Structural Health Monitoring Research Research Articles

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.

A neural network based digital twin model for the structural health monitoring of reinforced concrete bridges

Developments in Structural Health Monitoring (SHM) research over the past few decades have demonstrated potential in optimising maintenance solutions for degrading infrastructure. The scale of structural deterioration worldwide and the inadequacy of current non-destructive evaluation techniques necessitate the adoption of accessible, quantitative, continuous SHM technology into mainstream asset management practice. This paper seeks to address this significant demand by proposing a robust, end-to-end, fibre-optic sensor (FOS) monitoring prototype which utilises deep neural networks to convert FOS strain output into an interactive digital twin (DT) visualisation. Finite-element validation demonstrated that the prototype was capable of capturing reliable structural analytics, recording an average error of less than 2kNm and an absolute error of less than 0.15 mm for bending moment and deflection respectively. Furthermore, the predictive mean absolute error of the integrated artificial neural network was less than 1με during testing, demonstrating the accuracy of the digital twin when generating baseline strain data for structural analysis.

Research on structural damage identification and localization based on artificial neural network

Abstract Structural health monitoring is a research hotspot in engineering, and structural damage identification is one of the key problems in structural health monitoring research. This paper proposes a study on structural damage identification and localization based on artificial neural networks, and for the problem that the learning convergence speed of the BP neural network is too slow, a genetic algorithm is used to optimize the update of weights and thresholds during training and learning. In the finite element simulation, the structure's primary and secondary damage are taken as input nodes, and the optimized GA-BP neural network is used for training and identification. For the localization recognition of the primary damage of the structure, the maximum recognition relative errors of both the BP neural network and the GA-BP neural network did not exceed 5%, but the latter's accuracy was 2.63% higher than that of the former. For the localization recognition of secondary damage, the GA-BP neural network can effectively recognize 90% of the samples. The artificial neural network-based structural damage recognition localization has high recognition efficiency and accuracy, which is conducive to improving the robustness of the structural damage recognition system and is of significant help to real-time structural health monitoring.

Vibration displacement extraction based on an auto-tuning Kalman smoother from GNSS

High-frequency global navigation satellite system (GNSS) is one of the most effective means for structural health monitoring. At present, most of the GNSS-based structural health monitoring research focuses on the positioning method, however, the unmodeled error caused by the complex monitoring environment and the receiver are always ignored by such technology. Most of the displacement extraction algorithms based on GNSS kinematic positioning are based on the Kalman filter and its variants, and the research on the effective determination of the process noise matrix and the measurement noise matrix is still limited. This paper aims to explore a displacement extraction method with automatic parameter tuning for structural health monitoring. An auto-tuning Kalman smoother has been introduced for extracting vibration displacement from GNSS kinematic positioning. Specifically, we divide the GNSS kinematic positioning results into a training set and a validation set, then use non-convex optimization to obtain the hyperparameters of the Kalman smoother. Finally, we extract the displacement from Kalman smoother with the tuned hyperparameters. Simulation and field experiments were carried out to verify the proposed method. The results show the feasibility and effectiveness of the method for vibration displacement extraction. For real-time kinematic positioning, the displacement extraction accuracy is improved by 9.58% compared to the traditional Kalman smoother; for precise point positioning, the displacement extraction accuracy is improved by 15.36%. The influence of the proportion of training set on the proposed method is discussed, and suggestion for the training set proportion is given.

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.

A Miniaturized Low-Frequency FBG Accelerometer Based on Symmetrical Cantilever Beam

Real-time monitoring of vibration characteristics of large-scale engineering structures such as buildings, bridges, and dams is important for research in structural health monitoring and other fields. Aiming at the key problem that the characteristic index and miniaturization of the existing fiber Bragg grating (FBG) accelerometer with a cantilever beam cannot be considered simultaneously, a miniaturized low-frequency FBG accelerometer with a symmetrical cantilever beam is proposed. Using formula derivation and analysis system (ANSYS) simulation software, the structural parameters of the sensor are optimized and simulated for analysis. According to the simulation results, the real sensor is made. Finally, the sensor performance is tested. The results show that the natural frequency of the sensor is about 72 Hz, which can be used to monitor vibration signals in the frequency range of 8–52 Hz. The sensitivity of the sensor is about 681.7 pm/g, and the resistance to transverse interference is less than 4.9%. Its volume is only 6.48 cm3, which is much smaller than that of an FBG accelerometer of the same kind. The research results provide a reference for the development of an FBG accelerometer of the same type.

