Tool For Structural Health Monitoring Research Articles

A quadrilateral inverse plate element for real-time shape-sensing and structural health monitoring of thin plate structures

The inverse finite element method (iFEM) emerged as a powerful tool in shape-sensing and structural health monitoring (SHM) applications with distinct advantages over existing methodologies. In this study, a quadrilateral inverse-plate element is formulated via a sub-parametric approach using bi-linear and non-conforming cubic Hermite basis functions for engineering structures, which can be modeled as thin plates. Numerical validation involves dense and assumed sparse sensor arrangements for in-plane, out-of-plane, and mixed general loading conditions. iFEM analysis reveals efficient monotonic convergence to analytical and high-fidelity finite element reference solutions. After successful numerical validation, defect detection analysis is performed considering minute geometric discontinuities and structural stiffness reduction because of latent subsurface defects under tensile and transverse loading conditions. The inverse formulation successfully resolves the presence of simulated defects under a sparse sensor arrangement. The proposed inverse-plate element is accurate in the full-field reconstruction of shape-sensing profiles and reliable in defect identification and quantification in thin plate structures.

Investigation of the modal frequency of a long-span suspension bridge under multiple environmental conditions

This study employs advanced techniques, namely the improved multi-synchrosqueezing transform (IMSST) and the Fourier synchrosqueezed transform (FSST), to extract the instantaneous frequency of the Humber Bridge, in the UK, in operational conditions. First, a brief overview of the relevant theories behind the techniques used is provided. These techniques are then applied to track the instantaneous frequencies within the monitored vertical acceleration data of the bridge. The instantaneous frequencies extracted by both techniques are verified against each other. The investigation further encompasses the development of three distinct regression models: a simple regression model, an expanded regression model and a regression model based on principal component analysis (PCA). The efficacy of the PCA-based model is scrutinised by generating and testing datasets that mirror the monitored data, with the Kolmogorov-Smirnov (KS) test confirming their validity. The findings of this research indicate a statistically significant inverse relationship between modal frequency and acceleration intensity. Furthermore, by incorporating wind effect considerations into the regression analysis, the R-squared values in both the expanded and PCA-based models exhibit substantial improvements. This demonstrates the performance of the enhanced model in capturing the dynamics of the bridge. The robustness of the PCA-based model is further corroborated by its performance during testing, proving its potential as a reliable tool in structural health monitoring (SHM).

Eigenfrequency analysis using fiber optic sensors and low-cost accelerometers for structural damage detection

Structural Health Monitoring (SHM) is crucial for infrastructure safety and integrity. Arduino-based sensors are gaining popularity in low-cost SHM structures. Distributed fiber optic systems (DFOS), such as Distributed Acoustic Sensing (DAS), are employed for accurate SHM despite their high costs, computational demands, and energy consumption. The primary objectives of this work are to compare the accuracy of an accelerometer named LARA (Low-cost Adaptable Reliable Accelerometer (LARA)) that utilizes both Arduino and Raspberry Pi technologies with a DAS system in detecting structural damage and to explore the potential advantages of combining LARA and DAS to create an effective SHM tool. This study is the first to enhance the design of LARA. Subsequently, LARA and DAS were used in a laboratory setting to analyze eigenfrequency changes in a beam model with induced localized damage. Finally, this study evaluated the precision and reliability of LARA and its potential role as a trigger for DAS in detecting localized damage. The findings show that both LARA and DAS can identify changes in the eigenfrequencies of damaged structures with deviations as small as 3.68 %. Consequently, LARA demonstrated its potential as a trigger for DAS, significantly reducing the computational demands while enriching the analysis. This approach offers highly accurate eigenfrequency measurements and enhances the analytical capabilities of DAS by identifying the primary axes of the detected eigenfrequencies.

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.

Damage localization in composite structures based on Lamb wave and modular artificial neural network

Lamb wave-based technology for damage diagnosis in composite structures has emerged as a promising tool for structural health monitoring (SHM). However, traditional SHM faces challenges in multi-sensor localization, including complex data processing, high costs, and cumbersome maintenance management. The current trend is to employ data-driven approaches, but achieving precise damage localization across the entire structure with fewer sensors still presents difficulties. To address this issue, a damage localization method based on Lamb wave envelope characteristics and modular artificial neural network (M-ANN) is developed. In this approach, the envelope characteristics are employed to characterize the damage information and reduce the data dimension. Subsequently, the M-ANN model is innovatively designed with dropout layers in each hidden layer, significantly enhancing performance on random datasets. In the composite laminate experiment using only four sensors, the proposed method achieves an average relative error of 2.96 % for random damage samples, with 95 % falling within 6.17 %, outperforming other advanced damage localization approaches. Additionally, the method is utilized to investigate the requirements for the relative density of data acquisition under different damage localization needs in composite structures.

