Field Of Structural Health Monitoring Research Articles

A novel fiber Bragg grating-based smart clamp with macro strain measurement: design, modeling, and application to incipient looseness detection

Ensuring the safety and optimal performance of hydraulic pipelines, particularly in aircraft, is of paramount importance. The challenge of detecting incipient looseness in fasteners within a time-varying temperature environment has garnered widespread recognition, making it a focal point in the field of structural health monitoring. In this study, we propose an innovative solution—a smart clamp based on Fiber Bragg Grating (FBG). This clamp aims to detect incipient looseness in hydraulic pipe systems by measuring its macro strain. Notable advantages include its ability to avoid the chirping of the FBG, the ease of building a distributed sensing network, especially for the blind-hole connected structure, and its applicability for monitoring clamps connected with small-diameter bolt. The analytical model of the clamp under the preload of the connected bolt, along with the unique condition for the measurement, is provided. Demonstrating a force resolution [Formula: see text], the clamp exhibits a strong capability in detecting incipient looseness. Furthermore, environmental temperature interference is mitigated by configuring two FBGs. Conducting application experiments in a hydraulic system, the results indicate that the proposed clamp can effectively detect the loosening and tightening processes in real time, even under the time-varying temperature environment.

A scientometric analysis of drone-based structural health monitoring and new technologies

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure’s health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research.

Frequency response function-based closed-form expression for multi-damage quantification and its application on shear buildings

Vibration-based damage identification techniques have gained popularity immensely in the field of structural health monitoring (SHM). Frequency response function (FRF)-based damage quantification is mainly performed based on iterative model updating techniques. Most of these methods are time-consuming and erroneous in higher modes. Spectral element method (SEM) can overcome some of these limitations and can determine FRF expression directly for a broad class of civil structures. However, a closed-form expression for multiple damage severity is not proposed to date. In this study, an FRF-based formulation for quantifying damage has been proposed with the help of SEM in order to bypass the mode shape extraction for damage identification. To determine the effectiveness of the proposed formulation both numerical and experimental studies have been performed. A numerical simulation has been performed on a 14-storey shear building. Damage has been introduced by reducing the storey stiffness at intermediate and adjacent storeys. It has been perceived that damage severity at any storey can be estimated with only 3 sensors, one at the top storey and two others at the adjacent floors of the desired storey. To verify the effectiveness of the proposed expression, an experimental study has been carried out on a 9-storey miniature model. It has been observed that the first resonating peak amplitudes of the responses recorded simultaneously at desired locations of intact and damaged structures are sufficient to estimate the damage intensity. Thus, the proposed formulation is efficient as an output measurement-based approach. The novelty of this study lies in the proposed closed-form FRF-based multi-damage severity expression involving minimum number of sensors and without any information on the input excitation. The results obtained from both numerical and experimental study showcase the robustness, simplicity and applicability of the proposed formulation.

Crack Recognition in Masonry Structures: CNN Models with Limited Data Sets

This research focuses on the task of Masonry Wall Crack Identification using limited data, employing state-of-the-art Convolutional Neural Network (CNN) models. The models investigated include VGG16, MobileNetV2, Xception, and DenseNet121. The dataset, consisting of 946 masonry wall images containing cracks, is used to evaluate the effectiveness of each model in this specific domain. The training set comprises 642 images, the validation set consists of 90 images, and the test set includes 214 images. The models are pretrained on large-scale datasets to extract robust features and are then fine-tuned on the masonry wall crack dataset. Among the models, DenseNet121 stands out, achieving a commendable accuracy of 85.98% in accurately identifying masonry wall cracks. This result underscores the efficacy of DenseNet121 for the challenging task of crack identification in masonry structures using limited data. This study not only contributes to the field of structural health monitoring but also emphasizes the practicality of employing CNN models for real-world applications, particularly in the critical domain of masonry crack identification.

