Structural Health Research Articles

Wind turbine blade damage detection based on acoustic signals

In recent years, the size of wind turbine blades has increased, underscoring the critical importance of monitoring their structural health. This study explores the use of noise emitted during wind turbine operation for the assessment of blade structural integrity. During sound acquisition, the wind sound, pneumatic sound and mechanical sound are recorded together to form the wind turbine sound signal. Considering the computational challenges of spectral subtraction under extreme noise intensities, a pretrained sound source separation neural network was used to distinguish between random wind noise and mechanical noise in wind turbine sound signals. In this paper, the short-time Fourier transform (STFT) time-frequency diagrams of signals processed using the spectral subtraction method are compared with those processed by combining the source separation model and spectral subtraction. The results reveal that the combined approach provides a more detailed representation in the time-frequency diagrams. Additionally, the mel-scale frequency cepstral coefficients (MFCCs) algorithm is utilized for feature extraction in the experimental dataset, forming training and test sets for the normal and abnormal datasets. To carry out damage detection, the ResNet50 deep residual neural network model is employed. The training results of the same network model were evaluated using the datasets obtained from the four different denoising schemes in the experiments and a 95% confidence level assessment metric. The analysis of the 95% confidence intervals reveals that the proposed sound source separation model combined with the traditional spectral subtraction denoising algorithm is effective in reducing the noise of wind turbine sound signals and performs well in identifying the anomalous sound generated by blade damage. Under this approach, the 95% confidence intervals of the model training set accuracies range from 0.926 to 0.965, while the confidence intervals of the test set accuracies range from 0.869 to 0.931.

Crack detection in concrete structures using standard deviation of discrete wavelet transform

Crack detection in concrete structures is an important issue in the maintenance and repair operations of the structures. Detecting and distinguishing cracks in concrete can help determine the structure's health and prevent the possibility of structural failure. An efficient method for separating cracks in concrete is to use wavelet transform. This paper proposes a new crack detection technique based on a two-dimensional discrete wavelet transform and the standard deviation obtained from its detail signals to select an optimum wavelet function. According to our findings, there is a significant relation between the statistical index standard deviation and the desired detail signal obtained from the two-dimensional discrete wavelet transform for selecting the optimum wavelet function. Specifically, results show that as the standard deviation of the matrix of detail signal increases by a given wavelet function, crack detection resolution increases by that wavelet function.

Smart Nanocoating‐ an Innovative Solution to Create Intelligent Functionality on Surface

AbstractThe interaction of substrate surfaces with the environment (moisture, heat, pressure, chemicals) has cascade effects on their efficiency, performance, durability due to their susceptibility towards microbial adhesion, spontaneous leaching, impairment, corrosion etc. Many scientific, technological innovations such as smart coating have been discovered for the protection of these surfaces. It is a high‐tech, high value coating capable of responding to environmental impulses. Incorporation of nanofillers, nanomatrices in smart coating improve surface performance by extending service life, provide high durability, high physical coverage, adhesive strength etc. Real‐time monitoring of environmental conditions, structural health could be made possible by sensing coatings. This review predominantly highlights the essential features, design, advancements, applications of smart nanocoatings over elementary nanocoatings. To cover the whole study, drawbacks of traditional coating methods are also discussed. Finally, the research gaps, future perspectives of smart nanocoatings will be overlooked.

Multiple damage detection in sandwich composite structures with lattice core using regression-based machine learning techniques