Damage discrimination on drone fuselage with OBR Based Fibre-Optic Networks

This paper presents a methodology for a structural health monitoring (SHM) system based on optical backscatter reflectometry (OBR) fibre optic networks. The study focuses on the use of machine learning methods to monitor the microdeformation of a drone fuselage and to detect and discriminate be-tween different types of damage when the structure is subjected to static loads. A total of 1500 points are measured for each load, providing a comprehensive understanding of the structural response. In addition, four types of damage, in-cluding partial disbonding at different locations, are induced with varying de-grees of extension to evaluate the sensitivity and accuracy of the SHM system. The results show that the OBR-based SHM system is capable of detecting and localising damage with high accuracy, indicating its potential as a reliable and effective tool for real-time structural health monitoring in aerospace applica-tions. The methodology presented in this study could be extended to other types of structures and damage scenarios, contributing to the development of more advanced and effective SHM systems. Keywords: Machine Learning, OBR sensor network, Unmanned Aerial Vehi-cle, Drone.

Guided Ultrasonic Wave Monitoring of Damage in Anisotropic Composites

Lightweight, carbon fibre reinforced composites are often employed for the manufacture of aerospace components due to their good strength to weight ratio. However, due to poor interlaminar strength, composite components are prone to barely visible impact damage, caused by low velocity impacts, during aircraft operation. Guided-wave-based structural health monitoring (SHM) techniques, using a network of distributed sensors, is an important Structural Health Monitoring (SHM) tool for the detection and localization of in-service impact damage in composite structures. However, the material anisotropy of composite laminates influences guided wave propagation and scattering. These anisotropic effects, if unaccounted for, could lead to inaccurate localization of damage, and potential regions of the structure where guided waves cannot propagate with sufficient amplitude, reducing damage sensitivity. Defect characterization can be improved by considering the scattering characteristics of various damage types for the sparse array signal processing. Guided wave scattering (A0 Lamb wave mode) was investigated around an artificial insert delamination in a quasi-isotropic CFRP panel. Permanent magnets, mounted on an undamaged region of the plate, were also used as scattering targets and compared to the delamination case. Full 3D Finite Element (FE) simulations were performed for both the delamination and magnet cases and compared to wavefield data obtained from non-contact laser Doppler vibrometer (LDV) measurements. Individual ply layers were modelled using unidirectional composite material properties to accurately capture the anisotropy effects. Good agreement was found between the experimental measurements and simulations. Scattered guided wave amplitudes around each damage type show strong directional dependency with energy focusing along the fiber directions of the outer ply layers of the laminate. Distinct scattering behavior was observed for each damage type. Implications of anisotropy and angular scattering on sparse array SHM of different defect types are discussed.

Combinatorial Quantification of Multi-Features of Coda Waves in Temperature-Affected Concrete Beams.

Coda waves are highly sensitive to changes in medium properties and can serve as a tool for structural health monitoring (SHM). However, high sensitivity also makes them susceptible to noise, leading to excessive dispersion of monitoring results. In this paper, a coda wave multi-feature extraction method is proposed, in which three parameters, the time shift, the time stretch, and the amplitude variation of the wave trains within the time window, are totally derived. These three parameters are each mapped to the temperature variations of concrete beams, and then combined together with their optimal weight coefficients to give a best-fitted temperature-multi-parameter relationship that has the smallest errors. Coda wave signals were collected from an ultrasonic experiment on concrete beams within an environmental temperature range of 14 °C~21 °C to verify the effectiveness of the proposed method. The results indicate that the combination of multi-features derived from coda wave signals to quantify the medium temperature is feasible. Compared to the relationship established by a single parameter, the goodness-of-fit is improved. During identification, the method effectively reduces the dispersion of identification errors and mitigates the impact of noise interference on structural state assessment. Both the identification accuracy and stability are improved by more than 50%, and the order of magnitude of the identification accuracy is improved from 1 °C to 0.1 °C.

