Structural Damage Detection Research Articles

Imaging of Structural Timber Based on In Situ Radar and Ultrasonic Wave Measurements: A Review of the State-of-the-Art.

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced.

Optimization of structural damage identification method based on finite element method and Bayesian updating

The application of Bayesian updates to the finite element method for structural damage detection represents an avant-garde approach in the industry. Its merit lies in astutely transforming the influence of uncertainty into a forward problem. Nonetheless, the selection of simulation experiments and loading measurement schemes still relies to a certain extent on exhaustive methods. In this paper, based on the finite element method and Bayesian method, the OpenSees software platform is used to compare the actual experimental values with a large number of simulation experiments. The sensitivity of loading and measuring schemes is clarified, and the significance of simulation experiments in finding efficient schemes and reducing detection costs is verified. Concurrently, taking a truss structure as a case study, the combination of structural optimization and simulation experiment is deeply studied. The conclusion of this paper can provide a way to improve the level of simulation experiments and save computing resources.

Effect of Signal Features and Model Variables on Energy-Traced Arrival Time Picking of Acoustic Signals Used for Structural Damage Detection

To monitor damage developments in structures, various structural health monitoring methods based on different principles are used. The common aspect of elastic wave-based methods is to place appropriate sensors on the structure, to detect acoustic wave propagation and to analyze these signals the sensors transformed. The arrival time of these recorded signals to the sensors is the most significant parameter used to determine critical information such as the time and location of the damage. Therefore, the accurate calculation of the arrival time affects the accuracy of the damage detection. In this study, effects of the signal-to-noise ratio (SNR), sampling frequency, length of the signal, and length of the focal window on determining the arrival time of the signals to the sensors were investigated. For this purpose, an energy-traced arrival time picking approach (Akaike Information Criterion, AIC), which is the frequently used method in the literature, has been applied to a typical acoustic signal originated from a concrete cracking. The results of the study suggest the necessity of noise elimination, the optimum level of data logging and the ratios of focal window lengths for accurate time of arrival detection in the field monitoring of the structures using acoustic methods.

Response of a novel all-solid-state sodium-based-electrolyte battery to quasi-static and dynamic stimuli

In response to growing environmental and economic concerns, developing new technologies prioritising safety, sustainability, and reliability has become imperative. In the energy sector, batteries play an increasingly significant role in applications such as powering electronic devices and vehicles. In this context, lithium-ion batteries have raised environmental concerns, driving the exploration of alternative technologies. Sodium-based batteries have emerged as an attractive option due to their environmental and economic advantages, as well as their potential for multi-functional applications. This study investigates a novel battery developed by a research team at the University of Porto, with a specific focus on its strain-sensing capabilities for potential applications in damage detection of structures. The battery under investigation is a novel all-solid-state design, comprised of a sodium-ion ferroelectric electrolyte and zinc and copper as the negative and positive electrodes, respectively. A series of quasi-static and dynamic tests are conducted to qualitatively assess the piezoelectric behaviour of the battery. The consistent findings show that the battery generates a difference in the electric potential in response to mechanical stimuli, thus confirming its piezoelectric nature. Furthermore, the results demonstrate the battery can accurately detect the operating frequencies of a shaker, despite encountering inherent electromagnetic interference noise from the electrical grid during testing. These promising outcomes highlight the substantial potential of this emerging technology for a wide range of applications, including but not limited to structural health monitoring systems. Given its novelty, this technology presents multi-functional capabilities for diverse practical future applications, such as energy harvesting that leads to self-powered structural health monitoring systems.

