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Digital technologies can be used to gather accurate information about the behavior of structural components for improving systems design, as well as for enabling advanced Structural Health Monitoring (SHM) strategies. New avenues for achieving automated and continuous structural assessment are opened up via development of virtualization approaches delivering so-called Digital Twins (DTs), i.e., digital mirrored representations of physical systems relying on fusion of simulation models and real-time monitoring data. In this framework, the main motivation of the work presented in this paper stems from the existing challenges in the implementation and deployment of a real-time predictive framework for virtualization of dynamic systems. Kalman-based filters are usually employed in this context to address the task of joint input-state prediction in structural dynamics. A Gaussian Process Latent Force Model (GPLFM) approach is exploited in this work to construct flexible data-driven a priori models for the unknown inputs, which are then coupled with a combined deterministic-stochastic state-space model of the structural component under study for Kalman-based input-state estimation. The use of Gaussian Process (GP) regression for this task overcomes the limitations of the conventionally adopted random-walk model, thus limiting the necessity of offline user-dependent calibration of this type of data assimilation methods. This paper proposes the use of alternative covariance functions for GP regression in structural dynamics. A theoretical analysis of the GPLFMs linked to the investigated covariance functions is offered. The outcome of this study provides insights into the applicability of each covariance type for GP-based input-state estimation. The proposed framework is validated via an illustrative simulated example, namely a 3 Degrees of Freedom (DOF) system subjected to an array of different loading scenarios. Additionally, the performance of the method is experimentally assessed on the task of joint input-state estimation during testing of a 3D-printed scaled Wind Turbine (WT) blade. [ABSTRACT FROM AUTHOR]

Impacting systems are widely used in many engineering applications, such as self-propelled robots, energy harvesting and percussive drilling, which exhibit rich and complex nonlinear phenomena. Among these applications, predicting nonlinearities and estimating system parameters are of great interest of nonlinear dynamics research community. Backbone curve is an analytical tool that captures the frequency–amplitude dependence of nonlinear systems. In this paper, we estimate the impacting stiffness of a single-degree-of-freedom non-smooth dynamical system qualitatively by using the backbone curve. It was found that an increase of the impacting stiffness may lead to lowering the backbone curve. An adaptive differential evolution algorithm with the Metropolis criterion is proposed to identify the parameters of the impacting system quantitatively using experimental data, which are consistent with our theoretical predictions. Finally, the identified parameters are verified, and the limitations of the backbone curve are drawn. The nonlinear characteristics identification method studied in this paper can be extended to other vibro-impact systems and is potentially applicable to structural health monitoring and robotic sensing. [Display omitted] • Nonlinear characteristics identification for an impacting system is studied. • Backbone curve is employed to identify the stiffness of the impacting spring. • Differential evolution algorithm with Metropolis criterion was also adopted. • Backbone curve can only estimate the impacting stiffness qualitatively. • The method is applicable to structural health monitoring and robotic sensing. [ABSTRACT FROM AUTHOR]

• This paper introduces a strain monitoring method where optical fibers are embedded along a spiral path in composite cylindrical structures. • A monitoring method is proposed to judge the health state of the structure by using the strain data monitored in this direction. • The comparison between the measurements of the embedded fiber optic and the surface-bonded strain gauges under various working conditions demonstrates excellent consistency, validating the fiber optic sensor's measurement accuracy. Composite cylinder structures are widely used in various engineering fields. Monitoring the health state is an essential method for ensuring the safe operation of the structures. Embedded distributed fiber optic sensors have gained significant interest as they effectively address issues related to aesthetics and protection. This paper presents a strain monitoring method that involves embedding optical fibers along a spiral path within composite cylindrical structures. Additionally, a monitoring method is proposed to evaluate the structure's health state using the strain data collected in this manner. To validate the measurement accuracy of the fiber optic sensors, a comparison is made between the measurements obtained from the embedded fiber optic sensors and surface-bonded strain gauges under different working conditions. The results demonstrate excellent consistency, establishing the reliability of the fiber optic sensors. In this study, a distributed fiber optic sensor network and embedding scheme are designed for large composite cylinder structures, and strain monitoring experiments at various conditions, including pressure, bending, and hoisting loads, are conducted. Finite element simulations are also performed at corresponding working conditions, and the effectiveness of fiber optic sensor measurement is confirmed by comparing normalized simulation results with measured results. The embedded distributed fiber optic sensors effectively capture the strain changes at the reinforcement frame located in the middle of the cylinder structure, which aligns with the theoretical analysis of the cylinder structure under varying loads. This study on the embedding of fiber optics in large composite cylinder structures provides valuable insights for structural health monitoring purposes. [ABSTRACT FROM AUTHOR]

• A reinforced concrete moment-resisting frame was tested on a shaking table. • Table excitations and frame response accelerations with increasing intensities of the table motion were acquired. • Crack images and three damage-sensitive features are compared in various stages of the damage. • The results explore the dependence of the damage-sensitive features on horizontal peak table velocities. Methods of early warning in damage detection of civil engineering structures represent a particular challenge in Structural Health Monitoring (SHM). To investigate these problems, vibration records of a full-size, reinforced concrete frame subjected to seismic damages on a shaking table, were investigated. The seismic recordings of excitations and response were accompanied by images of cracks associated with each state of the damage. This paper presents the results of the damage identification from non-linear response during the damaging phases of the shaking table tests. Time-dependent base shear forces were observed, together with wavelet spectral analyses of the responses. Three damage-sensitive features accounting for decreasing stiffness, energy dissipation, and the wavelet changes during inelastic seismic responses were analyzed as functions of horizontal peak table velocities. The paper aims to study seismic response records of moment-resisting reinforced concrete structure frames in search of prospective simple online damage indicators which could be applied to start alarms, right after excessive damage loads, e.g., in instantaneous earthquake rapid damage assessments of buildings. [ABSTRACT FROM AUTHOR]

Fiber optic curvature sensors face several challenges that limit their application in structural health monitoring. These include high fabrication and demodulation costs, poor stability and robustness. In this paper, we propose a novel Mach–Zehnder interferometer (MZI) sensor that overcomes these limitations.The sensor was fabricated through the splicing of a seven-core fiber(SCF) and two single-mode fibers (SMF) with a common fusion splicer. The proposed sensor exhibited a good linear response, with a sensitivity of 22 nm/m − 1 in the range of 0–0.313 m − 1. The MZI sensor was demodulated by a neural network-based system and a data augmentation algorithm, reducing the need for large data samples and improving demodulation accuracy on sparse datasets. Overall, the sensor is characterized by fabrication simplicity, high mechanical strength, and compatibility with existing optical devices, making it applicable to various fields, such as aerospace, civil engineering, and mechanical manufacturing. [ABSTRACT FROM AUTHOR]