Development and Prospect of Smart Materials and Structures for Aerospace Sensing Systems and Applications.

The rapid development of the aviation industry has put forward higher and higher requirements for material properties, and the research on smart material structure has also received widespread attention. Smart materials (e.g., piezoelectric materials, shape memory materials, and giant magnetostrictive materials) have unique physical properties and excellent integration properties, and they perform well as sensors or actuators in the aviation industry, providing a solid material foundation for various intelligent applications in the aviation industry. As a popular smart material, piezoelectric materials have a large number of application research in structural health monitoring, energy harvest, vibration and noise control, damage control, and other fields. As a unique material with deformation ability, shape memory materials have their own outstanding performance in the field of shape control, low-shock release, vibration control, and impact absorption. At the same time, as a material to assist other structures, it also has important applications in the fields of sealing connection and structural self-healing. Giant magnetostrictive material is a representative advanced material, which has unique application advantages in guided wave monitoring, vibration control, energy harvest, and other directions. In addition, giant magnetostrictive materials themselves have high-resolution output, and there are many studies in the direction of high-precision actuators. Some smart materials are summarized and discussed in the above application directions, aiming at providing a reference for the initial development of follow-up related research.

Research on impact localization method based on passive tomography of piezoelectric sensors

Impact monitoring of plate structures has always been a hot spot in structural health monitoring research. In order to improve the accuracy of impact monitoring, this paper proposes a beam-focusing impact localization algorithm based on passive tomography using a sensor arrangement method in a ring array. The impact damage monitoring of large-sized structures in different directions is performed by each linear array of the ring array and the impact damage localization distribution maps of the corresponding linear arrays are generated. The localization accuracy of the linear arrays in different directions is obtained experimentally to assign the weights of each linear array to achieve image reconstruction of the impact damage. The experimental results show that the method can achieve a more accurate visualization of the impact location, and the reconstructed impact laminar image can well reflect the impact location distribution information. It can be used for the localization of impact damage and extended monitoring.

An Intelligent Health Monitoring Model Based on Fuzzy Deep Neural Network.

An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. Injury Assessment. The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%.

Switching Kalman filter for damage estimation in the presence of sensor faults

Bayesian filtering based approaches for diagnosis of structural damage have been widely employed in structural health monitoring (SHM) research. The approach however may lead to an inaccurate alarm/decision due to the presence of faulty sensor/s. Nevertheless, sensor faults are inevitable during real field SHM in which sensor may malfunction or get detached from the structural surface, registering completely irrelevant information as measurement. Eventually, such erroneous information induce error in the estimation which leads to an inaccurate, sometimes divergent and impractical solution. The current study deals with Bayesian filtering based structural damage detection in the presence of one or multiple (consecutive) sensor faults. The damage detection is addressed with joint state-parameter estimation approach while a switching filtering strategy is employed for sensor fault detection. Switching approach employs multiple possible sensor fault models which are subsequently integrated to the measurement model of the joint estimation approach. The selection of the competent model (/switching between model ensembles) is undertaken recursively based on their likelihood against measured response. The proposed approach is tested on a numerical lumped mass model of a shear frame building, followed by a laboratory experiment on a cantilever beam. It has been perceived that estimation of health for structures measured with faulty sensors can actually lead to a false (positive and negative) alarm which can, however, be avoided by the employment of the proposed approach. The performance of the proposed approach is further established for healthy and damaged system with pre-existing and sudden sensor faults.