Multi-visual modality micro drone-based structural damage detection

Accurate detection and resilience of object detectors in structural damage detection are important in ensuring the continuous use of civil infrastructure. However, achieving robustness in object detectors remains a persistent challenge, impacting their ability to generalize effectively. This study proposes DetectorX, a robust framework for structural damage detection coupled with a micro drone. DetectorX addresses the challenges of object detector robustness by incorporating two innovative modules: a stem block and a spiral pooling technique. The stem block introduces a dynamic visual modality by leveraging the outputs of two Deep Convolutional Neural Network (DCNN) models. The framework employs the proposed event-based reward reinforcement learning to constrain the actions of a parent and child DCNN model leading to a reward. This results in the induction of two dynamic visual modalities alongside the Red, Green, and Blue (RGB) data. This enhancement significantly augments DetectorX's perception and adaptability in diverse environmental situations. Further, a spiral pooling technique, an online image augmentation method, strengthens the framework by increasing feature representations by concatenating spiraled and average/max pooled features. In three extensive experiments: (1) comparative and (2) robustness, which use the Pacific Earthquake Engineering Research Hub ImageNet dataset, and (3) field-experiment, DetectorX performed satisfactorily across varying metrics, including precision (0.88), recall (0.84), average precision (0.91), mean average precision (0.76), and mean average recall (0.73), compared to the competing detectors including You Only Look Once X-medium (YOLOX-m) and others. The study's findings indicate that DetectorX can provide satisfactory results and demonstrate resilience in challenging environments.

Neural networks-based damage detection for symmetrical structures considering modal feature similarity

Symmetric structures are commonly used in various fields. To ensure the safety of these structures, it is crucial to employ appropriate damage detection methods. Among the methods used for structural health detection, the combination of structural dynamics and neural networks based on finite element models has gained popularity due to its non-destructive nature. This study presents a neural network-based damage detection approach for symmetrical structures, taking into account the similarity of modal features. By considering the interference of modal feature similarity, the proposed method effectively detects damage in symmetrical structures. In this approach, the rates of mode shape change before and after damage are used as inputs to the neural network, as they are found to be less sensitive to modeling errors compared to the mode shapes themselves. The effectiveness and adaptability of the method are validated through a numerical case analysis of a symmetrical load-bearing base plate used in walkers for patients with spinal cord injuries.

Transformer‐based framework for accurate segmentation of high‐resolution images in structural health monitoring

AbstractHigh‐resolution image segmentation is essential in structural health monitoring (SHM), enabling accurate detection and quantification of structural components and damages. However, conventional convolutional neural network‐based segmentation methods face limitations in real‐world deployment, particularly when handling high‐resolution images producing low‐resolution outputs. This study introduces a novel framework named Refined‐Segment Anything Model (R‐SAM) to overcome such challenges. R‐SAM leverages the state‐of‐the‐art zero‐shot SAM to generate unlabeled segmentation masks, subsequently employing the DEtection Transformer model to label the instances. The key feature and contribution of the R‐SAM is its refinement module, which improves the accuracy of masks generated by SAM without the need for extensive data annotations and fine‐tuning. The effectiveness of the proposed framework was assessed through qualitative and quantitative analyses across diverse case studies, including multiclass segmentation, simultaneous segmentation and tracking, and 3D reconstruction. The results demonstrate that R‐SAM outperforms state‐of‐the‐art convolution neural network‐based segmentation models with a mean intersection‐over‐union of 97% and a mean boundary accuracy of 87%. In addition, achieving high coefficients of determination in target‐free tracking case studies highlights its versatility in addressing various challenges in SHM.

Machine Learning approaches to damage detection in composite structures combining experimental and simulation domains

Composite materials are widely used across major industries such as the automotive, aerospace and wind power, due to their excellent mechanical properties. A strong effort is thus put into developing innovative damage detection methodologies, for which Non-Destructive Testing (NDT) techniques can play a vital role as advanced measurement methods. One such technique is Laser Doppler Vibrometry which allows to accurately measure high-frequency vibration behavior with dense grids of points, without mass loading the structure. For data analysis, Machine Learning (ML) techniques have achieved high success on a number of structural applications, and can be leveraged to build automated and reliable damage classifiers. In this work, three methodologies have been developed by combining Laser Doppler Vibrometer (LDV) measurements with ML approaches, for the task of detecting damages on a carbon-fiber reinforced polymer (CFRP) plate. Each damage detection methodology requires pre- and post-processing steps, which were optimized with Bayesian Optimization. Principal Component Analysis (PCA) was also explored to reduce the dimensionality of the data, before classification. Moreover, making use of Finite Element Analysis (FEA), simulation data was generated with the ability of characterizing the high-frequency dynamic behavior of ply-based composite plates. The simulation data enriched the damage detection methodologies in a Transfer Learning (TL) framework. Results are presented for each damage detection methodology, alongside with a comparative overview of the advantages and disadvantages of each method.