• IE models and AGs are used to describe and compare a set of six real bridge decks bridges. • Modal responses from each of the six bridges are identified and subsequently compared using the Modal Assurance Criterion. • increasing number of elements making up the beam and slab bridge decks results in a consistent decreasing similarity metric • Similarity metrics from Fig. 7 appear largely reflective of the meaningful similarities between the bridges Bridges are valuable infrastructure assets that are challenging and expensive to maintain. State-of-the-art data-based bridge SHM solutions look to use bridge response data for condition assessment and damage detection. Data-based SHM methods can be limited in their application as they require large datasets to train models effectively, and most bridges lack the available data for the approaches to work. Further, it would be expensive and unrealistic to collect the required datasets to employ data-based methods to entire bridge networks. Recently, a population-based structural health monitoring (PBSHM) approach was proposed that seeks to leverage the data available for SHM problems by pooling together similar structures with their datasets. The PBSHM approach could be valuable in bridge SHM, enhancing the datasets available for 'populations of bridges. The PBSHM approach for assessing the similarity of bridges has been considered before and was shown to be useful for identifying similar and different bridge types. However, no data were considered in the previous work, and similarity metrics were only qualified using engineering judgement. For the PBSHM approach to be useful in bridge SHM, there is still a need to check that 'similar' bridges have similar responses for transfer learning to be feasible. This paper expands upon previous work and provides originality by investigating if bridges identified as similar also exhibit similar responses. The PBSHM derived similarity metrics convey the topological similarity between structures, and mode shapes are identified as being a topologically sensitive bridge response. Therefore, a modal test campaign is carried out for a set of six real bridges, and Operational Modal Analysis is used to identify modal responses from each of the bridge decks. The Modal Assurance Criterion is used to evaluate the similarity between the mode shapes from pairs of bridges and is subsequently compared to the similarity metrics evaluated between those bridges. The similarity metrics were found to be reflective of the similarities identified between the respective bridges' mode shapes for bridges of the same and different types. The significance of this finding is that it is an important step towards validating the PBSHM comparison approach for identify similar structures where transfer learning might be attempted. [ABSTRACT FROM AUTHOR]

In this paper, an alternative to solving Bayesian inverse problems for structural health monitoring based on a variational formulation with so-called transport maps is examined. The Bayesian inverse formulation is a widely used tool in structural health monitoring applications. While Markov Chain Monte Carlo (MCMC) methods are often implemented in these settings, they come with the problem of using many model evaluations, which in turn can become quite costly. We focus here on recent developments in the field of transport theory, where the problem is formulated as finding a deterministic, invertible mapping between some easy to evaluate reference density and the posterior. The resulting variational formulation can be solved with integration and optimization methods. We develop a general formulation for the application of transport maps to vibration-based structural health monitoring. Further, we study influences of different integration approaches on the efficiency and accuracy of the transport map approach and compare it to the Transitional MCMC algorithm, a widely used method for structural identification. Both methods are applied to a lower-dimensional dynamic model with uni- and multi-modal properties, as well as to a higher-dimensional neural network surrogate system of an airplane structure. We find that transport maps have a significant increase in accuracy and efficiency, when used in the right circumstances. [ABSTRACT FROM AUTHOR]

Vision-based methods have shown great potential in vibration-based structural health monitoring (SHM). However, these methods are not standard practices yet, since their accuracy and robustness may be influenced by extreme environmental conditions. To this end, this paper proposed a method, named dynamic regularized total variation correlation filter (DTVCF). In DTVCF, an effective optimization problem, which contains the space and structural shape information, is defined for dynamically updating the regularization weight map. Then the regularization weight map is smoothed by a total variation (TV) optimization. These are to better track the dramatically changing or almost invisible structural shape, caused by extreme environmental conditions. Moreover, efficient subpixel image registration (ESR) is used in each tracked region of interest (ROI), over time, to achieve subpixel accuracy. The superiority of DTVCF was validated in extreme environmental conditions. DTVCF could achieve subpixel level structural displacement estimation with high accuracy. Furthermore, DTVCF could process approximately 9.00 frame/s, and 3.50 frame/s in two shaking table tests, indicating its high efficiency for SHM applications. [ABSTRACT FROM AUTHOR]

Detecting anomalies in the vibrational features of age-old buildings is crucial within the Structural Health Monitoring (SHM) framework. The SHM techniques can leverage information from onsite measurements and environmental sources to identify the dynamic properties (such as the frequencies) of the monitored structure, searching for possible deviations or unusual behavior over time. The Temporal Fusion Transformer (TFT) network is a deep learning algorithm designed for multi-horizon time series forecasting and initially tested on electricity, traffic, retail, and volatility problems. In this paper, it is applied to SHM. More precisely, the TFT approach is adopted to investigate the behavior of the Guinigi Tower located in Lucca (Italy) and subjected to a long-term dynamic monitoring campaign. The TFT network is trained on the tower's experimental frequencies enriched with other environmental parameters. The transformer is then employed to predict the vibrational features (natural frequencies, root mean squares values of the velocity time series) and detect possible anomalies or unexpected events by inspecting how much the actual frequencies deviate from the predicted ones. The TFT technique is used to detect the effects of the Viareggio earthquake that occurred on 6 February 2022, and the structural damage induced by three simulated damage scenarios. [ABSTRACT FROM AUTHOR]

Continuous monitoring of acoustic events in concrete such as cracking and impacting, and providing their location information is a significant topic in structural health monitoring (SHM). This paper develops a combined technique of concrete implantable cube (CIC) and probabilistic localization method to monitor acoustic sources on concrete slabs. The CIC that integrates an octahedron-shaped piezoelectric sensor array can be implanted in concrete structures in long term and supply an omni-dimensional sensing capacity. Aiming at the designed sensor array and the impacts of randomness and heterogeneity of concrete material on the localization accuracy, a probabilistic localization method was proposed. To demonstrate the feasibility of the proposed technique, an experiment including 48 impact tests was conducted on a concrete slab specimen. The acoustic signals received by the implanted CIC were used to reconstruct localization maps to predict the impact areas. The results were quantified using three matrixes including accuracy, precision, and recall; consequently, the accuracy obtained the highest average score close to 0.98 and performed most steadily. In addition, to optimize the proposed technique furtherly, the sensitivity of two key parameters associated with this method were discussed. Overall, the experimental study showed a great potential of this developed technique for long-term monitoring acoustic events on concrete structures. [ABSTRACT FROM AUTHOR]

Research developments for structural dynamics in the fields of design, system identification and structural health monitoring (SHM) have dramatically expanded the bounds of what can be learned from measured vibration data. However, significant challenges remain in the tasks of identification, prediction and evaluation of full-scale structures. A significant aid in the roadmap to the application of cutting-edge methods to the demands of in-service engineering structures, is the development of comprehensive benchmark datasets. With the aim of developing a useful and worthwhile benchmark dataset for structural dynamics, an extensive testing campaign is presented here. This recent campaign was performed on a decommissioned BAE system Hawk T1A aircraft at the Laboratory for Verification and Validation (LVV) in Sheffield. The aim of this paper is to present the dataset, providing details on the structure, experimental design, and data acquired. The collected data is made freely and openly available with the intention that it serve as a benchmark dataset for challenges in full-scale structural dynamics. Here, the details pertaining to two test phases (frequency and time domain) are presented. So as to ensure that the presented dataset is able to function as a benchmark, some baseline-level results are additionally presented for the tasks of identification and prediction, using standard approaches. It is envisaged that advanced methodologies will demonstrate superiority by favourable comparison with the results presented here. Finally, some dataset-specific challenges are described, with a view to form a hierarchy of tasks and frame discussion over their relative difficulty. • Provides a new dataset for experts to benchmark vibration-based techniques. • A broad-scoping testing regime on a jet, for a variety of benchmarking schemes. • The unique challenges of the dataset are highlighted using preliminary analysis. [ABSTRACT FROM AUTHOR]

• Energy distribution characteristics of displacement and acceleration are revealed. • The estimated covariances on modal flexibility are used for comparison. • Lower uncertainty is obtained for modal parameters when using displacement. • Displacement response is more suitable for structural flexibility identification. For civil engineering structures, the dynamics-based structural health monitoring is commonly based on inverse analysis of structural acceleration response data to identify the structural parameters. As the measurement of structural displacement response becomes more feasible and cost-effective thanks to advances in computer vision techniques, a comparison of the displacement and acceleration response data for structural identification has become of considerable interest among researchers. This paper presents a comprehensive analysis and comparison of structural flexibility identification based on displacement versus acceleration response data through uncertainty quantification technique. Firstly, theoretical derivation reveals that the energy spectral density of acceleration response is equal to the fourth power of the frequency of the energy spectral density of displacement response, which affects the distribution of measurement noise across different frequency bands. Secondly, the uncertainty quantification method for modal flexibility from subspace-based system identification is utilized to compare the derivation process of covariance estimation on modal flexibility. Finally, numerical analysis and experimental example are carried out to compare the structural flexibility identification results when using the displacement versus acceleration. It shows that more precise identification results of lower-order modal parameters are obtained when using displacement, which further improves the precision of modal flexibility identification. The displacement is more suitable for identifying the modal flexibility when the measurement noises are at the same level. [ABSTRACT FROM AUTHOR]

