Structural Health Monitoring Data Research Articles

Experimental Research of the Time-Dependent Effects of Steel–Concrete Composite Girder Bridges during Construction and Operation Periods

The present work aimed to study the effects of temperature changes and concrete creep on I-shaped steel–concrete composite continuous girder bridges during construction and operation processes. This study combined structural health monitoring data, an ANSYS finite element simulation, and the age-adjusted effective modulus method to obtain the variation laws of temperature and internal force in composite girders. Moreover, a temperature gradient model was proposed that is suitable for bridges in Hebei, China. In addition, a concrete creep experiment under unidirectional axial compression was performed using concrete specimens prepared from the concrete batch used to create the composite girder. The long-term evolution laws of the deflection and internal force of the composite girder were obtained by predicting the concrete creep effect. The measured data showed that the temperature variation trends of the steel beam and concrete slab were characterized by a sinusoidal curve without a temperature lag. The heating rate of the concrete slab was higher than the cooling rate. The prediction results showed that the internal force changes in the composite girder were characterized by three stages. The stress changes in the composite girder during the first 10 days were significant and the stress charge rate of the concrete slab, the steel girder and the shear stud can reach 5%–28%. The stress change rate decreased continuously during 10–90 days. The stress changed slowly and smoothly after 90 days. This research can provide feedback and reference for structural health monitoring and service safety control of similar I-shaped steel–concrete composite bridges.

Investigation of dynamic properties of a long-span cable-stayed bridge during typhoon events based on structural health monitoring

Three typhoons approached the southeast coast of China during August 2012. The structural health monitoring (SHM) system recorded in-situ environmental factors (including wind speed and temperature) and structural responses (including displacements and acceleration) during the whole period. Based on the recorded SHM data, the variation features of the environmental factors and structural responses are investigated respectively. Furthermore, modal parameters including natural frequencies and damping ratios of SCB are identified and tracked during this period; such that, the relationship between modal parameters and environmental factors is studied. Results show that the variation of acceleration amplitude of the deck is mainly caused by the high-frequency excitations between 1.0 and 7.0 ​Hz which may correspond to the traffic and high-frequency wind loads. The low-frequency wind load, smaller than 1.0 ​Hz, is the major factor that controls the variation of dynamic displacements of the deck. In addition, a clear relationship between damping ratios and wind speed can be observed for the first three vertical modes, based on which, the aerodynamic damping can be subtracted. Conclusions can provide references for serviceability and safety assessment of similar long-span bridges.

A Bayesian approach for condition assessment and damage alarm of bridge expansion joints using long-term structural health monitoring data

Premature failure of bridge expansion joints has been increasingly observed in recent years, and nowadays it becomes a major concern of bridge owners. A better understanding of their performance in service is highly desired. Deterministic linear regression models between bridge temperature and expansion joint displacement have widely been adopted to characterize the in-service performance of bridge expansion joints. When such a regression pattern is elicited using real-time monitoring data, the deterministic models fail to account for uncertainty inherent in the monitoring data and interpret the model error. In this study, a probabilistic approach for characterization of the regression pattern between bridge temperature and expansion joint displacement by use of Structural Health Monitoring (SHM) data and for SHM-based condition assessment and damage alarm of bridge expansion joints is developed in the Bayesian context. The proposed approach enables to account for the uncertainty contained in the monitoring data and quantify the model error and the prediction uncertainty. By combining the Bayesian regression model and reliability theory, an anomaly index is formulated to evaluate the health condition of the expansion joint when newly collected monitoring data are available and to provide damage alarm once the probability of damage exceeds a certain threshold. In the case study, real-world monitoring data acquired from a cable-stayed bridge are used to illustrate the proposed approach, including examining the appropriateness of the design values of expansion joint displacements under extreme temperatures in serviceability limit state.