Large‐scale structural health monitoring using composite recurrent neural networks and grid environments

AbstractThe demand for resilient and smart structures has been rapidly increasing in recent decades. With the occurrence of the big data revolution, research on data‐driven structural health monitoring (SHM) has gained traction in the civil engineering community. Unsupervised learning, in particular, can be directly employed solely using field‐acquired data. However, the majority of unsupervised learning SHM research focuses on detecting damage in simple structures or components and possibly low‐resolution damage localization. In this study, an unsupervised learning, novelty detection framework for detecting and localizing damage in large‐scale structures is proposed. The framework relies on a 5D, time‐dependent grid environment and a novel spatiotemporal composite autoencoder network. This network is a hybrid of autoencoder convolutional neural networks and long short‐term memory networks. A 10‐story, 10‐bay, numerical structure is used to evaluate the proposed framework damage diagnosis capabilities. The framework was successful in diagnosing the structure health state with average accuracies of 93% and 85% for damage detection and localization, respectively.

Latest Advances in Finite Element Modelling and Model Updating of Cable-Stayed Bridges

As important links in the transport infrastructure system, cable-stayed bridges are among the most popular candidates for implementing structural health monitoring (SHM) technology. The primary aim of SHM for these bridges is to ensure their structural integrity and satisfactory performance by monitoring their behaviour over time. Finite element (FE) model updating is a well-recognised approach for SHM purposes, as an accurate model serves as a baseline reference for damage detection and long-term monitoring efforts. One of the many challenges is the development of the initial FE model that can accurately reflect the dynamic characteristics and the overall behaviour of a bridge. Given the size, slenderness, use of long cables, and high levels of structural redundancy, precise initial models of long-span cable-stayed bridges are desirable to better facilitate the model updating process and to improve the accuracy of the final updated model. To date, very few studies offer in-depth discussions on the modelling approaches for cable-stayed bridges and the methods used for model updating. As such, this article presents the latest advances in finite element modelling and model updating methods that have been widely adopted for cable-stayed bridges, through a critical literature review of existing research work. An overview of current SHM research is presented first, followed by a comprehensive review of finite element modelling of cable-stayed bridges, including modelling approaches of the deck girder and cables. A general overview of model updating methods is then given before reviewing the model updating applications to cable-stayed bridges. Finally, an evaluation of all available methods and assessment for future research outlook are presented to summarise the research achievements and current limitations in this field.

The Demonstrator of Structural Health Monitoring System of Helicopter Composite Blades

The health and usage monitoring systems are widely used for the monitoring of vibrations of power plant units on helicopters. At the same time critical helicopter structures like main and tail rotors or tail boom are still out of permanent monitoring. Practically all non-destructive techniques (NDT), like ultrasound, eddy current, X-ray and others make the inspection of structural components available on the ground only, during the maintenance check or overhaul. The article considers the promising technique for structural health monitoring (SHM) of a helicopter in flight, inter alia, of rotating blades. Actively developing methods of modal analysis, especially “operational” one, open the new opportunity for structural monitoring and its attendant issues. The Operational Modal Analysis (OMA) techniques and the methods of its data application are the basis of the novel concept of “modal passport” for monitoring of helicopter’s blades. There is brief description of basic principles of the modal passport and its trial application. The paper discusses the problems associated with practical application of modal passport to rotating helicopter blades and ways to overcome it. The light coaxial helicopter Ka-26 became the operating platform for experimental study of OMA application and works as the demonstrator of advance SHM techniques. Brief description of measurement system, including equipment and transducers on the blades is presented. The discussed research stage aims to demonstrate the potential of OMA techniques for monitoring of rotating blades, applying the seeded faults to estimate sensitivity of the SHM system. The paper discusses analytical and experimental study of the blade, including FE modelling and comparative analysis with the experimental data. The conclusions of the paper summarize the results of the research stage, outline the novelties and its practical application. The tasks of further works for SHM research stages using the demonstrator are formulated.

A hybrid methodology for structural damage detection uniting FEM and 1D-CNNs: Demonstration on typical high-pile wharf

Vibration-based structural damage detection (SDD) has been a subject of intense research in structural health monitoring (SHM) for large civil engineering structures over the decades. The performance of the conventional SDD approaches predominantly relies on the rational choices of the damage feature and classifier. Hand-crafted features or fixed classifiers would not be the optimal choice for all structural damaged scenarios. This paper proposes a novel, quick and precise real-time SDD framework for high-pile wharf foundations using a combination of finite element modeling and 1D convolutional neural networks (CNNs). The distinct advantage of this method lies in extracting the damage-related features from the raw displacement response directly and automatically, and the computational complexity of the compact 1D CNNs is significantly lower because the data processing involves only simple 1D operations. The results show that the presented 1D CNNs have a superior ability to accurately identify the occurrence and location of damage in real time. In addition, the comprehensive performance of the CNNs trained by the displacement response dataset in component form is significantly better than that based on the dataset in absolute value form. The results also demonstrated that although the proposed CNNs are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure, the vertical component still has a positive effect on the improvement of the generalization and robustness of the CNNs.