Interfacial debonding identification within reinforced concrete (RC) structures is an ongoing and critical issue in structural health monitoring. In this paper, a novel nonlinear harmonics-based method for identifying, localizing, and quantifying interfacial debonding in RC beams is presented. The mode conversion phenomenon and contact acoustic nonlinearity of guided waves at interfacial debonding region are thoroughly examined using a combined theoretical, numerical, and experimental investigation, which sheds light on the propagation behavior of the first and second harmonics in RC beams with single interfacial debonding. Inspired by the phase-reversal approach, the temporal separation of the second harmonic is achieved by integrating the excitation results from both the original and phase-reversed signals. The theoretical investigation is conducted to correlate the arrival time of harmonics with structural damage, and its accuracy is validated by the numerical group velocity inversion and wavenumber-frequency analysis. The experimental evaluation, involving six RC beam specimens with varying debonding length, further demonstrates the validity of the method for precise interfacial debonding identification. The experimental and numerical evidence confirms the effectiveness of the proposed method, highlighting its potential for practical application. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring has the potential to enhance the safety and serviceability of our aging infrastructures by detecting anomalies at an early stage. Bayesian Dynamic Linear Models (BDLM) have been shown to be effective at detecting anomalies by extracting structural patterns and latent variables from complex and noisy time series. However, the autoregressive component modelling the stationary prediction errors in most BDLM has a tendency to wrongfully capture patterns that should be attributed to anomalies, and thus hinders their detectability. This paper proposes a new bounded autoregressive (B A R) component, which imposes constraints on the autoregressive latent process with a new mixture Rectified Linear activation Unit. The B A R component is probabilistically verified on synthetic data using a new F1t metric, and is validated using real observations collected on a bridge and on a dam located in Canada. The experimental results demonstrate that the B A R model surpasses the performance of the existing autoregressive component with (1) an improved accuracy at estimating hidden states, (2) an early detection of anomalies, (3) a capacity to detect smaller anomaly magnitudes, and (4) the ability to control the tradeoff between the anomaly detectability and the false alarm rate. [ABSTRACT FROM AUTHOR]

• A high-SNR ultrasonic method is developed for the full matrix capture dataset. • The method shows strong advantage for weakly heterogenous medium in image quality. • The method is suitable for the multilayer medium and is competitive in efficiency. • The method can adapt to the probes with various shapes. • Simulation and experiments are conducted for the method validation. Ultrasonic full matrix capture (FMC) dataset has been widely used in various fields including biomedical imaging, non-destructive detection and structural health monitoring. However, the measurement medium is usually weakly heterogeneous, which leads to the speckle artifacts in the imaging result of the conventional methods and reduces the signal-to-noise ratio (SNR). To overcome this deficiency, this paper proposes a novel high-SNR method to recover the targets in the weakly heterogeneous medium clearly from the FMC dataset. Singular value decomposition (SVD) is utilized to extract the signal subspace, and it transforms the passive reflector detection problem into an active source location problem. Then, the beamforming is conducted with the cross-spectral matrix calculated by the signal subspace. Furthermore, the deconvolution process is implemented with the non-stationary point spread function (PSF) for the further suppression of speckle artifacts. In addition, the phase shift factor is introduced to make the proposed method adapt to the multilayer medium. Three existing imaging methods for FMC datasets are introduced to make comparisons with the proposed method. The simulation and experiment results demonstrate that the proposed method can recover the image with much higher SNR (>20 dB) wherein the speckle artifacts in the background are well suppressed. Additionally, the proposed method is more efficient than total focusing method, and it only takes about 2 s to obtain the high-quality imaging result. [ABSTRACT FROM AUTHOR]

Fatigue damage prognosis studies typically involve updating a degradation model based on the collected measurements. The updated model can accurately depict the past degradation process, while it is always assumed to be capable of describing the future damage progression, and thus can be used for remaining useful life (RUL) prediction. However, this assumption cannot always hold true, due to the uncertainties arising from the degradation process. In this context, this paper proposes a scheme of prognostic-aided model updating in a particle filter (PF)-based prognostic framework, which empowers the updated model to capture both the historical and future degradation process, and consequently, enhances the prognostic performance. First, two degradation models are formulated using the physics-based damage state and the data-driven health indicator (HI), respectively. The former is then re-formulated in state space by incorporating a measurement model. Next, the state space model is updated by the latest measurement through PF, providing the RUL and lifetime percentage (LP) distributions. Finally, the particles of damage state and parameters are modified by comparing the LP distributions from the updated model and the latter degradation model. The feasibility and robustness of the proposed method are validated by an experimental study, where an aluminum lug joint subject to fatigue crack growth is monitored by a guided wave measurement system. [ABSTRACT FROM AUTHOR]

Health monitoring technologies, empowered by sensor-driven information and model updating, play an important role in assessing the status of civil structures and detecting anomalies. However, significant domain discrepancies exist in the distribution of physical parameters between numerical structural models and their real-world counterparts. As a result, many health monitoring theories struggle to be effective in practice, even if they perform perfectly in the simulated data. To bridge the domain discrepancies, this paper proposes an unsupervised domain adaptation approach based on an adapted cycle-consistent generative adversarial network (CycleGAN) that incorporates physical constraints and a self-attention mechanism. The approach focuses on the mutual transformation of the multi-channel time series obtained from numerical models and actual structures. Specifically, the physical constraints are derived from the governing equation of linear dynamic systems, while the self-attention mechanism is achieved by adding transformer structures to both the generator and discriminator. Through free-vibration experiments on a steel beam and a large-scale steel bridge model, the physical constraints and transformer structures have proven beneficial for improving the learning capability and training stability of the GAN model. Furthermore, the proposed approach is not only verified as effective in transforming acceleration responses between the test structures and their corresponding finite element models in both time and frequency domains but has also been shown to reproduce mode shapes accurately. [ABSTRACT FROM AUTHOR]

• An AEKF-UM approach is proposed for identifying time-variant parameters with unknown mass distribution. • A real-time updating procedure is presented for improving the process and measurement noise covariance matrices. • An index is defined basing on dramatic increase of measurement noise covariance for damage instant detection. • The covariance resetting technique is used to enhance the tracking capability for effectively capturing the time-varying parameters. Although the extended Kalman filter (EKF) provides a promising way for structural state estimation, it cannot effectively track time-varying parameters online. Besides, the structural mass distribution is usually assumed to be known in advance for many EKF-based methods, limiting their applications. In this paper, by using limited observations, an adaptive EKF with unknown mass coefficients (AEKF-UM) approach is proposed for the identification of time-variant parameters and mass distribution at the same time. A real-time updating procedure is presented for improving the process and measurement noise covariance matrices at each time step to assure the stability and accuracy of convergence results. Based on the dramatic increase of measurement noise covariance, an index is defined for determining the damage instant. A covariance resetting technique is then used to enhance the tracking capability for the purpose of effectively capturing the time-varying parameters. The unknown mass coefficients can be estimated at the same time by adding them into the extended state vector. To validate the effectiveness of the proposed approach, two numerical cases are considered, i.e. (i) the Phase I ASCE structural health monitoring benchmark building structure, and (ii) a four-story nonlinear structure equipped with a magneto-rheological (MR) damper. Experimental tests on a four-story building model subject to base excitation are also conducted to investigate the performance of the proposed approach. Results show that the proposed approach is capable of satisfactorily tracking abrupt changes of stiffness parameters with unknown mass distributions. [ABSTRACT FROM AUTHOR]