Inverse finite element method with energy-based regularization for deformation reconstruction and structural health monitoring

Distributed Fiber Optical Sensing (DFOS) can be used to perform compact, low-invasive and large-scale strain measurements, providing an important tool for strain-based deformation reconstruction and Structural Health Monitoring (SHM). However, most current deformation reconstruction methods are difficult to apply to the sparse uniaxial strain measurement conditions associated with DFOS, especially for structures with complex geometries. This work introduces an energy-based regularization method, i.e., a priori assumption that the actual deformation state is close to the state with the minimum deformation energy under the premise of satisfying the strain constraints, into inverse finite element method to perform load-independently deformation reconstruction under the condition of sparse uniaxial strain measurement. The Inverse Finite Element Method with Energy-based Regularization (iFEM-EBR) can naturally construct the DFOS-based deformation reconstruction model with quasi-forward finite element analysis without introducing additional mathematical processing. The sensitivity of this method to structural characteristics, such as material parameters, makes it a unique prospect in SHM.

Fatigue damage reconstruction in glass/epoxy composites via thermal analysis and machine learning: A theoretical study

This study introduces an advanced, non-contact diagnostic tool for structural health monitoring of fatigue damage in fiber/polymer composite materials. The approach combines thermal image recognition of fatigue self-heating hotspots with high-fidelity thermal modeling to quantitatively assess subsurface fatigue damage distributions by machine learning. To this end, artificial thermal images are generated through 3D numerical thermal analysis of an inherent fatigue damage heat source within a glass/epoxy composite, derived from sampling a multivariate Gaussian distribution of microcracks. Subsequently, these synthetic thermal images are employed to train three distinct regression models: a convolutional neural network, a Gaussian processes regressor, and a straightforward least squares model. Various image augmentation techniques are applied to expand the dataset efficiently. All models accurately predict the size of the damage and – most importantly – the maximum temperature within the damage deep inside the composite. The regression methods estimate the diagonal elements of covariance matrix components of the Gaussian distribution, with accuracies ranging from 86% to 99%. The findings presented in this work contribute to establishing a solid foundation for non-destructive subsurface fatigue damage assessment in composite materials, with many practical applications in experimental composites fatigue research.

Automated Prediction of Crack Propagation Using H2O AutoML.

Crack propagation is a critical phenomenon in materials science and engineering, significantly impacting structural integrity, reliability, and safety across various applications. The accurate prediction of crack propagation behavior is paramount for ensuring the performance and durability of engineering components, as extensively explored in prior research. Nevertheless, there is a pressing demand for automated models capable of efficiently and precisely forecasting crack propagation. In this study, we address this need by developing a machine learning-based automated model using the powerful H2O library. This model aims to accurately predict crack propagation behavior in various materials by analyzing intricate crack patterns and delivering reliable predictions. To achieve this, we employed a comprehensive dataset derived from measured instances of crack propagation in Acrylonitrile Butadiene Styrene (ABS) specimens. Rigorous evaluation metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2) values, were applied to assess the model's predictive accuracy. Cross-validation techniques were utilized to ensure its robustness and generalizability across diverse datasets. Our results underscore the automated model's remarkable accuracy and reliability in predicting crack propagation. This study not only highlights the immense potential of the H2O library as a valuable tool for structural health monitoring but also advocates for the broader adoption of Automated Machine Learning (AutoML) solutions in engineering applications. In addition to presenting these findings, we define H2O as a powerful machine learning library and AutoML as Automated Machine Learning to ensure clarity and understanding for readers unfamiliar with these terms. This research not only demonstrates the significance of AutoML in future-proofing our approach to structural integrity and safety but also emphasizes the need for comprehensive reporting and understanding in scientific discourse.

A real-time quantitative acceleration monitoring method based on triboelectric nanogenerator for bridge cable vibration

Real-time monitoring of vibration acceleration of civil infrastructure is imperative for effective management and safe operation of structures. Although the triboelectric nanogenerator (TENG) shows potential for self-powered sensing, it faces challenges in correlating limited experimental electrical amplitudes to structural responses, thereby hindering comprehensive performance analysis of civil infrastructure. Herein, a self-driven acceleration TENG sensor (A-TENG) is designed and manufactured, comprising an outer shell and an inner mass-spring-damper system. By establishing a sensing model based on vibration theory and the TENG mechanism, the electrical signals generated by the sensors can be correlated with structural acceleration, enabling self-driven real-time quantitative characterization. Indoor and on-site experiments demonstrate that the A-TENG sensor is capable of continuously monitoring the acceleration profile with excellent consistency compared to commercial sensors. The non-contact free-standing sliding mode design and ultra-lightweight construction of the A-TENG sensor (∼8 g) enhance its start-up sensitivity (∼0.1 m/s2), long-term stability (∼30,000 loading cycles) while minimizing mass interference. The proposed sensing theory renders a novel approach to offering complete time-domain information, which is vital for the precise analysis of structural behavior. This work facilitates understanding of self-driven sensors utilizing TENG technology and provides a useful tool for long-term real-time quantitative structural health monitoring.