Shared micromobility-driven modal identification of urban bridges

Recent research in Indirect Structural Health Monitoring (ISHM) uses the dynamic response of instrumented vehicles to carry out “drive-by” monitoring of bridges. These vehicles are generally cars or trucks instrumented with different types of sensors. However, some urban bridges are inaccessible to regular vehicles. Also, cars and trucks have non-negligible weight and suspension systems that may affect the collected vibration data. Stiff, light, and standardized shared micromobility vehicles, such as bicycles and electric kick scooters, have never been explored for ISHM purposes. This paper proposes an innovative and automatic ISHM strategy based on the data collected by smartphones temporarily installed on shared micromobility vehicles. An identification procedure suitable for cloud computing is proposed to extract the dynamic parameters of bridges without needing any sensor deployment, becoming particularly appealing for monitoring a densely built environment at a territorial scale. The methodology is applied to a real footbridge in Bologna (Italy).

Finite Element Models of a Benchmark Footbridge

Modern footbridges are often lively structures, characterized by natural frequencies that fall in the range of pedestrian activities, such as walking, running, and jumping. Therefore, serviceability assessment under human-induced excitation is crucial both at the design stage and during the footbridge lifetime. This paper presents and validates two different FE models of an existing footbridge with very complex geometry: the Streicker Footbridge at the Princeton University Campus. It represents a benchmark in the field as a testbed for vibration serviceability assessments under pedestrian excitation. The real structure is equipped with strain and temperature sensors that are currently used to collect measurements in both static and dynamic modes for research and educational purposes in Structural Health Monitoring (SHM). Based on detailed drawings of the Streicker Footbridge, a three-dimensional beam-based model was developed to represent the complex behavior of the full-scale benchmark bridge. Subsequently, a more refined discretization of the bridge deck adopting shell elements was inserted. The bridge Finite Element models were validated against available SHM data concerning static and dynamic tests. The relevant ANSYS APDL script files along with an example of pedestrian jumping application are available upon request for further research developments on the relationship between pedestrians and the benchmark footbridge.

An Optimization Method for the Layout of Soil Humidity Sensors Based on Compressed Sensing

In the farmland Internet of Things, to achieve precise control of production, it is necessary to obtain more data support, which requires the deployment of many sensors, and this will inevitably bring about high investment and high-cost problems. This paper mainly studies the optimization of sensor placement in the agricultural field. Through compressed sensing and algorithm optimization, the number of sensors used is reduced and the cost is reduced on the premise of ensuring the effect. At present, there are many mature sensor layout optimization methods, but these methods will have incomplete parameters due to experimental conditions and environmental factors. They are more suitable for structural health monitoring and lack research in agricultural applications. Considering that the sensor layout optimization can be converted into the characteristics of image compression selection and the compression effect of the compressed sensing theory is better, therefore, this paper proposes a sensor layout optimization method based on compressed sensing. Due to the structural characteristics of the existing measurement matrix in the compressed sensing theory, the specific position distribution of the optimized sensor layout cannot be obtained directly. This paper improves the existing sparse random measurement matrix to determine the number of sensors required for a given region and the function of the specific location of each sensor. The experimental results show that soil moisture can be measured with a small error of 0.91 by using 1/3 of the original sensor number. The result of data reconstruction using 1/6 of the original sensor is average, and the average error is up to 1.68, which is suitable for the environment with small data fluctuation.