• A novel deep neural network model is proposed, which uses two branches to reconstruct the real and imaginary parts of the signal, solving the key problem that deep neural network models are difficult to extract effective features from compressed signals in the time domain. • A large-scale frequency transformation matrix is decomposed into the multiplication of two small-scale matrices, greatly reducing the number of trainable parameters. • A Fourier Unit is introduced and its feature extraction ability is improved, further enhancing the feature extraction ability of the whole model without significantly increasing the model parameters. • The proposed method is capable of reconstructing vibration responses without relying on sparsity assumptions. Compressive sensing (CS) has become a promising technique for addressing the transmission and storage challenges associated with big data in structural health monitoring. However, classic CS methods require signals to have good sparsity, limiting their performance in low-sparse situations. In order to address this challenge, this paper proposes a deep learning-based CS algorithm for high accuracy reconstruction of structural vibration responses without the assumption of sparsity. The key innovations of the proposed method can be mainly divided into three points. Firstly, a novel deep neural network model is proposed, which uses two branches to reconstruct the real and imaginary parts of the signal in the frequency domain, solving the key problem that deep neural network models are difficult to extract effective features from compressed signals in the time domain. Secondly, a large-scale frequency transformation matrix is decomposed into the multiplication of two small-scale matrices, greatly reducing the number of trainable parameters, and solving the problem that the original frequency transformation block cannot be directly used to extract global frequency-domain features of structural vibration responses due to the large number of parameters. Thirdly, a Fourier Unit is introduced and its feature extraction ability is improved, further enhancing the feature extraction ability of the whole model without significantly increasing the model parameters. The model does not use the adversarial training strategy, and the training process is simple and stable. It also does not require a large amount of adjustment of hyper-parameters. The number of trainable parameters of the model is only about 4 million, and the model training can be carried out with low-cost hardware. The effectiveness of the proposed method is verified by the low sparsity vibration response of a laboratory grandstand simulator. The maximum sparsity level of the vibration response of the simulator with 30 nodes is 0.381, which is larger than the sparsity level of the vibration response of common large-scale civil structures, that is, the sparsity is smaller and the reconstruction difficulty is greater. Experimental results show that the reconstruction error of the two classic CS algorithms is more than 3 times higher than that of the proposed method across all compression ratios. The identification results of the modal parameters of the original response and the reconstructed response show excellent consistency in their frequencies and vibration modes. The maximum relative error of the first 12 frequencies is only 0.038%, and the modal assurance criterion of the first 12 mode shapes is at least 0.9962. Overall, in large-scale low-sparse situations, the proposed method has high accuracy and high computational efficiency in reconstructing structural vibration responses, which can greatly alleviate the transmission and storage problems of monitoring big data with very low hardware requirements. [ABSTRACT FROM AUTHOR]

• A Hierarchical Bayesian formulation is proposed for updating structural models. • An Expectation-Maximization strategy is offered to mitigate computational costs. • Difficulties of treating close modes are resolved using a geometric approach. • Applications to an offshore platform are provided to verify the proposed method. Updating Finite Element (FE) models based on modal data has become a standard tool for condition monitoring and damage detection in structures. The basis of this approach is to minimize the discrepancy between the experimental and analytical modal frequencies and shapes through a particular loss function derived through deterministic or probabilistic methods. However, the weighting of modal features can affect the identified structural model parameters, which requires proper treatment. This paper revisits this problem by proposing a novel hierarchical Bayesian formulation, wherein the covariance matrix of modal residuals is identified from the data, leading to automated weighting of the modal residuals. As the calculation of the posterior distribution encounters computational overheads, an efficient Expectation-Maximization algorithm is proposed to approximate the optimal values of the parameters. Bayesian information criterion is employed to select the most plausible layout for the prediction errors covariance matrix. A new strategy is also established for handling dynamical modes with close modal frequencies. Finally, the proposed approaches are examined using simulated and real data from FINO3 offshore platform. As a result, reasonable estimations with desirable uncertainty bounds are acquired. Although the weighting of modal features varies on a case-to-case basis, it is observed that the proposed method might lead to non-trivial and interesting weighting factors according to the data-driven uncertainty of modal residuals. [ABSTRACT FROM AUTHOR]

• Combination of CAN and LDR to detect kissing bonds in adhesive joints. • Nonlinear acoustics showing second harmonic and frequency downshifts of the LDR. • Harmonic Balance Method used to solve the nonlinear Duffing's equation for the LDR. • Nonlinear contact bilinear model developed for FE analysis of the adhesive joint. Adhesively bonded joints are susceptible to contamination of surfaces during manufacture and environmental deterioration in real operating conditions. These may cause the generation of so-called "kissing bonds" that can dramatically alter the strength of the joint leading to premature failure. Nonlinear acousto-ultrasonic (AU) techniques have shown great potential for monitoring kissing bonds with piezoelectric sensors permanently installed on the structural joint, thus enabling online and in-situ inspection. This paper investigated the combined contact acoustic nonlinearity (CAN) and local damage resonance (LDR) effects in the presence of kissing bonds in adhesive joints. Experimental nonlinear AU tests showed the formation of LDR frequency down-shifts and "jumps" of the fundamental damage resonance in adhesively bonded aluminium joints, with the kissing bond located internally to the overlapping region between the two adherends. These results were supported by a theoretical model based on the solution of the nonlinear Duffing's equation, under the assumption that the debonded region is a damped nonlinear harmonic oscillator subject to harmonic forcing. The Harmonic Balance Method was used to solve the nonlinear differential problem, showing the generation of frequency down-shifts via the dependence of the ratio between the excitation and LDR frequencies with the amplitude of the fundamental damage resonance. Additionally, two-dimensional finite element simulations using a reduced order model based on the Craig-Bampton technique were carried out to support experimental AU tests for the identification of the LDR frequency and the generation of nonlinear resonance effects. Good agreement between analytical, numerical, and experimental results revealed that a monitoring approach combining CAN and LDR is an extremely efficient and sensitive tool for ensuring integrity and safety of structural adhesive joints. [ABSTRACT FROM AUTHOR]

• A hybrid approach based on modulation transfer and vibroacoustic modulation is used for damage detection and localisation. • The damage indicators are calculated from frequency components obtained by the proposed algorithm. • The experiment is conducted on both the damaged and undamaged beams. • The complementarity of methods based on modulation transfer and vibroacoustic modulation phenomena. • Manifestation of various non-linear mechanisms responsible for modulation transfer and vibroacoustic modulation. This paper describes a hybrid approach to fault detection by using modulation transfer and vibroacoustic modulation. The work aims to locate faults and study the components of the response signals. This research is relevant for two reasons. The first is the small amount of research concerning hybrid modulation methods in fault localisation. The second is related to the insufficient knowledge of the response signals composition, which is crucial for fault detection procedures. Thus, the tests were carried out on two beams made of aluminium, one damaged and the other undamaged. The tested structures were excited by two sources. The first was a piezo stack actuator that introduced an amplitude-modulated wave with a modal frequency carrier. The second source was a piezoelectric exciter that introduced a harmonic ultrasonic wave. The tests were performed for several amplitude levels applied to the piezo stack actuator. The recorded responses were processed to calculate frequency spectra and prepare damage index trend charts. The spectrum analysis revealed the presence of components related to the modulation transfer and vibroacoustic modulation phenomenon. The trend analysis indicated that modulation transfer can be applied for damage localisation and complement vibroacoustic modulation techniques. [ABSTRACT FROM AUTHOR]