The abstract discusses the importance of Structural Health Monitoring (SHM) in ensuring the reliability and health of structures. It introduces a novel approach for recognizing cracks and delamination in sandwich composite structures using advanced methods for data analysis and machine learning algorithms. The research leverages artificial intelligence and machine learning to accurately identify and locate damage such as delamination and cracks in composite sandwich layers, which can significantly enhance troubleshooting performance, improve detection accuracy, and reduce the time and cost associated with repairs. The article presents a damage detection technique utilizing regression analysis for sandwich composite structures with a lattice core, capable of identifying and locating multiple cracks and delamination, as well as simultaneous defects in the structure. The analysis is conducted based on the sandwich composite structure’s healthy and damaged conditions in Abaqus software, and acceleration responses under random forces obtained through the finite element method were used to train various machine learning models, including regression algorithms like k-Nearest Neighborhood Regression (KNN), Light Gradient Boost Machine (LGBM), and Decision Tree Regression (DTR) to detect the damage location. The results indicate that the regression (LGBM), k-Nearest neighborhood, and decision tree regression (DTR) were the most successful functions, respectively, and the damage classification models accurately identified the damage and its location in the composite structure, achieving an accuracy rate of approximately 98.8%.

Enhancing bridge damage detection with Mamba-Enhanced HRNet for semantic segmentation.

With the acceleration of urbanization, bridges, as crucial infrastructure, their structural health and stability are paramount to public safety. This paper proposes Mamba-Enhanced HRNet for bridge damage detection. Mamba-Enhanced HRNet integrates the advantages of HRNet's multi-resolution parallel design and VMamba's visual state space model. By replacing the residual convolutional blocks in HRNet with a combination of VSS blocks and convolution, this model enhances the network's capability to capture global contextual information while maintaining computational efficiency. This work builds an extensive dataset with multiple damage kinds and uses Mean Intersection over Union (Mean IoU) as the assessment metric to assess the performance of Mamba-Enhanced HRNet. Experimental results demonstrate that Mamba-Enhanced HRNet achieves significant performance improvements in bridge damage semantic segmentation tasks, with Mean IoU scores of 0.963, outperforming several other semantic segmentation models.

Similarity assessment of structures for population-based structural health monitoring via graph kernels

In population-based structural health monitoring, the aim is to make inferences about the health of structures using information from a population of other structures. It is possible to use transfer learning here, as long as the structures that are used for transfer behave similarly to each other. As a result, assessing the similarity of structures and the data collected from those structures is necessary for successful transfer. In this paper, ideas from kernel and graph theories are used to assess whether the constructional makeup of two engineering structures – a bridge and a wind turbine, for example – are similar or not. To the human brain, this notion may seem trivial because the intended use, construction and behaviours of these structures are vastly different. However, for a computer, automatically measuring these dissimilarities requires a whole host of information. In this paper, the aim is to use irreducible-element models and attributed graphs to represent engineering structures, and to use graph kernels to measure the similarity of these models. The proposed methods are able to compare discrete and continuous attributes of structures in polynomial time. Similarity assessments are provided for a group of toy structures as well as a case study of seven real operational bridges. The latter population is important in dealing with a class of highly complex real-world examples of civil infrastructure; the analysis also allows a discussion on which aspects of bridge construction might be responsible for structural similarity or dissimilarity.

Optical micro/nano fiber enabled accelerometer

Abstract Highly sensitive and miniaturized accelerometers play an important role in structural health, earthquakes, and body motion monitoring. Herein, an optical micro/nanofiber (MNF) enabled accelerometer is proposed. The structural parameters of the MNF accelerometer are optimized based on theoretical simulation. The accelerometer shows excellent linear relationship between displacement and force, and achieves a minimum detectable acceleration of 0.15 m/s2. For vibration sensing, the natural frequency of the sensor can be as high as 753 Hz.

A Multi-Sensing IoT System for MiC Module Monitoring during Logistics and Operation Phases.

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module's structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module's structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module's structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module's structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations.