A Study on Technological Dynamics in Structural Health Monitoring Using Intelligent Fault Diagnosis Techniques: A Patent-Based Approach

The performance and reliability of structural components are greatly influenced by the presence of any abnormality in them. To this purpose, structural health monitoring (SHM) is recognized as a necessary tool to ensure the safety precautions and efficiency of both mechanical and civil infrastructures. Till now, most of the previous work has emphasized the functioning of several SHM techniques and systematic changes in SHM execution. However, there exist insufficient data in the literature regarding the patent-based technological developments in the SHM research domain which might be a useful source of detailed information for worldwide research institutes. To address this research gap, a method based on the Co-Operative Patent Classification (CPC) codes is proposed in the current study. The proposed method includes the patent analysis of SHM in terms of its global publication trend and technology-based applications. This analysis is performed using patent database search tools, namely, IncoPat and Espacenet. The period ranging from 2005 to 2019 is selected to retrieve the required patent documents. A new approach termed as Patents’ value is utilized to investigate the technological impact of a patent in the form of forward citations, technical stability, and scope of protection. The identification of emerging SHM techniques and forecasting of vacant technology is also part of current research work. The research results have revealed the increasing trend in the number of published patents each year related to various SHM technologies. In this regard, China, the United States, and South Korea are notified as to the major depositor countries, respectively. Hence, mapping of patent data in this research is an effort to illustrate the effectiveness of the proposed method to demonstrate the development trends and dynamic inventions over the time in SHM research domain to achieve the optimal damage inspections of various mechanical components.

Structural Health Monitoring and Prognostic of Industrial Plants and Civil Structures: A Sensor to Cloud Architecture

The deployment of Structural Health Monitoring (SHM) systems is a natively interdisciplinary task that involves joint research contributions from sensing technologies, data science and civil engineering. The capability to assess, also from remote stations, the working conditions of industrial plants or the structural integrity of civil buildings is widely requested in many application fields. The technological development aims to continuously provide innovative tools and approaches to satisfy these demands. As a first instance, reliable monitoring strategies are needed to detect structural damages while filtering out environmental noise. Ongoing solutions to tackle these topics are based on the exploitation of highly customized sensing technologies, such as shaped transducers for Acoustic Emission (AE) testing or Micro-Electro-Mechanical System (MEMS) accelerometers for Operational Modal Analysis (OMA) [1]. On the other hand, effective data acquisition and storage techniques must be employed to cope with the heterogeneity of the sensing devices and with the amount of data produced by collecting raw measured signals. Finally, damage detection and prediction tasks should be computed via data-driven algorithms that can complement the model-based alternatives traditionally used in civil engineering. Layered SHM architectures [2] represent straightforward approaches to address the system complexity originated by this interdisciplinary design; however, few real-world implementations have been presented so far in the literature. In this paper, we overcome these limitations by presenting an Internet of Things (IoT)-based SHM architecture for the predictive maintenance of industrial sites and civil engineering structures and infrastructures. The proposed cyber-physical system includes a monitoring layer, that consists of accelerometer-based sensor networks, a data acquisition layer, built on the recent W3C Web of Things standard [3], and a data storage and analytics layer, which leverages distributed database and Machine Learning tools. We extensively discuss the hardware/software components of the proposed SHM architecture, by stressing its advantages in terms of device versatility, data scalability and interoperability support. Finally, the effectiveness of the system is validated on a real-world use-case, i.e., the monitoring of a metallic frame structure located at the SHM research labs of the University of Bologna, Italy, within the MAC4PRO project [4].

Structural health monitoring research under varying temperature condition: a review

Suffering from solar radiation, day–night replacement and seasonal changes, the structure will produce notable temperature behaviour, which has a vital effect on the long-term process of the health monitoring. Previous studies show that there is a significant correlation between the measuring responses and temperature from health monitoring systems. To analyse the structural state more accurately, much literature employed health monitoring methods considering temperature effects. This paper reviews technical research concerning health monitoring of civil structures under varying temperature. Firstly, the correlation researches of structural measuring responses (dynamic and static responses) and temperature are reviewed, which includes the researches of the influence mechanism and the data statistics, and the studies of the influence of non-uniform temperature on responses are also reviewed. In addition, different types of separation and forecast methods of the temperature-induced part of the structural responses data are summarized, followed by a brief summary of benefits and drawbacks of these methods. Lastly, the recently proposed process frameworks of damage assessment considering temperature effects are also introduced.