Structural identification is an essential process in structural health monitoring, condition assessment and structural safety evaluation. This inverse problem becomes more challenging when information on the dynamic loads is missing or not fully known. Hence, it is important to establish methods to identify structural parameters and dynamic loads simultaneously from the measured structural responses which are easy to obtain compared to dynamic loads. This paper proposes a novel method to identify simultaneously structural parameters and dynamic loads from structural responses measured on a limited set of degrees-of-freedom. Firstly, an objective function is defined as the difference between the measured structural responses and the theoretically computed responses, and then the derivative of the residual function with respect to structural parameters is calculated numerically using the forward-difference method. The derivative of the residual function with respect to the external dynamic loads is computed using the state-space formulation and the system matrix composed of Markov parameters to facilitate the derivative-based identification. Secondly, the nonlinear optimization problem is solved using the Levenberg–Marquardt algorithm. Several numerical examples are analysed to demonstrate the effectiveness and robustness of the method. Finally, the effect of initial estimates of the parameters and dynamic loads and the effect of measurement noise, as well as the effects of number of measurements are investigated. The proposed method is also shown to achieve a satisfactory solution even when the initial estimates of parameters and dynamic loads are far from their true values. • The structural parameters and the dynamic loads are identified simultaneously. • A state-space formulation is used to facilitate the identification. • The Levenberg–Marquardt algorithm is used to solve the optimization problem. • The effects of noise, number of measurements and excitations are analysed. [ABSTRACT FROM AUTHOR]

In this paper, the problem of bridge health monitoring is formulated as an unsupervised semantic segmentation where the aim is to identify the onset of structural damage by detecting unexpected irregularities in the time series data. In particular, the main objective of this study is to approach this problem through an indirect sensing framework based on the concept of vehicle-mounted scanning. Thus, the sole source of information is the acceleration responses of the moving vehicle passing over the bridge. This is achieved through automatic feature learning by adopting the concept of matrix profile to conduct an all-pair-similarity search for a given time series, where the location of the contextual change is determined by the corresponding Corrected Arc Crossings (CAC) profile. The performance of the method is first evaluated through numerical investigations for different damage cases where the effect of operational uncertainties such as road roughness, and variability in vehicle properties are considered. Further, the method is experimentally evaluated using a lab-scale bridge model with different damage cases. Through these investigations, it is consistently observed that the location of the change point always coincides with the minimum point of the CAC profile. The proposed framework has three major advantages making it more appealing compared to the competing techniques. First, it has been fully formulated in the context of unsupervised learning where no access to labeled data is required. Second, it is capable of working with a limited amount of data yet providing promising results. Finally, it can be easily applied to online settings where data is streaming in real-time. These successful results demonstrate the potential of the proposed method as a cost-effective and rigorous bridge inspection tool and open up opportunities in the field of indirect bridge health monitoring. [ABSTRACT FROM AUTHOR]

Data-driven machine-learning algorithms generally suffer from a lack of labelled health-state data, mainly those referring to damage conditions. To address such an issue, population-based structural health monitoring seeks to enrich the original dataset by transferring knowledge from a population of monitored structures. Within this context, this paper presents a transfer learning approach, based on domain adaptation, to leverage information from completely-labelled bridge structure data to accurately predict new instances of an unknown target domain. Since intrinsic structural differences may cause distribution shifts, domain adaptation attempts to minimise the distance between the domains and to learn a mapping within a shared feature space. Specifically, the methodology involves the long-term acquisition of natural frequencies from several structural scenarios. Such damage-sensitive features are then aligned via domain adaptation so that a machine-learning algorithm can effectively utilise the labelled source domain data and generalise well to the unlabelled target-domain data. The described procedure is applied to two case studies, including the Z24 and the S101 benchmark bridges and their finite element models, respectively. The results demonstrate the successful exchange of health-state labels to identify the damage class within a population of bridges equipped with SHM systems, showing potential to reduce computational efforts and to deal with scarce or poor data sets in application to bridge network monitoring. • A data-based domain adaptation framework is applied to bridge monitoring. • Domain Adaptation aligns and transform damage-sensitive features into a shared feature space. • Joint Domain Adaptation and Normal Condition Alignment are compared and discussed. • The KNN algorithm uses the transformed features to perform damage detection and classification. • The method is applied to transfer health-state labels across real bridges and finite element models. [ABSTRACT FROM AUTHOR]

Accurate damage quantification for an individual aircraft by using structural health monitoring (SHM) technology poses a persistent challenge in practical engineering. Existing damage quantification models typically rely on prior data acquired from ground design and tests. However, an individual aircraft is subject to diverse uncertainties throughout its whole service time, such as time-varying environmental and operational conditions, different flight missions, and different damage morphologies. Consequently, employing a prior trained model for damage quantification inevitably introduces errors, thereby limiting the engineering applicability of SHM. To improve the damage quantification accuracy for individual aircraft under the influence of uncertainty, a new whole lifetime data-based damage quantification model hierarchical evolution mechanism is proposed in this paper. Multi-source data from the design, service, and maintenance stages of individual aircraft are adopted to continuously evolve the probabilistic damage diagnosis model, enabling it to characterize the specific data distribution and track the damage propagation. The proposed method is validated through fatigue tests of a landing gear beam structure, which is an important aircraft load-carrying component, with guided wave monitoring based hidden Markov model quantifying its crack propagation. The validation result demonstrates the effectiveness of the proposed method in ensuring the long-term reliability of the model and achieving more accurate damage quantification for an individual aircraft. [ABSTRACT FROM AUTHOR]

Nonlinearity and high dimensionality emerge as two primary challenges in the realm of system identification within the context of structural health monitoring (SHM) applications. Particle filter (PF) has been demonstrated efficient for nonlinear identification, but it suffers from the curse of dimensionality and behaves poorly in high-dimensional problems. The idea of state and measurement partitioning has been used in many PF algorithms to simplify high-dimensional identification problems into the identification of several lower-dimensional subgroups, but with very few applications to SHM problems. In this context, by combining multiple particle filters (MPF) with the decay of correlations property, this paper develops a novel multiple local particle filter (MLPF) for high-dimensional identification problems. A whole state vector is partitioned into several state subgroups, each consisting of fewer state components and then estimated by one PF through a novel likelihood including the local state and measurement vectors. The feasibility and efficiency of the proposed method are tested through a benchmark toy example, a case study of a twenty-story Bouc-Wen frame structure under ground motion, and an experimental study of fatigue delamination growth in composites. [ABSTRACT FROM AUTHOR]

