Role In Structural Health Monitoring Research Articles

Characterization of dynamic coupling induced shifts of frequencies in vehicle-scanning method

A comprehensive strategy combining theory and experiment is proposed to characterize the dynamic coupling induced shifts of vehicle and bridge frequencies through both experimental investigation and numerical validation, in which the effect of vehicle-bridge interaction is theoretically incorporated in the vehicle-scanning method based on the experimental model. It is known that the measurement of bridge frequencies plays an important role in structural health monitoring as the shift of frequency can be served as an indicator of possible damage occurring in a bridge. Nevertheless, the attention has been mainly drawn on the accuracy improvement in developing the vehicle-scanning method so far, with limited discussion of dynamic coupling effect on the frequency shifts. As such, a combined theoretical–experimental approach is presented to show that the influence of vehicle-bridge interaction results in the coupled frequencies of a vehicle-beam system using the vehicle-scanning method, and adopting a heavy lumped test vehicle can lead to the underestimated bridge frequency and overestimated vehicle frequency. As a new finding, the effect of rolling frequency is experimentally observed in the vehicle spectrum for the first time.

Simulation-based Optimal Sensor Placement in Tunnel Structures considering Uncertain in-situ Conditions and Multiple Loading Scenarios

The expansion of the underground infrastructure and the necessity to maintain the full functionality of the existing tunnels highlights the role of structural health monitoring in keeping track of the behaviour of the structure over time. In the study, the focus is on segmental tunnel lining for deep and long tunnels. The aim of this research is to investigate the best position for sensor placement based on the potential loading scenarios that might insist on the tunnel structure. Since for the evaluation of the lining safety we are interested in the maximum stresses reached in the structure, which might lead to durability critical cracking or crushing of concrete, a method is presented to suggest optimal positions for strain gauges. Due to uncertain geological conditions, multiple loading configurations acting on the tunnel structure are simulated using a finite element model. The results are employed to better identify the sensor locations, on the basis of where damage is prone to occur and where maximum information about the stress state in the structure can be inferred. The results obtained by the framework are discussed in light of a practical engineering perspective and the advantages as well as the limitations of the proposed approach are expounded.

Physics-Informed Machine Learning for Structural Damage Diagnosis in Aluminium Plates

Damage diagnosis plays a crucial role in Structural Health Monitoring (SHM) by facilitating the identification, localization, and estimation of the extent of defects in structures. Lamb waves, known for their sensitivity to defects, are widely employed in SHM methods for thin-walled structures. Most of those traditional methods require extracting damage indices from Lamb wave signals. This operation involves substantial post-processing and implies that part of the diagnostic information is lost. To solve those limitations and improve the damage diagnosis accuracy, machine learning methods have recently been proposed in the literature. However, the reluctance of the industrial sector to adopt conventional black-box models due to their lack of explainability poses a challenge. This study proposes a physics-informed machine-learning approach to address the limitations of standard black-box methods. Particularly, a Physics-Informed Neural Network (PINN) is implemented to predict the density of an aluminium plate based on measurements of plate displacements caused by Lamb wave excitation. This is made possible by the implementation of a specific loss function, which leverages physical knowledge in the form of the partial differential equation governing Lamb waves. Predicting the plate density based on measured displacements eliminates the need for artificial damage indices, utilizing the density variation itself to detect and localize damage. Additionally, the outputs of the PINN, rooted in physics equations, offer enhanced explainability compared to standard black-box models. The versatility of this framework extends to predicting material properties distributions for components, and efforts will be directed towards adapting the method for composite materials, where the approach may pose additional challenges.

Different Scenarios of Autonomous Operation of an Environmental Sensor Node Using a Piezoelectric-Vibration-Based Energy Harvester.

This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node's power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery's lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems.

Integrating self-sensing nano composite and fiber optic sensors technologies for advancing structural monitoring: A mini review

Concrete structures are susceptible to various damages due to ageing and natural hazards. The growing research focus in civil engineering systems is Structural Health Monitoring (SHM). Meanwhile, advanced sensing technologies play an eventual role in SHM, enhancing safety and maintenance by providing real-time data on concrete structures. This review represents the characteristics of advancing sensing technology, such as self-sensing sensors based on nanomaterials and fiber optic sensors. Further, the investigation has been performed to comprehend the evolving field of sensing technology related to several aspects, such as the principal mechanism, practical application, and challenges pertain to SHM. The advantages and limitations of integrating self-sensing multifunctional sensors with nanotechnology as an alternative to fiber optic sensors is discussed. In addition, the analogy of these two sensors is detailed to understand the efficiency of advanced sensing technology using nanotechnology. Both types of sensors exhibit the capability to monitor the changes in the structures. Self-sensing technology provides a straightforward solution, thus leading to cost-effective and simplified structural integrity compared to complex and fragile fiber optic sensors. Self-sensing multifunctional sensors offers an efficient integrated approach while fiber optic sensors provide comprehensive solution for structural monitoring. Particularly, self-sensing sensors offers direct data collection and large-scale applications for extensive projects due to their ease of installation. Therefore, the highly advanced self-sensing sensors with Level 5 technology will eventually be an ideal choice over FOS for SHM.