Cross-correlation difference matrix based structural damage detection approach for building structures

Cross-correlation difference matrix based structural damage detection approach for building structures

Enhancing Damage Detection in 2D Concrete Plates: A Comprehensive Study on Interpolation Methods and Parameters

In an era marked by increasing demands for stability and durability in construction, the importance of damage detection in concrete structures cannot be overstated. As these structures underpin the safety and longevity of vital assets, this paper embarks on a comprehensive exploration of methodologies to enhance precision and reliability in 2D concrete plate damage detection. By focusing on the interpolation of damage index values and leveraging the insights gained from energy loss analysis and the characterization of the time of arrival of signals, we address the pressing need for improved non-destructive damage detection techniques. Our study encompasses a range of simulation attempts, each involving various interpolation parameters, and systematically evaluates their performance. The culmination of this research identifies the most effective combination of techniques and parameters, leading to the best results in damage detection. This multidimensional investigation promises to provide valuable contributions to the field of structural health monitoring, benefiting both researchers and practitioners engaged in the evaluation of concrete structures.

Pr3+-Doped Lithium Niobate and Sodium Niobate with Persistent Luminescence and Mechano-Luminescence Properties

In this research, a comprehensive series of Pr3+-doped lithium niobate and sodium niobate materials were obtained at different temperatures via solid-state sintering, and their structures and properties were compared. NaNbO3: 0.75% Pr3+ phosphors were synthesized by sintering at 1150 °C for 2 h and emitted red persistent luminescence for more than 1200 s, peaking at 612 nm under UV excitation, which was a typical long persistent luminescence phenomenon. Furthermore, the sample glowed when pressurized, and a red bright luminescence which lasted for several seconds was visible to the naked eye. This was a typical mechanical luminescence phenomenon of samples under mechanical stress, directly converting mechanical energy into light energy. It was determined that NaNbO3:Pr3+ and LiNbO3:Pr3+ both possess multimode luminescence. Owing to their red long persistent luminescence (LPL) and mechano-luminescence (ML) properties, Pr3+ phosphors can be employed in fields, such as display technologies, stress sensing, structural damage detection, and other complex applications.

Multi-task Framework for Vibration-Based Structural Damage Detection of Spatial Truss Structure Using Graph Learning

Multi-task Framework for Vibration-Based Structural Damage Detection of Spatial Truss Structure Using Graph Learning

Damage Classification of a Three-Story Aluminum Building Model by Convolutional Neural Networks and the Effect of Scarce Accelerometers

Structural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their propagation. Structural damages may arise from various factors, including natural phenomena and human activities. To address this, diverse applications have been developed to enable timely detection of such damages. Among these, vibration-based methods have received considerable attention in recent years. By leveraging advancements in computer processing capabilities, machine learning and deep learning algorithms have emerged as promising tools for enhancing the efficiency and accuracy of vibration-based SHM. This study focuses on the application of convolutional neural networks (CNNs) for the classification and detection of structural damage within a steel-aluminum building model. An experimental platform was devised and constructed to generate data representative of building damage scenarios induced by bolt loosening. Both the typical placement of sensors on each floor and the utilization of only one accelerometer were employed to understand the effect of scarcity of accelerometers. By subjecting the building model to controlled vibrations and environmental conditions, the response data from both sensor configurations were collected and analyzed to evaluate the effectiveness of the CNN approach in detecting structural damage under varying sensor deployment strategies. The findings demonstrate that the CNNs exhibited high accuracy in both damage classification and detection, even under scenarios with limited sensor coverage. Moreover, the proposed method proved effective in identifying structural damage within building structures.