• A synchronization approach for structural health monitoring data is introduced. • Time lags are estimated from the slopes of cross spectral density phase plots. • The proposed approach is applied at the post-processing stage. • Time lag estimation is robust in the presence of non-proportional damping. • The applicability of the approach is tested with simulations and real-world data. Since the advent of structural health monitoring (SHM), vibration analysis has been one of the most popular strategies for yielding modal information of structures, such as eigenfrequencies and mode shapes. As a result of the relatively high sampling rates adopted in vibration analysis, obtaining accurate modal information requires absolute synchronization of structural response data. Despite the advances in sensing technologies and synchronization protocols, synchronization discrepancies in structural response data may occur due to drifting in internal clocks of independent data acquisition units. Particularly in wireless SHM, in which each wireless sensor node essentially operates as an independent data acquisition unit, mitigating clock drifting has been an area of intensive research. This paper addresses the issue of data synchronization from a physics-based perspective, aiming to add an additional synchronization check at the post-sampling stage. Specifically, a frequency-domain synchronization approach is reported, which builds upon the relationship between time lags in structural response data and the cross spectral density phase angles. Validation tests are conducted using simulations of a multi-degree-of-freedom oscillator and data collected from a full-scale road bridge, demonstrating the capability of the approach to estimate time lags between SHM data even in the presence of non-proportional damping, thus overcoming a common limitation of other synchronization methods. The frequency-domain synchronization approach is intended to complement synchronization protocols in extracting accurate modal information. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) strategies seek to evaluate, predict, and maintain structural integrity, to improve the safety and design service life of structures in operation. Many of these strategies involve monitoring changes in structural dynamics, as damage can affect modal properties and present as changes in the characteristics of the resonance peaks of the frequency response function (FRF). While recent advances have improved the safety and reliability of structures, a number of challenges remain, impeding the practical implementation and generalisation of these systems. Like damage, benign variations, such as those caused by changes in temperature or other environmental fluctuations, can affect dynamic properties, making it difficult to distinguish between damage and normal operating conditions. In addition, newly-deployed structures can have insufficient data to describe the normal operating conditions (i.e., data scarcity), which can impair the development of data-based prediction models. Another common challenge is data loss (i.e., data sparsity), which may result from transmission issues, sensor failure, a sample-rate mismatch between sensors, and other causes. Missing data in the time domain will result in decreased resolution in the frequency domain, which can impair dynamic characterisation. For situations that may benefit from information sharing among datasets, e.g., population-based SHM of similar structures, the hierarchical Bayesian approach provides a useful modelling structure. Hierarchical Bayesian models learn statistical distributions at the population (or parent) and the domain levels simultaneously, to bolster statistical strength among the parameters. As a result, variance is reduced among the parameter estimates, particularly when data are limited. In this paper, a combined probabilistic FRF model is developed for a small population of nominally-identical helicopter blades, using a hierarchical Bayesian structure, to support information transfer in the context of sparse data. The modelling approach is also demonstrated in a traditional SHM context, for a single helicopter blade exposed to varying temperatures, to show how the inclusion of physics-based knowledge can improve generalisation beyond the training data, in the context of scarce data. These models address critical challenges in SHM, by accommodating benign variations that present as differences in the underlying dynamics, while also considering (and utilising), the similarities among the domains. [ABSTRACT FROM AUTHOR]

High-speed trains inevitably experience the impact of complex temperature loads during operation, which can easily result in cracks. Structural health monitoring methods based on Lamb waves are affected by the impact of temperature on their propagation mechanism, leading to difficulties in constructing accurate damage diagnostic models. This paper proposes a crack damage identification method based on adaptive multi-scale sample entropy under variable temperature environment. A sliding window method is proposed to obtain multiple wave packets in a Lamb wave, which avoids the disadvantage of insufficient information of a single wave packet and improves the robustness and reliability of diagnosis. A variance-based multiscale transform method is proposed to process Lamb wave signals, which reduces the sensitivity of Lamb wave signals to temperature compared to the traditional mean-based multiscale transform method. Lamb waves in different sliding windows are transformed at multiple scales to extract damage Multi-scale sample entropy (MSE) at different scales. The Quantum genetic algorithm (QGA) is introduced to optimize sliding windows and MSE, achieving adaptive MSE extraction under variable temperature environments. Based on the advantage of adaptive multiscale entropy, a quantitative crack diagnosis model is established. To verify the effectiveness of the proposed method, crack detection experiments under variable temperatures were conducted. The results show that the proposed adaptive MSE has good resistance to temperature changes and can accurately identify crack length. [ABSTRACT FROM AUTHOR]

The current image processing techniques heavily rely on classic windows for suppressing spectral leakage. However, the limited capability of these classic windows to suppress spectral leakage restricts their application in the field of high-precision measurement. This paper proposes a novel family of windows capable of suppressing spectral leakage associated with the Moiré fringes pattern, thereby enhancing the accuracy of lithography alignment. Specifically, a novel two-dimensional Hanning self-convolution window is introduced, which is derived from the Hanning window to inherit its advantage of ultra-rapid sidelobe decay. The behaviors of the main lobe and desirable sidelobes of the 1st- to 4th-order are examined. Additionally, a novel fringe phase extraction algorithm is established based on the proposed window, which demonstrates high accuracy and efficiency. Simulation and experimental results validate the feasibility and practicality of the proposed window. In particular, the proposed window outperforms classic windows in suppressing spectral leakage, thereby successfully enhancing the phase extraction accuracy to achieve high-precision misalignment measurement at the sub-2-nm level (1.47 nm @ 3σ criterion). The proposed method can be easily extended to other real engineering applications utilizing fringe to achieve high-precision measurements, such as fringe projection profilometry, damage detection, and visual mechanical vibration monitoring. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) systems have been increasingly employed to continually assess the current state of bridges. However, the vast amounts of sensor data generated by SHM systems, along with constantly changing environmental and operational conditions, make structural damage assessment a computationally demanding and challenging task. Traditional data-driven approaches primarily utilise machine learning methods for pattern recognition and feature extraction to address this issue. This paper introduces a methodology for assessing bridge conditions using a probabilistic temporal autoencoder (PTAE). The proposed approach effectively extracts features and captures temporal relationships in multi-sensor data collected only during train crossings. By calculating the reconstruction loss and KL divergence-based of damage features, the methodology enables the identification of potential damage of a monitored bridge. An Exponentially Weighted Moving Average (EWMA) filter and a control chart-based threshold mechanism are applied to further refine the damage assessment process, facilitating the distinction between healthy and progressively deteriorating damage cases. The proposed method is adaptable to various monitoring scenarios and sensor configurations, and robust to varying operational and environmental conditions. The effectiveness of the methodology is assessed using numerically generated data and validated with real-world data from the KW51 bridge. The results demonstrate that the proposed method can detect damage with a limited number of sensors, making it a valuable approach to enhance bridge safety. • PTAE-based method processes multi-sensor SHM data for accurate damage assessment under varying conditions. • Reconstruction loss and KL divergence-based features enable potential bridge damage detection during train crossings. • EWMA filter and control chart threshold refines assessment, differentiating healthy and deteriorating cases. • Adaptable, robust methodology suits various monitoring scenarios and sensor configurations, enhancing bridge safety. • The method's effectiveness is validated through numerical and real-world KW51 bridge data, highlighting its practical applicability. [ABSTRACT FROM AUTHOR]

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials. Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view – cross sectional analysis – of these complex structures during assembly and curing. In this paper, we enhance the quality assurance of WTB manufacturing utilising FMCW radar as a non-destructive sensing modality. Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave. (2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD). This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies. (3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD). EAD takes advantage of the Rayleigh distribution, which characterises an instantaneous amplitude signal. The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods. This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application. The code and dataset will be made publicly available. The code and dataset are available here 1 1 https://github.com/FrusqueGaetan/Focus_SVDD_EAD. [ABSTRACT FROM AUTHOR]

Structural Damage Identification (SDI) is a crucial branch in the field of structural health monitoring, providing an essential support for the safe operation of structures. In this paper, a novel structural damage identification method based on a surrogate-assisted evolutionary optimization algorithm is proposed. This method incorporates cost-effective surrogate modelling techniques and swarm intelligence optimization algorithms with powerful global optimization capabilities. Three popular surrogate models are fused based on a weighted average strategy to construct an integrated surrogate model with stronger generalization ability and higher accuracy. In addition, the self-designed movement strategy is more suitable for the characteristics of Termite Life Cycle Optimizer (TLCO), allowing the improved TLCO to have stronger robustness and the capability to escape from local optimum. The effectiveness of the proposed method for solving the SDI problem is confirmed in a damaged dam model with different complexities. Some important findings are as follows: (i) Compared with the conventional SDI methods, which directly combines optimization algorithms and finite element models, the proposed method maintains reliable accuracy, while improving computational efficiency by a factor of more than 100. (ii) There is no direct relationship between the accuracy of the surrogate models and the effectiveness of the SDI method based on their combination with ITLCO. The proposed method can serve as a promising tool for damage identification in large-scale structures. [ABSTRACT FROM AUTHOR]