Research on Tuning Control Technology for Wireless Power Transfer Systems for Concrete Embedded Sensors

Concrete embedded sensors play a very important role in structural health monitoring. However, the time of endurance of sensors remains a performance bottleneck and sensors need to be charged without damaging the structure as well. Wireless power transfer (WPT) technology is a promising approach to solving this problem. However, the electromagnetic characteristics of concrete medium can cause WPT systems to be untuned and can reduce the energy transmission efficiency of the system. In this paper, the induced medium loss and eddy current loss of a WPT system in concrete are calculated using analytical equations and finite element analysis method. The equivalent circuit model of a concrete–air transmedia WPT system is established according to the calculated losses and a composite tuning control technology is proposed based on the above analysis. In addition, the composite tuning control technology combines the advantages of frequency-modulation tuning and dynamic compensation tuning to ensure the overall resonance of the WPT system. The tuning control technology can ensure the whole resonance of the WPT system and make the natural resonant frequencies of primary and secondary sides consistent. The experimental results show that compared with the untuned control technology, the output power and efficiency of the tuned control system increased by 73% and 11.05%, respectively. The proposed tuning control technology provides direction for future charging of concrete-embedded sensors.

Long-term continuous automatic modal tracking algorithm based on Bayesian inference

Modal tracking plays a vital role in structural health monitoring since changes in modal parameters help us understand a structure’s dynamic characteristics and may reflect the potential deterioration of structural performance. Although numerous modal parameter estimation (MPE) methods exist, it is not guaranteed that an MPE process will exclude all spurious modes and not lose any physical modes every time over a long-term monitoring period. Relatively large damping of a structure, poor data quality, and significant changes in structural modal parameters may make the estimated modal parameters spurious, missing, or misclassified. It makes long-term modal tracking semiautomated or manual, which constrains timely downstream applications such as anomaly detection, condition assessment, and decision making. This research aims to propose a long-term continuous automatic modal tracking algorithm based on Bayesian inference even when the modal parameters, damping, and data quality change significantly. Bayesian inference is used to determine the physical modes from the results of existing MPE methods. Both the modes identified from the most recent response set and the modal probability model from multiple previous response sets are considered in the Bayesian model to better determine the physical modes from the results of MPE. Moreover, the proposed algorithm requires only three extra hyperparameters compared to general modal tracking algorithms, and they can be quickly determined by a grid search method. The performance of the proposed algorithm is verified by a numerical example and a real-world civil structure Z24 Bridge benchmark.

Synchronous Wireless Sensor and Sink Placement Method Using Dual-Population Co-evolutionary Constrained Multiobjective Optimization Algorithm

Optimal wireless sensor placement (OWSP) plays a pivotal role in structural health monitoring. This study proposes a method to determine the simultaneous placement of sensors and sinks that minimizes energy consumption and maximizes information effectiveness. Network connectivity and reliability are critical constraints that determine the lifetime of wireless sensor networks. In this study, OWSP was formulated as a constrained multi-objective optimization problem with mixed-integer programming. Accordingly, a dual-population constrained multi-objective optimization (DCCMO) algorithm, which includes new crossover and mutation operators, was developed. In DCCMO, weak cooperation between two offspring populations is exploited to improve the efficiency of the solution search. The performance of DCCMO was compared to that of five other state-of-the-art algorithms using numerical examples with varying network parameters. DCCMO not only successfully matches the constrained Pareto front but also balances energy consumption and information effectiveness while exhibiting greater diversity and faster convergence than all other tested algorithms.