Structural health monitoring on offshore jacket platforms using a novel ensemble deep learning model

Monitoring health condition of offshore jacket platforms is crucial to prevent unexpected structural damages, where a prevailing challenge involves translating available feature information into structural damage patterns. Although the artificial neural network (ANN) models are popular in addressing this challenge, they often fail to capture the temporal correlations between the feature information and the damage patterns, which reduce their capability for discovering the laws governing the structural damage detection. To bridge this research gap, this study proposes a novel ensemble deep learning model to enhance the temporal feature extraction to improve the damage pattern identification. In this approach, a one-dimensional Convolutional Neural Network (CNN) extracts the spatiotemporal features from the structural vibration measurements. Simultaneously, a SENet attention mechanism is introduced to select the most informatic features. Subsequently, a bidirectional long short-term memory network (BiLSTM) is employed to learn the mapping between the extracted features and the structural damage patterns. Furthermore, the particle swarm optimization (PSO) algorithm is used to optimize the BiLSTM hyperparameters to enhance its stability and reliability. Both simulations and experiments are carried out to collect the vibration responses of the offshore jacket structure in different damage scenarios. The analysis results demonstrate that the proposed method produces remarkable improvement with respect to the accuracy and robustness in identifying the structural damages when compared with the ANNs. The overall detection accuracy of the proposed CNN-BiLSTM-Attention ensemble model is beyond 95%, which provides strong applicability to practical structural health monitoring of offshore platforms.

A Two-step Inverse Method for Quantitative Damage Evaluation of Plate-like Structures Using Vibration Approach

This study presents a novel two-step approach to assess plate-like structural laminar damages, particularly for delamination damage detection of composite structures. Firstly, a 2-D continuous wavelet transform is employed to identify the damage location and sizes from vibration curvature data. An inverse method is subsequently then used to determine the bending stiffness reduction ratio along a specified direction, enabling the quantification of the delamination severity. The method employed in this study is an extension from the one-dimensional inverse method developed in a previous work of the authors. The applicability of the two-step inverse approach is demonstrated in a simulation analysis and by an experimental study on a cantilever composite plate containing a single delamination. The inverse method is shown to have the capacity to reveal the detailed damage information of delamination within a constrained searching space and can be used to determine the effective flexural stiffness of composite plate structures, even in cases of complex delamination damage. Conflict of Interest Statement The authors declare no conflicts of interest.

Identifying Damage in Structures: Definition of Thresholds to Minimize False Alarms in SHM Systems

In recent years, the development of quick and streamlined methods for the detection and localization of structural damage has been achieved by analysing key dynamic parameters before and after significant events or as a result of aging. Many Structural Health Monitoring (SHM) systems rely on the relationship between occurred damage and variations in eigenfrequencies. While it is acknowledged that damage can affect eigenfrequencies, the reverse is not necessarily true, particularly for minor frequency variations. Thus, reducing false positives is essential for the effectiveness of SHM systems. The aim of this paper is to identify scenarios where observed changes in eigenfrequencies are not caused by structural damage, but rather by non-stationary combinations of input and system response (e.g., wind effects, traffic vibrations), or by stochastic variations in mass, damping, and stiffness (e.g., environmental variations). To achieve this, statistical variations of thresholds were established to separate linear non-stationary behaviour from nonlinear structural behaviour. The Duffing oscillator was employed in this study to perform various nonlinear analyses via Monte Carlo simulations.

Damage detection of road domain waveform guardrail structure based on machine learning multi-module fusion.