Accurate condition monitoring has been a major challenge among railroad management authorities as they work to minimize collisions that lead to fatalities or damage to railroads infrastructure. Hence, research and technological developments in railroad maintenance and inspection are vital. Several research studies, on the inspection and defect detection techniques for railroads have been conducted by scholars. Despite the significant advancements made in this area, extensive studies are still required to enhance the accuracy of prognostic methods for railroad structural health monitoring (SHM) and condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising measurement method because of its quick sensing capabilities over long distances and for massive structures. DAS systems are classified according to the optical sensing quality and sensing range. As DAS produces large noisy datasets, in the case of railroad applications, algorithms for precise real-time and reliable analysis are essential. Meanwhile, data-driven and machine learning (ML) methods for defect detection have emerged as valuable approaches for SHM. Engineers and stakeholders can use ML algorithms to examine the large volumes of data produced by SHM systems to identify patterns and behaviors that might not appear in manual inspections. To support more precise and accurate maintenance and inspection for railroad systems, methodologies used to detect, identify, and characterize abnormal conditions in railroads using DAS and signal processing approaches for DAS large size and noisy signals, must be reviewed. Accordingly, in this literature survey, the applications of DAS methods for railroad CM are investigated. Among the variety of DAS methods, optical time domain reflectometry (OTDR) is reviewed in more details, since it is the most common approach in distributed fiber optic sensing. In addition, different OTDR-based DAS research for train tracking and railroad SHM are reviewed, and a comprehensive summary of different railroad defects is provided for further investigation. In all, this review paper provides a comprehensive background on distributed fiber optic sensing, the importance and challenges in railroad continuous CM, and the state-of-the-art application and future roadmap for the application of DAS in the railroad industry. [ABSTRACT FROM AUTHOR]

It has been demonstrated that acoustic-emission (AE), inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an increased sensitivity to damage, as well as an ability to localise its source. There are, however, numerous challenges associated with the analysis of AE data. One issue is the high sampling frequencies required to capture AE activity. In just a few seconds, a recording can generate very high volumes of data, of which a significant portion may be of little interest for analysis. Identifying the individual AE events in a recorded time-series is therefore a necessary procedure for reducing the size of the dataset and projecting out the influence of background noise from the signal. In this paper, a state-of-the-art technique is presented that can automatically identify cluster the AE events from a probabilistic perspective. A nonparametric Bayesian approach, based on the Dirichlet process (DP), is employed to overcome some of the challenges associated with this task. Additionally, the developed model is applied for damage detection using AE data collected from an experimental setup. Two main sets of AE data are considered in this work: (1) from a journal bearing in operation, and (2) from an Airbus A320 main landing gear subjected to fatigue testing. [ABSTRACT FROM AUTHOR]

Effective bridge structural health monitoring systems offer a route to assist in the safe and economic operation of bridges, but have seen limited adoption onto the short and medium-span bridges which represent the majority of our bridge stock. This lack of adoption is in part due to the practical challenges of deploying bridge SHM systems in resource-constrained environments, especially where long-term monitoring at transport network-scales is considered. This paper tackles the problem of implementing an effective sampling method to facilitate long-term, bridge rotation-based damage detection, particularly within the confines of a resource-constrained setting. We demonstrate that leveraging readily available expected traffic-volume information enables the creation of more effective bridge SHM sampling methods by capturing more vehicle crossings in the same time. Furthermore, we introduce an empirically tuned, temperature-capable bridge finite-element model for long-term simulations, incorporating a stochastic vehicle arrival model and a feature error model to allow for the presence of any residual noise/dynamics due to imperfect filtering or inaccuracies in the feature extraction stages. This allowed us to simulate 60 years worth of bridge rotation monitoring data with varying damage severities to evaluate the impact of environmental and operational variations on our proposed expected traffic volume based sampling methods. Our results show that sampling only the peak traffic hours was the most effective for the same reduced 'on' time, i.e. for an 8x reduction in 'on' time only a 1.5x increase in the minimum detectable damage was observed versus a 2x increase for naive random sampling. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the development of ground-based Synthetic Aperture Radars (GBSAR) for the purpose of monitoring structural displacements. GBSAR offers high-resolution monitoring over a wide area and can capture data every few minutes. However, compact high-frequency multiple input multiple output (MIMO) radars have emerged as an alternative for monitoring sub-second displacements, such as structural vibrations. MIMO radar has sub-second acquisition interval. However, it has limited cross-range resolution compared to GBSAR, and interference between antennas and presence of multiple scatterers in the scene can cause strong sidelobes in the processed data. On the other hand, GBSAR utilizes a long synthetic aperture to achieve high cross-range resolution. However, due to its longer data acquisition time compared to MIMO radar, conventional methods are insufficient for detecting scatterers' sub-second displacements that occur during the data acquisition process. This study proposes a method to effectively monitor sub-second or sub-minute displacements using GBSAR signals. The proposed method enhances the conventional radar interferometric processes by employing spectral estimation, allowing for multi-dimensional detection of targets' azimuth angle, linear displacement, and vibrational characteristics. Consequently, this method improves both the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals. The paper presents the theoretical details and mathematical formulations of the proposed method for both MIMO radar and GBSAR imaging modes. To evaluate the effectiveness of the proposed method, numerical simulations and real experiments are conducted. The experimental results validate the capability of the proposed method in both GBSAR and MIMO configuration modes for high-resolution monitoring of fast linear displacements and vibrations. The results exhibit promising signal-to-noise ratio (SNR) and peak-to-sidelobe ratio (PSLR) values. [ABSTRACT FROM AUTHOR]

Vibration measurements for structural health monitoring (SHM) by operational modal analysis (OMA) are classically obtained from sensors that are embedded in or physically attached to the monitored structure, like accelerometers or strain gauges. However, the setup time of these sensors and their restricted number and space coverage limit their monitoring capabilities. Video image-based sensing methods can overcome these shortcomings. With adequate image processing methods, motion signals are extracted from video image flows, which are then processed by system identification methods to estimate modal parameters. In this way, the pixels in selected regions of interest within the images act as a dense network of contactless sensors distributed over the whole structure. In this paper, the efficiency of this video-based approach is demonstrated with laboratory experiments on a cantilever beam, in particular, by evaluating its capability for detecting weak damages mimicked by slight mass modifications. To this end, the steerable filter-based method (ST), that recovers displacements from local phase, is first extended to overcome its motion limitation of one pixel size. Then, the performance of the improved motion extraction method is compared with two other well-established methods in the context of OMA, where natural frequencies, damping ratios and mode shapes with high spatial resolution are estimated together with their uncertainty bounds using covariance-driven subspace identification. The compared methods are evaluated with the help of reference laser displacement measurements as well as a finite element model of the beam, revealing differences in the accuracy of the estimated mode shapes depending on the chosen method for motion extraction. Finally, aiming to investigate early structural damage detection, experiments are carried out under small structural changes and the results are compared to a reference state with the help of estimated uncertainties. Small but statistically significant changes in the modal parameters are detected, showing the potential of the vision based framework for SHM. • Modal parameters are obtained with motion estimation and subspace identification from vision data. • An extension of the local phase computed by the steerable filters to displacements of > 1 pixel shows highest accuracy. • Three motion extraction methods are evaluated for accuracy and precision. • Vision and laser displacement data lead to similar precision of modal parameters. • Small structural changes by added masses can be clearly detected. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for anomaly/damage detection via structural health monitoring (SHM) systems based on a Binomial Distribution Classifier (BDC). The approach consists of two monitoring levels, labeled as alert and alarm states, respectively, where a damage index (DI) is computed and tracked over time. The alert state is reached when the DI exceeds a given threshold. At this stage, the BDC algorithm starts counting the number of DI values above the threshold within an observation window and computing the related probability of occurrence by using the binomial probability distribution. If the probability falls below a desired limit, the alarm state is triggered. Conversely, the SHM system returns in the non-alert condition. The proposed approach is discussed and evaluated through case studies involving both simulated and experimental data. In the examples, the DI is computed using the Mahalanobis distance of the monitored modal frequencies. The results demonstrate the capability of the BDC to reduce false alarms while preserving the probability of detection. [ABSTRACT FROM AUTHOR]