Urban Dark Fiber Distributed Acoustic Sensing for Bridge Monitoring

Distributed Acoustic Sensing (DAS) technology applied to telecommunication fiber optic networks offers new possibilities for Structural Health Monitoring (SHM). The dynamic responses of five bridges are extracted along a 24 km long optical fiber crossing the Lyon metropolitan area in France. From their characteristic seismic signatures, three physical parameters informing on the health of structures have been determined: vibration frequencies, damping and modal shapes. The fiber measurements are in agreement with velocimetric data serving as reference. The telecom optical fiber records the dynamic response of bridges in several directions and thus allows the reconstruction of 3D deformation modes using their orthogonality properties. Time tracking of frequencies, commonly used to assess structural integrity, shows that the average values of natural frequencies vary cyclically between day and night. The increase in frequencies during the night does not exceed 2% and probably reflects an overall stiffening of the structures due to the drop in temperature. The telecom fiber allows to obtain deformation and damping identity of structures, highlighting soil-structure coupling between the bridge and underlying soil. This study shows that it is possible to assess the spatial and temporal variability of bridge dynamic response from DAS data using existing fiber networks.

Expanding Irreducible Element models: beams and composites

Population-based Structural Health Monitoring's (PBSHM) aspiration is to accumulate a deeper knowledge of a structure's health, by harvesting available data across a range of similar structures (or substructures). Whilst each structure is represented via a unified language called an Irreducible Element (IE) model, the comparison and similarity metrics are computed within a graph space. As such, each model is subsequently converted into an Attributed Graph (AG) before being placed into the PBSHM Framework's comparison graph space; the network. This paper looks into the application of using the PBSHM Framework's comparison graph space as a complex network and determining if the theories of communities and community structures from network science, can be applied to the PBSHM Framework's network, to determine similarity and define yet unknown populations.

Semi-analytical simulation of ultrasound wave propagation in large plate-like structures

The ultrasonic guided wave (UGW) has gained considerable popularity among NDE methods for damage detection and structural health monitoring (SHM) due to its high frequency and low attenuation. Lamb waves are reflected or diffracted by structure boundaries, discontinuities, and damages. It is possible to determine a structure's health, defects, and locations based on its UGW propagation characteristics. In this study, we propose an efficient modeling method for UGW propagation through damaged and scattering plates by combining the Modal Pencil method with Wave Finite Element (WFE) and Hybrid FE/WFE methods and verifying the results with a high-precision elastic finite element model (FEM). With this approach, the transducer-excited ultrasonic field can be modeled, as well as its steady-state and transient propagation, scattering, and reflections at defects and boundaries. Long-range propagation configurations can be efficiently addressed by this method because its computation efficiency is independent of propagation path lengths. As part of the case study, we study the propagation of Lamb waves through a joint metal thin-walled structure with embedded damage. In the case study, different sensor locations are used to validate the received UGW signals quantitatively. Our findings will contribute to the development of a digital twin for monitoring structural health by providing an efficient and robust approach to modeling ultrasound propagation in damaged and large plate-like structures.

Incorporating specialist engineering knowledge into IE models to facilitate enhanced SHM-based transfer learning within PBSHM

Population-based Structural Health Monitoring (PBSHM) aims to gather additional knowledge on a structure's health by monitoring multiple structures (the population), compared to the level of knowledge available when utilising only a single structure's data. Before effective transfer learning can occur, a similarity between the structures must be established to prevent negative transfer. Irreducible Element (IE) models, combined with graph theory, are the vehicle used within PBSHM to facilitate this process. Recent research has introduced the Canonical Form (CF) which facilitates a singular IE model per structure; however, detailed IE models solely rely on predefined rules to reduce down to the CF, and do not require the incorporation of specialist engineering knowledge within these reductions. In particular, the specific SHM problem of interest may constrain the reduction. This work aims to enhance the capabilities of IE models by allowing authors to incorporate their specialist engineering knowledge into an IE model. A case study of a steel truss pedestrian bridge is presented, focusing on incorporating the engineering knowledge utilised in its design, construction, and maintenance within an IE model. By enabling authors to leverage their domain expertise, a more comprehensive understanding of structural similarities can be achieved, thereby enhancing transfer learning potential in PBSHM. This paper outlines the methodology and provides insights into the application of domain knowledge in IE models, showcasing its potential benefits for the field.