Anomaly detection for large span bridges during operational phase using structural health monitoring data

In view of the limitation of damage detection in practical applications for large scale civil structures, a practical method for anomaly detection is developed. Within the anomaly detection framework, wavelet transform and generalized Pareto distribution (GPD) are adopted for data processing. In detail, to reduce the influence of thermal responses on signal fluctuations induced by anomaly events, wavelet transform is employed to separate thermal effects from raw signals based on the distinguished frequency bandwidths. Subsequently, a two-level anomaly detection method is proposed, i.e. threshold-based anomaly detection and anomaly trend detection. For the threshold-based anomaly detection, the threshold for anomaly detection is determined by GPD analytics, corresponding to a 95% guarantee rate within 100 years. Moreover, the threshold is periodically updated by incorporating the latest monitoring data to model the increase of traffic volumes and gradual degradations of structures. For the anomaly trend detection, the moving fast Fourier transform is adopted for discussion. Finally, the mid-span deflection of Xihoumen Suspension Bridge is selected as the index to validate the effectiveness of the proposed methodology. Two types of anomaly events are assumed in the case study, i.e. the overloading event and structural damage. The two-level anomaly detection is implemented. It is indicated through the case study that the proposed anomaly detection approach (without the influence of temperature) is able to detect three 100 ton overloaded vehicles and damages in main cables. However, the assumed cases subject to 100 ton vehicle and damages in stiffening girders are hardly detected by using the deflection index, owing to the sensitivity of the index to each anomaly event. In the future studies, a structural health monitoring-based multi-index anomaly detection system is promising to ensure the operational and structural safety of large span bridges.

A Bayesian approach to degradation modeling and reliability assessment of rolling element bearing

This paper presents two Bayesian hierarchical models—one utilizing the life-time data and other using the structural health monitoring (SHM) data, for degradation modeling and reliability assessment of rolling element bearings. The main advantage of the proposed life-time data based model is that, it accounts for the variability in failure times caused due to the difference in material properties, initial degradation, operating and environmental conditions by introducing Bayesian hierarchy in the model parameters. On the other hand, SHM data (such as vibration and strain) based model focuses on stochastic nature of bearing degradation, and models it using a two-phase Wiener process. In this model, the point of phase-transition is the time when the damage initiates. The detection of such a point is undertaken using Bayesian change point algorithms. For both the models, the model parameters and reliability are updated as more data becomes available. In this manner, the prior domain knowledge and life-time data or SHM data collected from the field can effectively be integrated to get updated reliability. Two case studies for rolling element bearings are presented to demonstrate the applicability to life-time as well as SHM data.

Evaluation of Loads Applied on Engineering Structures Based on Structural Health Monitoring Data

Evaluation of Loads Applied on Engineering Structures Based on Structural Health Monitoring Data

Towards semi-supervised and probabilistic classification in structural health monitoring

In practical applications of data-driven Structural Health Monitoring (SHM), recording labels for each of the measured signals can be infeasible and expensive. In consequence, conventional methods for (supervised) machine learning can become irrelevant in certain applications of damage classification. Semi-supervised methods, however, allow algorithms to learn from information in the available unlabelled measurements as well a limited set of labelled data. As such, this paper suggests a semi-supervised Gaussian mixture model for probabilistic damage-classification, informed by both labelled and unlabelled signals. The generative statistical model is shown to improve the classification performance, compared to supervised learning, with simulated and experimental SHM data, while requiring no further inspections of the system. Specifically, semi-supervised learning leads to 3.87% and 3.83% reductions in the classification error for the simulated and experimental datasets respectively. These results indicate that, through semi-supervised learning in SHM, the cost associated with labelling data could be managed, as the information in a small set of labelled signals can be combined with larger sets of unlabelled data.

On the estimation of the ductility demand on reinforced concrete bridge piers from structural health monitoring data

A method for the estimation of the ductility demand on reinforced concrete piers during ground seismic shaking is proposed. The required input is the measurement data from a structural health monitoring system within the time window of the seismic event. The procedure relies on detection of high-frequency transients related to damage and their distance forward in time to the closest deformation extrema. A formulation is proposed which relates these measurements to the ductility demand on the pier. A step-by-step description of the procedure is provided along with a case study implementation on an experimental reinforced concrete column. The ductility demand is estimated and compared with the one obtained from the experimental force–deformation curve.