Electrode Placement Optimization for Electrical Impedance Tomography Using Active Learning

Electrical impedance tomography (EIT) offers a versatile imaging modality with a multitude of applications, although it encounters accuracy limitations. Herein, a novel systematic framework is presented that integrates a neural network (NN), active learning, and transfer learning to optimize electrode placement, improving image reconstruction performance based on user‐defined metrics. Given the many‐to‐one mapping between electrode configuration and the performance metric, the approach utilizes a NN that predicts performance metrics from electrode placement input. To maintain NN's prediction accuracy for unseen electrode configurations, performance metrics are maximized while iteratively updating the NN via active learning during the optimization process. Transfer learning is employed to expedite optimization of electrode placements for time‐consuming iterative reconstruction techniques by fine‐tuning a NN initially trained on one‐step reconstruction data. The method is validated using two representative reconstruction methods: one‐step reconstruction with Newton's one‐step error reconstructor prior and the iterative total variation method. This research underscores the potential of the proposed framework in addressing EIT's inherent limitations and augmenting its performance across diverse applications and reconstruction methods. The framework could potentially contribute to the advancement of noninvasive medical imaging, structural health monitoring, strain sensing, robotics, and other fields that depend on EIT.

Loose bolt localization and torque prediction in a bolted joint using lamb waves and explainable artificial intelligence

With the increasing application of artificial intelligence (AI) techniques in the field of structural health monitoring (SHM), there is a growing interest in explaining the decision-making of the black-box models in deep learning-based SHM methods. In this work, we take explainability a step further by using it to improve the performance of AI models. In this work, the results of explainable artificial intelligence (XAI) algorithms are used to reduce the input size of a one-dimensional convolutional neural network (1D-CNN), hence simplifying the CNN structure. To select the most accurate XAI algorithm for this purpose, we propose a new evaluation method, feature sensitivity (FS). Utilizing XAI and FS, a reduced dimension 1D-CNN regression model (FS-X1D-CNN) is proposed to locate and predict the torque of loose bolts in a 16-bolt connected aluminum plate under varying temperature conditions. The results were compared with 1D CNN with raw input vector (RI-1D-CNN) and deep autoencoders-1D-CNN (DAE-1D-CNN). It is shown that FS-X1D-CNN achieves the highest prediction accuracy with 5.95 mm in localization and 0.54 Nm in torque prediction, and converges 10 times faster than RI-1D-CNN and 15 times faster than DAE-1D-CNN, while only using a single lamb wave signal path.

Structural Damage Detection Based on the Correlation of Variational Autoencoder Neural Networks Using Limited Sensors.

Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially constructing a Variational Autoencoder network model for bridge damage detection, optimizing parameters such as loss functions and learning rates for the model, and ultimately utilizing response data from limited sensors for model training analysis to determine the structural state. The contribution of this paper lies in the ability to identify structural damage without baseline data using response data from a small number of sensors, reducing sensor costs and enhancing practical applications in engineering. The effectiveness of the proposed method is demonstrated through numerical simulations and experimental structures. The results show that the method can identify the location of damage under different damage conditions, exhibits strong robustness in detecting multiple damages, and further enhances the accuracy of identifying bridge structures.

Dual-parameter combined measurement sensor based on tri-metal structure

Simultaneous measurement of strain and temperature in vibrating environments has always been a key technical issue in the field of structural health monitoring. To address the bottleneck of inadequate dual-parameter measurement performance in existing Fiber Bragg Grating (FBG) strain and temperature sensors, a new type of fiber optic grating sensor with anti-vibration capabilities for the measurement of both strain and temperature is proposed. The sensor utilizes a flexible hinge structure made of 304 steel with high elastic modulus for strain sensitization and a bimetallic structure composed of 7075 aluminum and 4 J36 Invar alloy for temperature sensitization, integrating both types of sensors. Theoretical analysis of the sensor was conducted, followed by the fabrication of a prototype and the establishment of a testing system for performance evaluation. The results show that the designed dual-parameter FBG sensor has a strain sensitivity of approximately 3.06 pm/µε, about 2.62 times that of a bare FBG, and a temperature sensitivity of about 50.2 pm/℃, roughly 4.97 times that of a bare FBG. In anti-vibration performance tests between 5–100 Hz, the wavelength drift of the strain and temperature sections of the FBG in the sensor were both within 1 pm, meeting the requirements for simultaneous measurement of strain and temperature in vibrating environments. These findings provide a reference for the development of similar sensors and further improvement of the measurement performance of FBG strain and temperature sensors.

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures.

The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology

Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures.