Monitorization of crack propagation in welded aluminium assemblies

Utilizing welded assemblies of aluminum profiles presents an effective means of reducing mass in rolling stock. However, these lightweight and optimally stiffened structures might be susceptible to unexpected failure modes that were not accounted for during the design phase. This could be due to insufficient information about in-service conditions or uncertainties in the material and manufacturing processes. This study aims to assess the feasibility of monitoring welded aluminum assemblies in real-time under both static and cyclic loading conditions to evaluate their structural health. Mechanical tests were conducted on welded aluminum profile assemblies while employing piezoelectric wafer active sensors (PWAS) for continuous monitoring. The PWAS facilitated ultrasonic active and passive inspections during testing. Passive sensing involved utilizing the acoustic emission technique to locate the source of acoustic signals associated with damaging mechanisms like crack nucleation and propagation. Upon identifying a detrimental event, on-demand active inspections using Ultrasonic Guided Waves were deployed to examine the structure and gather crucial information about its current condition based on analyzing temporal signal features. Although our mechanical tests were conducted in a laboratory environment, we recorded some temperature variations. Hence, we compensated for environmental influences through a data fusion model. Additionally, repetitivity analyses were conducted to confirm the reliability of the proposed inspection technique. Encouragingly, results demonstrated consistency and reliability in this proposed method.

Acoustic Emission in Ceramic Matrix Composites

Abstract The integration of ceramic matrix composites (CMCs) into safety-critical applications, such as turbine engines and aerospace structures, necessitates a sound understanding of their expected damage evolution under in-service conditions and real-time health-monitoring methods to assess their damage state. The measurement of acoustic emissions (AEs), the transient elastic waves emitted during damage formation, offers an enhanced capability for evaluating damage evolution and structural health in CMCs due to its high sensitivity, accurate temporal resolution, and relative ease of use compared to other nondestructive evaluation (NDE) techniques. Recent advances in numerical simulation methods and data-driven model development, in combination with improved multimodal experimental characterization methods and sensor hardware, are rapidly advancing AE to a mature technique for damage quantification. This review discusses the fundamental principles of acoustic emissions, provides practical guidelines on their experimental characterization and analysis, and offers perspectives on the current state-of-the-art.

A shallow 2D-CNN network for crack detection in concrete structures

PurposeThis study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.Design/methodology/approachThis paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.FindingsResults show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.Originality/valueThis paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.

Aircraft Structural Design and Life-Cycle Assessment through Digital Twins

Numerical modeling tools are essential in aircraft structural design, yet they face challenges in accurately reflecting real-world behavior due to factors like material properties scatter and manufacturing-induced deviations. This article addresses the potential impact of digital twins on overcoming these limitations and enhancing model reliability through advanced updating techniques based on machine learning. Digital twins, which are virtual replicas of physical systems, offer a promising solution by integrating sensor data, operational inputs, and historical records. Machine learning techniques enable the calibration and validation of models, combining experimental inputs with simulations through continuous updating processes that refine digital twins, improving their accuracy in predicting structural behavior and performance throughout an aircraft’s life cycle. These refined models enable real-time monitoring and precise damage assessment, supporting decision making in diverse contexts. By integrating sensor data and updating techniques, digital twins contribute to improved design and maintenance operations by providing valuable insights into structural health, safety, and reliability. Ultimately, this approach leads to more efficient and safer aviation operations, demonstrating the potential of digital twins to revolutionize aircraft structural analysis and design. This article explores various advancements and methodologies applicable to structural assessment, leveraging machine learning tools. These include the utilization of physics-informed neural networks, which enable the handling of diverse uncertainties. Such approaches empower a more informed and adaptive strategy, contributing to the assurance of structural integrity and safety in aircraft structures throughout their operational life.

