimal sensor network configurations for SHM applications, to obtain a better understanding of wave propagation (especially in composite structures) and also to generate simulated (virtual) test data for SHM algorithm development. Therefore, an Abaqus explicit finite element (FE) model was developed for wave propagation due to impacts. To validate such a model, a series of impact tests were performed with a drop tower on an aluminium clamped square panel, at different impact energy levels and different impact locations. On the panel, eight FBG and six PZT sensors were installed. These experimental results were used to validate the FE model, in which the impacts were simulated and the computed PZT and FBG sensor responses were compared against the experimental results. The paper will present the impact test setup, the finite element model and the validation results. The main focus of the paper will be the sensitivity analysis, identifying the model parameters that significantly affect the impact sensor responses and their optimal settings. A very good correlation between the numerical and experimental results was obtained. In near future work, the model will be extended to a (thermoplastic) composite panel, for which experimental results on a flat composite panel already have been collected, and a stiffened composite panel representative of an actual horizontal tail plane are planned.

POD methodologies for SHM application in civil engineering

Structural Health Monitoring (SHM) technologies can significantly enhance safety through continuous monitoring. The successful deployment of real-time SHM systems rely mainly on reliability evaluations. While numerous standards exist for reliability assessments or Probability of Detection (POD) of NDT techniques, there are no specific standards guiding reliability procedures for SHM data. Consequently, NDT POD methods are often applied to SHM data, despite the time and statistically dependent nature of SHM data. Especially in the field of civil engineering, there is the need for a SHM specific POD approach able to perform a meaningful reliability evaluation. Hence, in the current work, different SHM POD models including random intercept model and length at detection (LaD) model are implemented for a vibration-based monitoring data relevant for civil engineering. Unlike the linear response observed in NDT inspection data concerning damage, SHM data can exhibit non-linear characteristics in certain cases. In those cases, an advanced LaD model suitable for non-linear data is also developed in the current study. In addition, a comparison between the results obtained using NDT POD models and SHM POD models is demonstrated using an open-source GUI (TU1402) based on FE model. Moreover, the results obtained by the application of modified LaD POD model to a real test bridge data is demonstrated. Keywords: SHM, reliability, POD, civil engineering.

Smart Clay Bricks enabling Diffuse Strain Monitoring in Masonry Buildings: Progresses at Full-scale

Strain monitoring in masonry buildings can provide useful information regarding the onset of structural damage during operating conditions. This is because when cracks develop on masonry components, i.e., clay bricks and mortar layers, induce discontinuities and redistributions in their original stress/strain configurations. Although the application of local strain measurements for Structural Health Monitoring (SHM) of masonry buildings can be effective in detecting anomalies and modifications in their structural response caused by crack development, achieving diffuse strain monitoring remains a challenge in practical SHM applications, nowadays. This is mainly because strain sensors currently available on the market present several practical limitations, including scalability issues, which often make their large-scale application to masonry buildings difficult. In recent years, for overcoming the drawbacks of traditional sensor systems, innovative smart clay bricks have been developed, able to measure deformation when incorporated into load-bearing masonry structures. In that sense, this paper presents the latest advancements and applications of this brand-new sensing technology for SHM of real-scale masonry buildings.

Fast Bayesian modal identification with known seismic excitations

AbstractFast and accurate identification of structural modal parameters after an earthquake is crucial for assessing structural conditions and facilitating repair. With the development of modern earthquake observation techniques, the recorded ground motion can be leveraged as extra input information for modal identification, enabling the experimental modal analysis applicable. This study develops a Bayesian modal identification algorithm that aims at estimating the most probable value (MPV) of modal parameters and their identification uncertainty. Incorporating the recorded seismic input, the algorithm utilizes with the structural equation of motion in the frequency domain to formulate the likelihood function and adopts a constrained Laplace method for Bayesian posterior approximation of modal parameters. With the aid of complex matrix calculus, an iterative scheme is developed, allowing a fast search of the MPV of modal parameters and an analytical evaluation of the posterior covariance matrix. The performance of the proposed algorithm is validated by examples with synthetic, laboratory and field data, respectively. In addition, its effectiveness on predicting structural responses under a future earthquake is illustrated, showing its potential for various downstream applications in seismic structural health monitoring.

Hypermedia-driven RESTful API for digital twins of the built environment

Core domains to establish digital twins of the built environment are Building Information Modeling (BIM), Geographic Information System (GIS) and Internet of Things (IoT). RESTful Application Programming Interfaces (APIs) of these domains allow easy access to the digital twin. While multiple information models are reasonable since the focus of these domains is distinct, interoperability becomes a challenge. Therefore, in this paper we propose a hypermedia-driven RESTful API for digital twins of the built environment. While the GIS and IoT interfaces are covered by established standards, we define a RESTful interface for Industry Foundation Classes (IFC) models of the BIM domain. The APIs are interconnected with a federated approach though hypermedia navigation links. An evaluation by two use cases – a planning model and a structural health monitoring application – shows that the API can be used to navigate through the models crossing seamlessly the scope of the domains and overcoming barriers of interoperability.