Detection of transverse cracks in steel beams using damage location coefficients and artificial neural networks

Structural health monitoring plays a crucial role in ensuring the integrity and safety of engineering structures such as steel beams. This research paper presents a comprehensive methodology for detecting transverse cracks in beams with a constant section and any boundary conditions. The proposed approach utilizes the normalized squared modal curvature of the beam, the damage severity, and the natural frequency of the undamaged beam. By analyzing the natural frequencies of both the undamaged and damaged states, Relative Frequency Shift (RFS) values are obtained. Subsequently, the Damage Location Coefficients (DLC) are calculated by normalizing the RFS values. These DLC values are then employed to establish a comprehensive database of known damage signatures, enabling the training of an artificial neural network (ANN) in MATLAB. The trained ANN can predict the locations of damages for new scenarios by utilizing DLC values obtained from measurements. To validate the effectiveness of the ANN, extensive simulations using Finite Element Method (FEM) and experimental measurements are conducted on a steel cantilever beam. The results demonstrate the ANN’s capability to accurately predict the locations of transverse cracks, showcasing its potential as a reliable tool for structural health monitoring of steel beams.

Long-range distributed vibration sensing using phase-sensitive forward optical transmission.

Long-range vibration sensing is an important tool for real-time structural health monitoring. A new, to the best of our knowledge, design of a distributed fiber-optic vibration sensor is introduced and experimentally demonstrated in this study. The proposed system utilizes the transmission of light in the forward direction for sensing, and a self-interference method for laser source simplification. To extract vibration information from phase modulation of light, two Mach-Zehnder interferometers (MZIs) are employed with a 3 × 3 coupler-based differential cross multiplication algorithm for phase calculation. A folded double-ended detection configuration allows the time-of-flight difference via cross correlation (CC) to provide vibration positioning. Experimental results demonstrate a sensing range of up to ∼80 km without optical amplification, accompanied by a position accuracy of 336 m.

Reinforced concrete surface cracks length detection and length estimation by using digital image processing approach

Surface cracks are a common failure that occurs in reinforced concrete structures (RC). With the help of new technologies, access to crack properties should be easier and help the inspector to provide better results. However, most inspectors still prefer manual visual inspection approach, which leads to inconsistent results when investigating this flaw. Moreover, cracks with inconsistent shapes and irregularities are a difficult task for crack extraction, and inspectors overlook the details of cracks. Therefore, in this study, crack detection and thin crack appearance enhancement using various digital image processing algorithms (DSP) were proposed to improve the accuracy of crack length estimation. By using certain DSP on the captured crack images on RC, several algorithms were created and coded in MATLAB via the morphological approach to produce good quality of the original crack images. At the end of this study, the appearance of the thin crack was improved and helped to improve the estimate of the total length of the crack in pixels. The maximum percentage error between the estimated crack length was calculated and compared to the actual length and was 7.10 %. The surface crack detection algorithm and the has the potential as a helpful structural health monitoring (SHM) tool for crack inspection.

Evaluating Structural Details' Influence on Elastic Wave Propagation for Composite Structures via Ray Tracing.

This study presents a novel method based on ray tracing for analyzing wave propagation in composites specifically tailored for structural health monitoring applications. This method offers distinct advantages over the commonly used finite element method mainly in computational resource utilization, which has become a limiting factor for these kinds of analyses. The ray tracing method is evaluated against a number of example cases representing structural details such as thickness changes, stringers, or simulated damage, and the significance of ray tracing to study wave propagation under these conditions and how it can serve as a valuable tool for structural health monitoring are highlighted. This model has been developed as part of a complete SHM framework with the intention of being an efficient and simple way to calculate wave propagation and therefore it could be used as a way to determine relevant damage indicators or train an artificial intelligence model.