Imaging concrete cracks using Nonlinear Coda Wave Interferometry (INCWI)

Crack imaging in highly heterogeneous materials, such as concrete, plays a great role in structural health monitoring and non-destructive evaluation and testing. Numerous works have shown that Nonlinear Coda Wave Interferometry is a robust tool to assess the overall level of nonlinearity caused by micro or macrocracks in heterogeneous media. This work presents a novel qualitative method for Imaging using NCWI (INCWI) based on the combination of Nonlinear Coda Wave Interferometry (NCWI) and a spatial averaging method. INCWI is used herein to locate cracks generated in a concrete beam subject to three-point bending. Measurements are performed after each loading level, in both pre-peak and post-peak phases, once the beam has been unloaded. The imaging algorithm is described and applied to each stage of beam damage. In the post-peak phase, crack size varies from 10cm to 15cm in length and from 25μm to 200μm in width. Also, the INCWI results are compared with acoustic emission measurements and microscopic observations.

Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm

Structural damage identification plays a crucial role in structural health monitoring. In this study, a novelty method for structural damage identification is developed, which employs an advanced surrogate modelling technique to drive a new hybrid optimization strategy, namely a combination of K-means clustering optimizer and genetic algorithm (HKOGA). The core of this method is using the reliable sparse polynomial chaos expansion model as a cost-effective substitute for the computationally expensive structural finite element models, thus greatly improving the efficiency of the optimization strategy in finding the optimal value of the objective function. To evaluate the performance of this hybrid optimization strategy, seven optimization algorithms are selected and compared with it for 23 classical benchmark functions, and the comparative results show that the HKOGA has the best performance. Taking a small-scaled laboratory dam as an example, the efficiency and reliability of the proposed method to cope with the problems concerning finite element model updating and structural damage identification are explored. Two important findings are as follows. (i) For finite element model updating, compared to the conventional method based on iterative optimization, the proposed method improves computational efficiency by a factor of 59 while maintaining computational accuracy. (ii) For structural damage identification, leaving aside the huge leap in computational efficiency, the HKOGA has a faster convergence rate, stronger robustness, and higher accuracy than its sub-algorithm K-means clustering optimizer (KO). The results show that this method can be severed as an extremely efficient and potential tool to identify damage in large and complex structures.

Hybrid Digital Twins: A Proof of Concept for Reinforced Concrete Beams

AbstractDigital twins map physical objects, processes, and further entities from the real (physical) world into digital space. Going one step further, hybrid digital twins combine physics‐based modeling with data‐based techniques to form a simulation tool with predictive power. In the light of an increasing digitalization of our built world, such digital twins have great potential to contribute to the protection of critical technical infrastructures. In case of bridges, digital twins can have a key role in structural health monitoring. This contribution outlines a path to approach these goals and provides a proof of concept of a hybrid digital twin for steel‐reinforced concrete beams as a representative component in civil engineering structures.Four model components are combined to form the hybrid digital twin, namely, a physics‐based full‐order model, a fast‐to‐evaluate reduced‐order model, a purely data‐driven model, and a baseline model. Applied to the concrete beam, the full‐order model is based on a novel finite element formulation allowing for efficient modeling of slender structures embedded into solid bodies. We use this method to capture the interaction between reinforcement components and concrete matrix of the beam. As reduced‐order model, a physics‐informed neural network is trained with parts of the available measurement data and with the governing equations of a simplified physical model. The data‐driven model localizes cracks in the concrete in a statistical outlier analysis of fiber‐optical strain measurement data. In completion, the baseline model estimates the system behavior based on a closed‐from expression from civil engineering literature.The proposed hierarchy of four models with decreasing model complexity allows to select an appropriate model according to an application specific trade‐off between model accuracy and cost. We demonstrate the combination of physics‐based modeling with data‐driven techniques based on sensor data measured in a physical four‐point bending test of the reinforced concrete beam.

Structural displacement monitoring using ground-based synthetic aperture radar

This study develops a ground-based synthetic aperture radar (GBSAR) imaging system and interferometric processing framework for structural health monitoring (SHM). Radar remote sensing for displacement monitoring plays a vital role in SHM. Radar sensors and, more specifically, GBSAR systems have gained more attention in recent studies due to their capability to overcome the limitations of other contact or non-contact SHM sensors. This paper proposes a three-dimensional SAR imaging system and advanced signal processing scheme to extract continuous (fast) displacement components, such as structural vibrations, and discontinuous (slow) displacements, such as subsidence or uplift, using SAR time-series datasets. Using controlled experiments, this study shows that the developed GBSAR can monitor sub-millimeter displacements with high spatial resolution and 0.02 mm precision in the LOS direction. Moreover, several simulations are carried out to evaluate the proposed displacement monitoring algorithms. Accordingly, the proposed continuous monitoring framework can measure fast LOS displacements with better than 0.03 mm precision by reducing the effects of radar movement, clutters, and disturbances. Also, evaluations on a simulated monitoring scene at 20 m from the GBSAR’s LOS show that the proposed discontinuous displacement monitoring algorithm can estimate 3D displacement vectors with sub-millimeter precision. The results show that the developed GBSAR can be used for repeat-pass LOS displacement monitoring and the proposed algorithms demonstrate high potential for measuring continuous sub-second LOS displacements and long-term 3D displacement vectors.