Impact damage characterization approach for CFRP pipes via self-sensing

The growing usage of carbon-fiber-reinforced polymers (CFRPs) has necessitated the development of structural health monitoring (SHM) for ensuring their integrity and safe operation. This paper presents a novel technique for monitoring the structural health of 3D CFRP pipe structures subjected to multiple impacts based on real-time electrical resistance monitoring. Damage characteristics such as its location and type were identified using the monitoring data and machine learning techniques. Additionally, finite element analysis was deployed to rationalize the electromechanical behaviors of pipes with varying stacking types and thus different fracture mechanisms. Analysis of the electrical resistance data coincided with the results of the finite element analysis in terms of damage modes and types. Therefore, a simple real-time electrical resistance measurement can detect the damage type without numerical analysis. A damaged section was identified by comparing the intensity of the data. Through machine learning techniques, the timepoints of damage severity demarcation were accurately predicted within error of 10 s and accuracies over 96 %. The machine-learning tools applied are unsupervised, thus reducing the effects of data dependency, labeling, and imbalance, as well as ensuring flexibility and general applications. The proposed technique was able to detect the damage location, severity, and type in real-time; hence, it has various potential applications in structural health monitoring including CFRP materials and is one of the few methods used to assess 3D structure characterization using self-sensing, which can result in mature technology and the optimal operation of CFRP systems.

Conformational interlocking induced construction of hierarchically porous graphene membrane for ultrahigh-pressure sensing

Based on microstructure engineering, graphene oxide (GO)-based piezoresistive pressure sensors have attracted increasing attention due to its amazing sensitivities, but the point-to-point contact mechanism among nanosheets compromises their high-pressure monitoring capabilities. Inspired by the multiple conformations of GO nanosheets, we utilize π-π conjugated interlayer interactions to lock the 3D crumpled conformation of nanosheets, resulting in controllable assembly into a flexible graphene membrane with hierarchically porous structures (PGM). These designed structures include microscale pores formed by the loose stacking of 3D crumpled nanosheets and nanoscale pores supported by densely packed crumples, reaching up to 100 nm in height. The PGM flexible sensor exhibits an unprecedented ultrahigh-pressure response of up to 2000 kPa, with high sensitivities of 1.1 and 0.7 kPa−1 in the pressure ranges of 1–600 kPa and 600–2000 kPa, respectively, and demonstrates over 10,000 cycles of high-pressure stability. Moreover, the PGM flexible sensors enable real-time monitoring of pressure vessel inflation/deflation processes and human activities such as finger, elbow, and knee bending, as well as walking, running, and jumping. This hierarchically porous structure provides a promising avenue for ultra-wide-range and ultra-high-pressure sensing devices, with potential applications in structural health monitoring, personal healthcare, and medical diagnostics.

COMPARATIVE ANALYSIS OF WIRED AND WIRELESS SENSORS IN STRUCTURAL HEALTH MONITORING

Structural Health Monitoring (SHM) systems play a critical role in ensuring the safety and longevity of civil engineering infrastructure. This paper presents a comparative analysis of wired and wireless sensor technologies within SHM applications, drawing insights from scholarly literature, expert interviews, and case studies. The significance of SHM in safeguarding infrastructure durability is emphasised, considering the substantial investment and long service lifespans associated with civil engineering structures. Thematic analysis was utilized and revealed key performance criteria such as reliability, flexibility, environmental adaptability, cost-effectiveness, and maintenance requirements. Wired sensors are lauded for their reliability and accuracy, particularly in critical infrastructure projects, while wireless sensors offer greater flexibility and ease of deployment, especially in remote monitoring scenarios. Environmental adaptability remains crucial for both sensor types, with fiber optic sensors demonstrating effectiveness in harsh conditions. Expert interviews further enrich the understanding of sensor performance, highlighting opportunities for advancements in wireless sensor technology and data analytics. The paper concludes with recommendations for future research and development efforts to address existing constraints and meet the evolving needs of SHM in civil infrastructure monitoring. Overall, this comparative analysis provides valuable insights for guiding advancements in sensor technology and SHM procedures to ensure the continued safety and integrity of essential infrastructure.

Visual Strain Sensors Based on Fabry-Perot Structures for Structural Integrity Monitoring.

Strain sensors that can rapidly and efficiently detect strain distribution and magnitude are crucial for structural health monitoring and human-computer interactions. However, traditional electrical and optical strain sensors make access to structural health information challenging because data conversion is required, and they have intricate, delicate designs. Drawing inspiration from the moisture-responsive coloration of beetle wing sheaths, we propose using Ecoflex as a flexible substrate. This substrate is coated with a Fabry-Perot (F-P) optical structure, comprising a "reflective layer/stretchable interference cavity/reflective layer", creating a dynamic color-changing visual strain sensor. Upon the application of external stress, the flexible interference chamber of the sensor stretches and contracts, prompting a blue-shift in the structural reflection curve and displaying varying colors that correlate with the applied strain. The innovative flexible sensor can be attached to complex-shaped components, enabling the visual detection of structural integrity. This biomimetic visual strain sensor holds significant promise for real-time structural health monitoring applications.