Membrane-based optical fiber Bragg grating pressure sensor for health monitoring of pile foundations

A fiber Bragg grating (FBG) pressure sensor is proposed, designed, and fabricated for lateral earth pressure sensing, in which the FBG sensor is mounted on a 3D printed trestle structure combined with a membrane. The applied pressure can cause a deformation on the membrane, and then this deformation applied on the trestle structure causes tensile strain on the FBG. The proposed sensor is functionalized as a high-sensitive pressure transducer capable of converting the pressure into strain on the FBG. Here, the performance of the proposed sensor is numerically and experimentally investigated. The results show that the pressure sensitivity at 30°C is 10.62 pm/kPa within a range of 0–0.6 MPa. Due to the thermal expansion of the structure, the pressure sensitivity coefficient decreases with the increase of temperature; however, the cross effect between the temperature and strain on the sensing sensitivity is investigated and can be eliminated. The fabricated sensor has advantages of high sensitivity, good stability, and high pressure resolution, so it has potential in the field of structural health monitoring.

A New Approach to Structural Damage Identification Based on Power Spectral Density and Convolutional Neural Network

In the field of structural health monitoring, vibration-based damage identification remains a formidable challenge. Key to this challenge is the establishment of a reliable association between observed vibration characteristics and the actual state of structural damage (e.g. stiffness reduction). This association not only accurately indicates the presence of damage, but also the location and severity of the damage. To solve this complex pattern identification problem, a large number of approaches, including deep learning, have emerged in recent years. In this paper, we propose a new structural damage identification method that utilizes the vibration information of the structure and a convolutional neural network based on Alex NET improvement. The method consists of calculating the acceleration response power spectral density of damaged and undamaged structures under impact loading separately, and then making a difference between the two power spectral data, and subsequently introducing these power spectral difference data into the convolutional neural network for training. The use of power spectral density analysis as a preprocessing step converts the time-domain signals into frequency-domain signals, and this conversion allows the convolutional neural network to capture and learn from the specific frequency characteristics of the data, thus facilitating the learning process of the neural network model. In this paper, the effectiveness of the method is critically evaluated through numerical simulation and experimental validation, and 3% and 5% noise are added to the numerical study to test the robustness of the method. During the convolution neural network training process, the optimal training mean squared error (MSE) is [Formula: see text] in the case of no noise addition; the optimal training MSE is [Formula: see text] in the case of noise addition. Both the results of simulations and experiments confirm the high accuracy and good robustness of the method in localizing structural damage.

Deep-learning approach based on multi-data fusion for damage recognition of marine platforms under complex loads

This paper presents an innovative deep-learning network underpinned by advanced data-fusion techniques developed explicitly for detecting structural damage in marine platforms. This study commences with a rigorous validation of the proposed methodology employing detailed experimental procedures and subsequently applies this method to identify damage under specific random load conditions utilizing state-of-the-art finite element analysis software. This entails the meticulous construction of an extensive database mirroring the real-life conditions encountered by a deep-water jacket platform in the South China Sea. By conducting thorough simulations based on the characteristic environmental loads of this marine area, the study succeeds in closely replicating real-world conditions. The developed damage-identification model, which is specifically adapted to the distinctive requirements of non-Gaussian white-noise loads, exhibits notable accuracy. The experimental outcomes derived from the application of the proposed deep-learning algorithm for data fusion indicate an impressive accuracy rate of 92.01%. In simulated scenarios involving complexly loaded jacket platforms, the model attains an accuracy of 88.25%, highlighting the robustness of the method and its wide applicability in the field of marine structural health monitoring.

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.

An integrated deep neural network model combining 1D CNN and LSTM for structural health monitoring utilizing multisensor time-series data

Introducing deep learning algorithms into the field of structural health monitoring (SHM) has contributed to the automatic extraction of damage-sensitive features, but the type and architecture of these algorithms are still in dispute. This paper proposes a hybrid deep learning framework entitled time-distributed one-dimensional convolutional neural network (1D CNN) long short-term memory (LSTM) model, which utilizes raw multisensor time histories to detect structural damages. Using a sliding window that moves along the temporal dimension, the multisensor data are first segmented into subsequences. The 1D CNN layers are simultaneously applied to each subsequence to extract damage-sensitive features from row data samples. These features are then fed into the LSTM layers to extract temporal features between subsequences. As the final step, these extracted features are classified using fully connected layers. In order to assess the performance of this model, a numerical model of a high-rise frame with nonlinear members is used. This hybrid model is assumed to identify the location of damages to this frame. In order to assess the proposed model with a real-world structure, a well-known benchmark building is employed to identify damage patterns by this deep hybrid neural network. A set of metrics related to the performance of the model is measured and evaluated. It is found that the model has an average accuracy of above 96.6% in localizing damage in the numerical structure and above 99.6% in detecting each damage pattern in the experimental building. The results indicate that the proposed model can be applied effectively to the SHM of different structural systems with different damage patterns.