Vibration-based damage indicators: a comparison based on information entropy

Vibration-based methods for damage localizations often rely on a damage feature defined in terms of changes of modal shapes and localize damage by detecting shape irregularities that are ascribed to local loss of stiffness. In this paper, the performance of several algorithms for damage localization is investigated and the results are compared in terms of the gain of information they provide, which is measured by the relative information entropy also known as Kullback–Leibner (KL) divergence. This parameter is a measure of the difference between two probability distributions and can quantify the information gain achieved using different statistical models. In this paper the relative entropy is used to compare the gain of information obtained using different damage-sensitive indicators retrieved from simulated structural health monitoring data. The investigation is carried out using structural responses simulated using the finite element model of a real bridge permanently monitored by the Italian Seismic Observatory of Structures.

On-line updating Gaussian process measurement model for crack prognosis using the particle filter

The particle filter (PF) has shown great potential for on-line fatigue crack growth prognosis by combining crack measurements from structural health monitoring (SHM) techniques. In this method, a key problem is to construct the mapping between the feature extracted from SHM signals and the crack size. However, this mapping may be inaccurate since the data used for establishing the mapping is affected by uncertainties from sources like damage geometries, sensor placements, and boundary conditions. To deal with this problem, this paper proposes an on-line updating Gaussian process (GP) measurement model within the PF based crack prognosis framework. The GP measurement model outputs the mean and variance of the crack length corresponding to the feature of SHM signals, which are input into the PF for evaluating the posterior estimation of the crack length for crack prognosis. Then, this posterior estimation is sequentially appended to the GP dataset for updating the measurement model. Moreover, once crack inspection data is obtained, it is combined with existing SHM data for additional updating of the GP measurement model. Validations are performed on the fatigue test of attachment lug structures, in which the guided wave based SHM technique is applied for crack monitoring. As the cyclic load may cause intricate influences on the guided wave propagation, it is more difficult to quantify the crack length. The validation result shows that the on-line updating GP measurement model can effectively map the feature of SHM signals to the crack length, and result in accurate crack growth prognosis with the PF based method.

Four years of Structural Health Monitoring of the San Pietro Bell Tower in Perugia, Italy: two years before the earthquake versus two years after

This paper addresses the structural health monitoring (SHM) of the bell tower of the Basilica of San Pietro in Perugia, Italy, which is located in a seismic area. Known as one of the landmarks of the Umbrian capital, the tower belongs to a monumental complex of exceptional historical and cultural value. Therefore, its protection with respect to earthquakes is an important issue. To this purpose, a vibration-based SHM system able to detect anomalies in the structural behaviour by means of statistical process control tools has been installed in the tower and is under continuous operation since December 2014. The effects of the 2016-2017 Central Italy seismic sequence were clearly detected by this system, even if earthquakes took place at relatively large distance from the bell tower. The large amount of SHM data collected over four years allowed to assess the modifications in the structural behaviour of the bell tower in post-earthquake conditions.

Deep learning for data anomaly detection and data compression of a long‐span suspension bridge

AbstractAs intelligent sensing and sensor network systems have made progress and low‐cost online structural health monitoring has become possible and widely implemented, large quantities of highly heterogeneous data can be acquired during the monitoring. This has resulted in exceeding the capacity of traditional data analytics techniques, especially in monitoring large‐scale or critical civil structures. In particular, data storage has become a big challenge, hence, resulting in the emergence of data compression and reconstruction as a new area in structural health monitoring (SHM) of large infrastructure systems. SHM data generally include anomalies that can disturb structural analysis and assessment. The fundamental reasons for the abnormality of data are extremely complex. Therefore, reconstruction of the abnormal data is generally difficult and poses serious challenges to achieve high‐accuracy after data has been compressed. Considering these significant challenges, in this paper, a novel deep‐learning‐enabled data compression and reconstruction framework is proposed that can be divided into two phases: (a) a one‐dimensional Convolutional Neural Network (CNN) that extracts features directly from the input signals is designed to detect abnormal data with validated high accuracy; (b) a new SHM data compression and reconstruction method based on Autoencoder structure is further developed, which can recover the data with high‐accuracy under such a low compression ratio. To validate the proposed approach, acceleration data from the SHM system of a long‐span bridge in China are employed. In the abnormal data detection phase, the results show that the proposed method can detect anomaly with high accuracy. Subsequently, smaller reconstruction errors can be achieved even by using only 10% compression ratio for the normal data.