Concerning the cost- and resource-saving maintenance of assembly products, it is vital to detect any potential malfunctions, defects or structural damage at the earliest-possible stage. For this reason, considerable efforts are being put into the development of Structural Health Monitoring, a field encompassing different approaches to damage identification and capable of preventing defects and even failure. Structural Health Monitoring is often supported by machine learning, a tool for rapid and effective damage identification that can recognize patterns or changes in the data received from the structure. Despite the advances machine learning has made in recent years, obtaining a suitable data set for the efficient training of machine learning algorithms within Structural Health Monitoring remains a challenge. Currently, the data are usually obtained experimentally, with numerical or analytical models. However, the experimental approach can often be time consuming, while the reliability of numerically obtained data relies heavily on the accuracy of the numerical models in capturing the true behavior of the structure. Analytical models may be constrained by the complexity of the observed object. In this paper an alternative approach based on an experimental–numerical (i.e., hybrid) modeling approach is proposed to build a training set for Structural Health Monitoring. Frequency Based Substructuring is utilized to determine the response model of the assembled system based on the properties of its components as well as to mix experimental and numerical models, while leveraging the advantages of each. This makes it possible to generate the samples of the training set in the form of hybrid models of the structure of interest, exhibiting the realistic properties of a physical structure, with a reasonable measurement effort. Here, the approach is demonstrated for the process of joint-damage identification. [Display omitted] • An approach to machine learning training set generation is proposed. • The proposed approach focuses on joint-damage identification. • Analytical, numerical and experimental data are used to obtain training data. • Dynamic substructuring is utilized to obtain training samples. • The proposed approach is tested in a case-study on a laboratory structure. [ABSTRACT FROM AUTHOR]

• The method for calculating transient frequency of bridge response based on principal component analysis and Hilbert transform is proposed. • The method is free from the requirement of the information of bridges under the undamaged state. • The method only needs limited sensors to locate the damage. Conventional vibration-based damage identification methods need a large number of sensors on the bridge to identify the structural modal information and rely on the response data of bridges under a non-damage state for comparison, which restricts their implementations in the health monitoring of bridge structures in service. This paper proposes a baseline-free structural damage detection method based on limited sensor information. The proposed method uses the Hilbert transform combined with principal component analysis (PCA) to locate the damage. Firstly, PCA is performed on the displacement response data matrix of the bridge under a moving load to obtain the principal component matrix. According to the physical interpretation of PCA of the responses of a beam-like bridge under a moving load, each column of the principal component matrix, namely the principal component, composes of a mode shape and the dynamic information at this modal frequency of the bridge. The dynamic information of the modal frequency is indirectly obtained by filtering out the mode shape by low pass filter. Then the Hilbert transform is used to process the dynamic component information to receive the instantaneous frequency which is regarded as the damage index. When the structure is damaged, the first instantaneous frequency at the damaged location changes abruptly. To demonstrate the effectiveness and performance of the proposed method, numerical simulations and experimental validations on beam bridge models are conducted. The results show that the method is free from the requirement of the information of bridges under the undamaged state, and only limited sensors are needed to locate the damage. [ABSTRACT FROM AUTHOR]

• Crack propagation is modeled using ordinary state-based peridynamics theory. • Waves propagating and interacting with cracks are simulated using peri‑ultrasound modeling. • SPC-I technique is used to extract the nonlinear response due to crack propagation. • SPC-I is shown to be useful for analyzing interactions between propagating cracks and ultrasonic waves. This work presents a nonlocal theory based on ordinary state-based (OSB) peridynamics for numerical modeling of crack propagation under loadings and peri‑ultrasound modeling for elastic wave propagation that interacts with generated cracks in 3-D plate structures. Propagating cracks in plate structures for two types of loading – tensile and shear are considered. For both loading cases, at six different times after the start of the loading the analysis is carried out to investigate how the results vary for these six different stages of crack propagation. An ultrasonic excitation is applied at a point on the surface of the plate to generate guided waves propagating through the plate structure and interacting with generated cracks at different stages of crack formation. The out-of-plane velocity fields are recorded at the receiving material point for further analysis. A relatively new and promising nonlinear ultrasonic (NLU) technique called sideband peak count-index (or SPC-I) is adopted to extract the nonlinear response due to the generated cracks. Results show that when the crack propagation is at its early stage the SPC-I value first increases, and then decreases with time at the advanced stages of crack propagation. This is because for both types of loading (tensile or shear) the newly generated cracks are small cracks (or micro-cracks) in the beginning and then they evolve into macro-cracks under increasing loadings. The observation for SPC-I trends is consistent with other numerical modelings and experimental observations reported in the literature, and such qualitative matching gives confidence in the proposed method. The proposed method combining the peri‑ultrasound modeling and SPC-I technique presented in this paper can provide a new insight for monitoring crack propagation and give some guidance for experimental structural health monitoring (SHM) applications. [ABSTRACT FROM AUTHOR]

This paper presents the development, signal analysis and application of an FBG array embedded smart rubber carpet for 2D shape reconstruction. The proposed sensor system is based on the optical fiber integration between two rubber layers, where the optical fibers have 7 FBGs for the quasi-distributed force assessment. The sensor system analysis is performed at room temperature in difference set of tests, namely the stability tests (without any applied force), the force characterization tests (forces applied directly on the FBG region) and the shape reconstruction tests (different forces applied at different positions in the carpet). The stability tests indicated a maximum variation below 0.02 nm considering all 7 FBGs, whereas the force characterization tests indicated a mean sensitivity of around 80 N/nm. However, it is worth to mention that there is a variation on the sensitivities considering all FBGs, which is related to minor thickness differences in the rubber layer that directly effects the strain transmission from the rubber layer to the optical fiber beneath it. Thereafter, the shape reconstruction occurs in two steps, the first one is the cubic interpolation between the FBGs, considering the boundary conditions on the carpet following the beam theory. Then, the k-nearest neighbors regression algorithm is used on the force distribution assessment with a dataset divided into training and testing with a 70/30 proportion. The shape reconstruction results indicate the feasibility of the proposed approach, where root mean squared errors below 0.65 N are obtained in the range of applied force between 3 N and 35 N. Therefore, the proposed smart carpet as a feasible option for different 2D shape reconstruction applications ranging from biomechanics to structural health monitoring. • Force sensor array using Fiber Bragg Gratings embedded in rubber carpet. • 2D Shape reconstruction using machine learning algorithms. • Relative errors on the shape reconstruction below 3%. [ABSTRACT FROM AUTHOR]

• A unified method for solving Bayesian model updating and model class selection. • New formulations for Bayesian model class selection with data fit and information gain. • Theoretical basis for developing the adaptive sampling scheme. • The method is shown to be potential for practical inverse problems by a full-scale structure. For complex engineering structures, it is important to systematically select a model class among various choices and search the complicated parameter space, while also quantifying uncertainties. This paper develops an enhanced adaptive sequential Monte Carlo (ASMC) method to solve Bayesian model updating and model class selection in a unified manner. Main contributions are: (1) Analysis of the sequential sampling process reveals difficulties of sampling from complex probability density functions (PDFs), which naturally leads to the PDF approximation using incremental weights of samples, and then the adaptive sampling scheme with this approximation. (2) Following ASMC framework, new formulations are derived to calculate model class evidence; these formulations enable the separate quantification of data fit and information gain of a model class. A simulated and a full-scale structure were used to demonstrate the proposed method. This work lays good foundation for downstream research such as automation in construction and structural health monitoring. [ABSTRACT FROM AUTHOR]