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds.

With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher R2 values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading.

Multipoint laser ultrasound transmitter using single-multi-single mode fiber structures

Laser ultrasound technology is a promising alternative to piezoelectric ceramics in the field of structural health monitoring, especially the distributed applications. A novel multipoint laser ultrasound transmitter using single-multi-single mode fiber structure was proposed in this study. Both theoretical simulations and experimental results indicate transmitters with controllable coupling ratio can be fabricated by directly changing the length of multimode fiber. To validate the capability of distributed application, a four-point energy-balanced laser ultrasound transmitter system has been achieved by connecting transmitters with different coupling ratio in increasing order. The four transmitters exhibited similar characteristics both in time domain and frequency domain, with a peak-to-peak amplitude of 1018 mV and a center frequency closed to 4 MHz. The transmitters exhibited a relatively high signal-to-noise ratio and an elevated energy conversion efficiency with over 5 times that of previous researches. The proposed transmitter has a potential for distributed applications. However, further analysis of the mode of the ultrasonic waves or packaging of the transmitter is needed before it can be used in structural health monitoring.

A semi-analytical wavelet finite element method for wave propagation in rectangular rods

Prior knowledge of the dispersion curves and mode shapes of guided waves provides valuable information for wave mode selection and excitation in the field of non-destructive evaluation (NDE) and structural health monitoring (SHM). They are typically computed by the matrix methods, the finite element (FE) and semi-analytical finite element (SAFE) methods. However, the former is prone to numerical instability, and the latter two are limited by the refinement level of the FE mesh. In this paper, a semi-analytical wavelet finite element (SAWFE) method is presented to characterize wave propagation in rectangular rods. The piecewise polynomial interpolation functions of the SAFE method are replaced by two-dimensional scaling functions of the B-spline wavelet on the interval (BSWI). To demonstrate the accuracy of the proposed SAWFE technique, the propagation of guided waves in an aluminium plate is studied first. Then, the propagation of guided waves in rectangular rods of arbitrary aspect ratio is investigated. The results of this work clearly show that the SAWFE method presented here has higher accuracy and efficiency than the SAFE method.

Fast Force Estimation of Cable Structures Using Smartphone-Captured Video and Template Matching Algorithm

Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video.

Data interpretation and forecasting of SHM heteroscedastic measurements under typhoon conditions enabled by an enhanced Hierarchical sparse Bayesian Learning model with high robustness

Rapid advances in Structural Health Monitoring (SHM) have led to a new era of Big Data in the past decade. However, collected data may include a huge amount of heteroscedastic data, particularly under extreme events (e.g., typhoons), which makes robust decisions remain a challenging problem. To this end, this article proposed an enhanced Hierarchical Sparse Bayesian Learning (eHSBL) model for SHM field data interpretation and forecasting, uncertainty analysis, as well as correlation analysis. The proposed eHSBL model incorporates the Gaussian kernel function and the Hierarchical Bayesian model for an operational large-scale suspension bridge, i.e., Tsing Ma Bridge (TMB) in Hong Kong, under extreme events exhibiting heterogeneities, strong volatilities, and high uncertainties. The Gaussian kernel function in the eHSBL model is capable of mapping SHM heteroscedastic data into high-dimensional space, and the Hierarchical Bayesian model can be iteratively updated by introducing hyperparameters to achieve the sparse expression of the model, so the model has strong generalization ability and high robustness. Through the incorporation of the extrapolation forecasting algorithm, the eHSBL can carry out the forecasting analysis for the future SHM data. Results show that the eHSBL regression and forecasting results under extreme events can well reflect the strain responses of the structure. The uncertainty and correlation analysis reveal that the eHSBL model error variation is influenced by temperature, traffic loads, and wind speeds, and the corresponding error variation curves and change rules are also provided and summarized, which will shed light on the performance evaluation and early warning for in-service long-span bridges.