Acoustic tunnel lining cavity detection using cepstral coefficients with optimized filter bank

Tunnels are an essential component of modern transportation infrastructure, and their structural health is critical to traffic safety, which can be seriously affected by tunnel lining cavities. In this paper, an acoustic-based detection approach for assessing the integrity of tunnel linings is studied. By tapping the tunnel lining surface, acoustic signals are sampled and analyzed using a novel feature parameter extraction algorithm-the energy-frequency cepstral coefficient, which uses wavelet packet decomposition to obtain energy distribution statistics in the frequency domain of the signal, and constructs a signal-dependent filter bank to achieve the cepstral coefficient extraction. Compared with the traditional Mel filter bank, this method can adaptively adjust the resolution of the filter bank according to the frequency characteristics of the classified samples. This allows for higher frequency resolution in regions where the energy distribution is concentrated. As a result, the extracted feature parameters achieve both dimensional compression and superior information retention. Experimental results show that the proposed energy-frequency cepstral coefficient feature outperforms the traditional Mel-frequency cepstral coefficient feature, resulting in a higher accuracy of tunnel lining detection. The convolutional neural network model achieves an accuracy of 99.2%, with a 78.9% reduction in error rate compared with the traditional Mel-frequency cepstral coefficient feature parameters. Additionally, a particle swarm optimization support vector machine model is trained to achieve an accuracy rate of 99.6% and an error rate reduction of 76.5%.

Image enhancement-based detection of concrete cracks under turbid water bodies

ABSTRACT Underwater concrete structure crack detection and structural health condition assessment based on image processing is challenging. The complex underwater environment and severe image degradation seriously affect the accuracy of crack detection. To solve these problems, a monocular vision and image-enhanced fractal-based fractal science based on computer vision and image processing techniques are proposed to conduct a non-contact detection study of underwater concrete cracks. This study established a four-level structural health condition to assist in underwater crack measurement and safety assessment. The box-counting method was used as a practical tool to calculate the fractal dimension. Three distances of 0.5, 0.8, and 1.2 m were set to verify the effective distance of the algorithm. The results show that the method proposed in this study can effectively detect cracks in submerged concrete members within 0.6 m and help managers correctly determine the structure's health using the fractal dimension.

The infrasonic choir: Decoding songs to inform decisions

The world around us is continuously evolving due to the actions of Mother Nature and anthropogenic activities, which impacts the decisions made on how we interact with the environment. Many of these changes are observable by listening to the infrasonic choir around us everyday. The voices of the infrasound choir include a wide variety of both natural (e.g., surf and volcanic activity) and man-made ( e.g., explosions and infrastructure) sources. Understanding how to exploit the infrasonic song to make sense of the environment in near-real-time is the focus of ongoing research. An example of this is listening to infrastructure (bridges and dams) for insights into structural health, which can be utilized to prioritize limited resources after a natural disaster (hurricane or earthquake). This presentation explores operationalizing this capability, such as how to monitor source rich urban spaces, re-envisioning sensing in these environments with the development of lightweight/low-cost sensors and mobile arrays, and developing detection, classification, and localization methods for near-real-time processing. When successful, continuous monitoring of the infrasonic choir can provide a method for understanding the environment to inform decisions. Permission to publish was granted by the Director, Geotechnical and Structures Laboratory, U.S. Army Engineer Research and Development Center.

A Machine Learning Model for Prediction of Marine Icing

Abstract Marine icing due to freezing sea spray has been attributed to many safety incidences. Prediction of sea spray icing is necessary for operational safety, design optimization, and structural health. In general, lack of detailed full-scale measurements due to the complexity and costs make validation difficult. The next best option is that of controlled laboratory experiments. The current study is the first study in the field of sea spray icing that investigates the use of new data science technologies like machine learning and feature engineering for the prediction of sea spray icing based on data collected from controlled laboratory experiments. A new prediction model dubbed “Spice” is proposed. Spice is designed “bottom-up” from experimentally collected data, and thus, if the input variables are accurately known, it could be said to be highly accurate within the tested range compared to existing theoretical models. Results from the current study show promising trends; however, more experiments are suggested for increasing the range of confident predictions and reducing the skewness of the training data. Results from spice are compared with five existing models and give icing rates in various conditions in the middle of the spectrum of the other models. It is discussed how validation from two existing full-scale icing measurements from literature proves to be challenging, and more detailed measurements are suggested for the purpose of validation.