Design and Investigation of a Fiber Bragg Grating Tilt Sensor With Vibration Damping

Fiber Bragg grating (FBG) sensors play an increasingly important role in structural health monitoring, and tilt sensors are one of the most commonly used among them. Traditional FBG tilt sensors are easily affected by external vibration, and to overcome such a problem, this work proposes a novel FBG tilt sensor with a vibration damping feature. Based on the classical equal strength beam structure, a vibration-isolating spring is used as the vibration-damping connection between the beam and the mass block, while a damping fluid is filled inside the sensor housing as the vibration-damping material. After elaborating on the details of sensor structural design and measurement theory, five sensors were set up for relevant performance tests, and the controlled experiments were carried out for the FBG sensors with and without the vibration isolation springs. Meanwhile, for sensors with vibration isolation springs, the viscosity of filled damping fluid was also varied in the experiments, to better reveal its influence on the sensor performance. The experimental results show that the proposed sensors have a high sensitivity of 112.743 pm/° within a range of ±15° and also have superior creep resistance as well as good temperature compensation capability. From controlled experiments, it is evident that the proposed sensor with a vibration damping structure offers better vibration stability and monitoring accuracy and can be effectively adapted to safety monitoring tasks in different engineering applications by simply adjusting the viscosity of filled damping fluid.

Structural health monitoring of a historic building during uplifting process: system design and data analysis

The south building of Huadong Hospital is a well-known historic building in Shanghai, China. In August 2021, the building was lifted by 1.30 m to correct the subsidence caused by long-term use and to expand the usable space of the building. To ensure structural safety during uplifting of the historic building, a structural health monitoring system is devised to monitor the overall status of the structure and the state of critical components. The structural health monitoring system comprises sensor, data acquisition, and data management systems. The content that is monitored includes vertical displacement, horizontal displacement, inclination, differential settlement, cracks, strains, and acceleration. The overview and uplifting scheme of the historic building are first introduced. The monitoring system’s design and sensor installation are then thoroughly discussed, followed by monitoring data analysis to investigate the state of the structure before and after uplifting. Furthermore, a structural safety assessment based on multisource monitoring data and neural networks is conducted. Huadong Hospital’s south building is currently the largest uplifted historic building in terms of construction area in China. The building’s structural health monitoring system played an exemplary and promoting role in structural health monitoring during uplifting of historic buildings, and it also has referential significance for similar engineering projects.

Effects of temperature on modal characteristics of non-uniform rigid-frame bridges

The effect of temperature on structural modal characteristics plays an important role in structural health monitoring and evaluation. Herein, the free vibration analysis of rigid-frame bridge is conducted by taking into account the effect of temperature. Firstly, the non-uniform rigid-frame bridge is divided into several members to develop the flexural and axial vibration equations for members under the effect of temperature in accordance with Hamilton’s principle. Secondly, each member is further divided into several segments, and the transfer relationship between segments is established to obtain the generalized dynamic stiffness matrix of the member. Then, the generalized dynamic stiffness matrix of each member is constructed by using the numerical assembly method similar to the finite element method so as to obtain the characteristic equation of rigid-frame bridge. Furthermore, the natural frequency and mode shape of the whole rigid-frame bridge are calculated by using the algorithm of Wittrick-Williams. Finally, the dynamic characteristic test of Yong-an bridge and the numerical calculation of the rigid-frame bridge are carried out to verify the generality and accuracy of the proposed method. The results indicate that the change of temperature will lead to the secondary internal force and the change of Young’s modulus, which together affect the modal characteristics of the structure, and the change of natural frequency caused by the change of Young’s modulus is greater than that caused by the secondary internal forces. In addition, there is a negative correlation between temperature and natural frequency of the rigid-frame bridge, and different temperature gradient modes also have certain influence on the mode shape of rigid-frame bridge.

A Study on the Effectiveness of Spatial Filters on Thermal Image Pre-Processing and Correlation Technique for Quantifying Defect Size.