The current highway waveform guardrail recognition technology has encountered problems with low segmentation accuracy and strong noise interference. Therefore, an improved U-net semantic segmentation model is proposed to improve the efficiency of road maintenance detection. The model training is guided by mixed expansion convolution and mixed loss function, while the presence of guardrail shedding is investigated by using partial mean values of gray values in ROI region based on segmentation results, while the first-order detail coefficients of wavelet transform are applied to detect guardrail defects and deformation. It has been determined that the Miou and Dice of the improved model are improved by 8.63% and 17.67%, respectively, over the traditional model, and that the method of detecting defects in the data is more accurate than 85%. As a result of efficient detection of highway waveform guardrail, the detection process is shortened and the effectiveness of the detection is improved later on during road maintenance.

Damage detection and location using a simulated annealing-artificial hummingbird algorithm with an improved objective function

Swarm intelligence algorithms and finite element model update technology are important issues in the field of structural damage detection. However, the complexity of engineering structural models normally leads to low computational efficiency and large detection errors in structural damage detection. To solve these problems, a simulated annealing-artificial hummingbird algorithm (SA-AHA) is proposed based on the artificial hummingbird algorithm (AHA). The Sobol sequence is used to improve the identification efficiency by optimizing the initial population distribution of the AHA. Then, the simulated annealing strategy is introduced to improve the detection accuracy by enhancing the global search ability of the AHA. In addition, a novel objective function is presented by combining modal flexibility residual, natural frequency residual, and trace sparse constraint of the structural model. Numerical simulations of a simply supported beam and a two-story rigid frame are carried out to verify the superiority of the proposed SA-AHA and the objective function. Simulation results demonstrate that the SA-AHA is better than the AHA in terms of damage computational efficiency and damage identification accuracy. Moreover, the new objective function can be more excellently applied to the SA-AHA than the previous one, which can be effectively used to locate and estimate the damage of the proposed SA-AHA in structure. Finally, experimental studies are carried out to verify the proposed method.

Distributed fiber optic sensors for structural health monitoring of composite pressure vessels

Abstract In this paper, we present a comprehensive overview of our research in the field of distributed fiber optic sensors for structural health monitoring of hydrogen composite pressure vessels. Specifically, we demonstrate how the integration of fiber optic sensors into composite pressure vessels enhances safety while simultaneously reducing maintenance costs. The small size of optical fibers enables their integration into composite structures during the manufacturing process, allowing continuous monitoring and precise detection and localization of structural damages during service life. We also discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. Our applications of fiber optic sensors demonstrate their potential to contribute significantly to the energy transition towards renewable sources.

Assessment of articular cartilage degradation in response to an impact injury using µMRI

ABSTRACT Purpose Degradation of articular cartilage (AC) due to injury to the knee joint may initiate post-traumatic osteoarthritis (PTOA). Failure to diagnose the onset of the disease at an early stage makes the cure ineffective for PTOA. This study investigated the consequences of a mechanical injury to the knee in a rabbit model using microscopic magnetic resonance imaging (µMRI) at high resolution. Materials and methods A mechanical injury was induced to the knee joints of 12 rabbits. Cartilage blocks were extracted from the non-impacted and impacted knee joints after 2 and 14 weeks post-impact. The specimens were studied using µMRI T2 relaxation and inductively coupled plasma analysis to determine the early degradation of the articular cartilage. Results The data established a connection between T2 relaxation time and the early progression of knee PTOA after an impact injury. T2 values were found to be higher in the impacted cartilage at both 2 and 14 weeks, in particular, T2–55° values in the impacted samples displayed a significant rise of 6.93% after 2 weeks and 20.02% after 14 weeks. Lower glycosaminoglycan measurement and higher water content in the impacted cartilage confirmed the µMRI results. Conclusions This µMRI T2 study was able to detect cartilage damage in the impacted knees. In addition, greater degradation in the affected knees at 14 weeks than at 2 weeks indicated the progressive nature of cartilage deterioration over time. The µMRI results were in accord with the biochemical analysis, indicating the detection of early structural damage in the cartilage.