Barely visible impact damage detection in composite structures using deep learning networks with varying complexities

Visual inspection is one of the most common non-destructive testing (NDT) methods that offers a fast evaluation of surface damage in aerospace composite structures. However, it is highly dependent on human-related factors and may not detect barely visible impact damage (BVID). In this research, low velocity impact tests with different energy levels are conducted on two groups of composite panels, namely ‘reference’ and ‘sensor-integrated’ samples. Then, the results of impact tests, together with C-scan and visual inspection images, are analysed to define the BVID range and create an original image dataset. Next, four different deep learning models are trained, validated and tested to capture the BVID only from the images of the impacted and non-impacted surfaces. The results show that all four networks can learn and detect BVID quite well, and the sensor-integrated samples reduce the training time and improve the accuracy of deep learning models. ResNet outperforms other networks with the highest accuracy of 96.2% and 98.36% on the back-face of reference and sensor-integrated samples, respectively. The proposed damage recognition method can act as a fast, inexpensive and accurate structural health monitoring tool for composite structures in real-life applications.

Bayesian incremental learning paradigm for online monitoring of dam behavior considering global uncertainty

Advanced data-driven methods for real-time monitoring of dam structural behavior, especially displacement, are obtaining increasing popularity. Most existing data-driven models are offline and static, however, which cannot be dynamically updated with the increase of monitoring data. Additionally, the capability of monitoring models to identify anomalies, which is the ultimate goal of dam behavior modeling, has received little attention. This study proposes a novel Extreme Learning Machine (ELM)-based Bayesian incremental learning methodology to address these challenges. The proposed model is termed Incremental Global Bayesian ELM (I-GBELM), which couples ELM with global Bayesian and incremental learning for online monitoring of dam displacements. Firstly, a global Bayesian approach to the ELM is presented to account for both input and target uncertainties and to infer the network parameters analytically. Secondly, an incremental learning paradigm is theoretically derived, which aims to directly learn new knowledge from additional data to update model parameters without retraining from scratch. The I-GBELM model integrates the strengths of both methods so that it allows accurately predicting displacements, identifying anomalies, and efficiently updating parameters. For online deployment purposes, finally, a non-iterative pruning algorithm is developed to adaptively discard the redundant hidden nodes, yielding a more compact model. The performance of the proposed model is fully validated through comprehensive verification cases using benchmark and real-world monitoring datasets. The empirical results indicate that the proposed I-GBELM outperforms all other models in most cases, and hence, is a competitive modeling tool for online structural health monitoring.

Bridge damage detection using precise vision-based displacement influence lines and weigh-in-motion devices: Experimental validation

This study presents an experimental validation for a high-precision vision-based Displacement Influence Line (DIL) measurement system for a purpose of damage detection on bridges. The vision-based DIL measurement system is a promising tool for structural health monitoring on real operation bridges, which combines two Computer Vision subsystems and weigh-in-motion (WIM) devices. Two vision systems are utilized for tracking vehicle position and measuring structural displacement, while the WIM device obtains vehicle weight information. To demonstrate the feasibility of such a vision-based DIL measurement system, this study developed a vision system using a Go-Pro camera for vehicle positioning and a consumer grade camera for displacement measurement, followed by a series of laboratory experiments on a simply supported bridge using vision-based DILs to assess damage existence and localisation. Five damage scenarios were created by restrengthening the test structure instead of damaging it. Each restrengthened structure was considered intact while the original structure was considered damaged. Vision-based DIL measurements were repeated 12 times for each damage scenario to observe uncertainties in damage localisation as well as DILs. As the measured DILs were found adversely affected by the friction on the boundary supports, the Chordwise Displacement Influence Line (cw-DIL) approach was proposed to compensate for this effect. Damage-induced cw-DILs were shown to be able to assess damage existence and localisation successfully and consistently for all five damage scenarios.

On Dispersion Compensation for GAW-Based Structural Health Monitoring.

Guided acoustic waves (GAW) have proven to be a useful tool for structural health monitoring (SHM). However, the dispersive nature of commonly used Lamb waves compromises the spatial resolution making it difficult to detect small or weakly reflective defects. Here we demonstrate an approach that can compensate for the dispersive effects, allowing advanced algorithms to be used with significantly higher signal-to-noise ratio and spatial resolution. In this paper, the sign coherence factor (SCF) extension of the total focusing method (TFM) algorithm is used. The effectiveness is examined by numerical simulation and experimentally demonstrated by detecting weakly reflective layers with a highly dispersive A0 mode on an aluminum plate, which are not detectable without compensating for the dispersion effects.