Thermal imaging plays a vital role in structural health monitoring of various materials and provides insight into the defect present due to aging, deterioration, and fault during construction. This study investigated the effectiveness of spatial filters during pre-processing of thermal images and a correlation technique in post-processing, as well as exploited its application in non-destructive testing and evaluation of defects in steel structures. Two linear filters (i.e., Gaussian and Window Averaging) and a non-linear filter (i.e., Median) were implemented during pre-processing of a pulsed thermography image sequence. The effectiveness of implemented filters was then assessed using signal to noise ratio as a quality metric. The result of pre-processing revealed that each implemented filter is capable of reducing impulse noise and producing high-quality images; additionally, when comparing the signal to noise ratio, the Gaussian filter dominated both Window Averaging and Median filters. Defect size was determined using a correlation technique on a sequence of pulsed thermography images that had been pre-processed with a Gaussian filter. Finally, it is concluded that the correlation technique could be applied to the fast measurement of defect size, even though the accuracy may depend on the detection limit of thermography and defect size to depth ratio.

Embedded Sensors for Structural Health Monitoring: Methodologies and Applications Review.

Sensing Technology (ST) plays a key role in Structural Health-Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems, or smart materials that monitor a wide variety of materials' properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and enabling continuous and permanent measurements of their structural integrity. The integration of ESs is limited to the processing technology used to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material's composition, which comprises either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM systems employing ESs, focusing on their actual developments and innovation, as well as analysing the challenges that these technologies present, in order to build a path that allows for a connected world through distributed measurement systems.

A Deep-Convolutional-Neural-Network-Based Semi-Supervised Learning Method for Anomaly Crack Detection

Crack detection plays a pivotal role in structural health monitoring. Deep convolutional neural networks (DCNN) provide a way to achieve image classification efficiently and accurately due to their powerful image processing ability. In this paper, we propose a semi-supervised learning method based on a DCNN to achieve anomaly crack detection. In the proposed method, the training set for the network only requires a small number of normal (non-crack) images but can achieve high detection accuracy. Moreover, the trained model has strong robustness in the condition of uneven illumination and evident crack difference. The proposed method is applied to the images of walls, bridges and pavements, and the results show that the detection accuracy comes up to 99.48%, 92.31% and 97.57%, respectively. In addition, the features of the neural network can be visualized to describe its working principle. This method has great potential in practical engineering applications.

Nonlinear deformation monitoring of elastic beams based on isogeometric iFEM approach

Shape sensing plays a key role in Structural Health Monitoring (SHM) and has become an excellent methodology for large-scale engineering structures to achieve significant improvement in their safety, reliability, and affordability. The inverse finite element method (iFEM) is an accurate and efficient method for shape sensing to reconstruct the three-dimensional displacements using in situ surface strain data. This study proposes a novel shape sensing method for large deformation monitoring based on strain gradient theory and iFEM method. Initially, the nonlinear displacement fields are described, and Green–Lagrange strain theory is employed to deduce the theoretical section strains, and then the strain–displacement relation is established by using a least-squares variational principle. Next, isogeometric displacement functions and the measured section strain formulations are deduced, and a decoupling method for high order section strains is proposed. Finally, a cantilever beam is used to demonstrate the accuracy and effectiveness of the refined isogeometric iFEM method for large deformations. The numerical results show the superior reconstruction capability and potential applicability of the proposed model for accurate shape prediction of the non-linear deformation of beam structure.

Employing vision-based sensing for long-term monitoring

Despite the crucial role of structural health monitoring (SHM), traditional contact-based sensors are costly and lacking automation. Vision-based sensors have recently emerged as a potential substitute, due to their non-contact nature and low cost. However, investigations lack validating their long-term performance. This study assesses vision-based sensing for robust and efficient long-term displacement monitoring. Additionally, the suitability of various machine learning (ML) algorithms in automatically extracting information from the generated data are examined. A first experiment examines the capability to monitor ambiently excited structures over long periods. The parameters assessed are: a) noise and drift (e.g., related to environmental changes such as temperature, humidity, and lighting); and b) obtainable displacement resolution related to ambient excitation. Then, a second experiment examines the capability to monitor dynamically excited structures over long periods (via the employment of an artificial excitation, e.g., an eccentrically loaded mass motor). Here, the following parameters assessed are the: a) frequency bandwidth range; b) frequency resolution; c) displacement resolution related to dynamic excitation; and d) maximum number monitored points for given computational resources. Finally, for processing the generated data, the following ML algorithms are examined: a) Artificial Neural Networks (ANNs); b) support vector machines (SVMs); c) decision trees; and d) Convolutional Neural Networks (CNNs). Concerning long-term monitoring, the paper found that non-contact sensing had comparable accuracy with traditional contact-based sensors. About the ML models, the CNNs were found to be advantageous. Whilst this study was carried out on a small-scale structure, the potential of vision-based sensing for long-term monitoring of full-scale structures was highlighted.