Clarifying and quantifying the geometric correlation for probability distributions of inter-sensor monitoring data: A functional data analytic methodology

In structural health monitoring (SHM), revealing the underlying correlations of monitoring data is of considerable significance, both theoretically and practically. In contrast to the traditional correlation analysis for numerical data, this study seeks to analyse the correlation of probability distributions of inter-sensor monitoring data. Due to induced by some commonly shared random excitations, many structural responses measured at different locations are usually correlated in distributions. Clarifying and quantifying such distributional correlations not only enables a more comprehensive understanding of the essential dependence properties of SHM data, but also has appealing application values; however, statistical methods pertinent to this topic are rare. To this end, this article proposes a novel approach using functional data analysis techniques. The monitoring data collected by each sensor are divided into time segments and later summarized by the corresponding probability density functions (PDFs). The geometric relations of the PDFs in terms of their shape mappings between sensors are first characterized by warping functions, and they are subsequently decomposed into finite functional principal components (FPCs); one FPC of the warping functions characterizes one deformation pattern in the transformation of the shapes of the PDFs from one sensor to another. Using this principle, the inter-sensor geometric correlation patterns of PDFs can be clarified by analysing the correlation of the FPC scores of warping functions to the PDFs from one sensor. To overcome the challenge of correlation quantification for real-valued samples (FPC scores) coupled with their functional counterparts (PDFs), a novel nonparametric functional regression (NFR)-based correlation coefficient is defined. Both simulation and real data studies are conducted to illustrate and validate the proposed method.

Structural health monitoring of timber buildings: a literature survey

ABSTRACT In recent years, multiple historic and contemporary timber buildings have been instrumented with sensors to monitor the performance of wood products and novel engineering systems. This paper presents the results of a literature survey focused on timber structural health monitoring (SHM) projects. This survey was aimed at investigating how the scopes of monitoring projects reported in the literature are technically addressed and who are the primary users of these data. The main contribution of this study is the definition of a general taxonomy to describe timber SHM projects, their scope, approaches and potential outcomes. This taxonomy aids readers in identifying ways of using information from SHM data. The results of this survey can be used to develop strategies allowing for data-supported decision-making for the preservation of historic buildings, the design of new structures and the service life management of built facilities.

Identification of earthquake ground motion based on limited acceleration measurements of structure using Kalman filtering technique

Identification of earthquake ground motion from structural health monitoring (SHM) data provides a good means to reconstruct seismic loads that are essential for postearthquake safety assessments and disaster simulations of structures. Because the data measured by an SHM system are structural absolute response, they cannot be directly applied to the structural motion equation, which is established in relative coordinate system. As such, this paper originally derives the motion equation in absolute coordinate system and then expands the equation into modal space. In addition, the proposed method allows for identifying earthquake ground motion using incomplete modal information and limited measurements through the standard Kalman filter. Subsequently, a numerical two-dimensional frame is used to validate the feasibility of the proposed method, and the influences of modal parameters and measurement noise on the identification accuracy are also fully investigated. The results show that the proposed method is sensitive to frequency and measurement noise but insensitive to modal shape and damping ratio. It is also found that the identified ground motion subjected to certain measure noise can still be reliably employed for postseismic response calculations of medium- and long-period structures. Finally, a shaking table test performing on a five-floor frame further demonstrates the effectiveness and accuracy of the proposed identification algorithm for practical application.

Improved automated operational modal identification of structures based on clustering

A robust automated method for operational modal analysis (OMA) is in a great demand for processing a large amount of structural health monitoring data from engineering structures. This paper proposes an improved automated OMA approach based on data-driven Stochastic Subspace Identification (SSI) and clustering techniques with novel criteria. The framework of the proposed approach includes two main components, namely, “modal identification by SSI” and “automated interpretation of SSI output.” Three procedures including hard validation criteria removal, an improved statistics-based clustering procedure, and a developed cluster merging procedure are combined in the second component for automatically interpreting the stabilization diagram from the SSI output, without a priori knowledge on the modal parameters and no manual tuning during interpreting. Numerical validation results on a frame structure model demonstrate that the proposed approach is capable of identifying the vibration modes accurately, under a significant noise effect. No spurious modes are observed, and the physical modes can be accurately identified. Experimental studies on a steel frame structure in the laboratory and a real footbridge are conducted to demonstrate the robustness and applicability of using the proposed approach for automated OMA and modal tracking. Identification results are compared with baselines and those from an existing reference method to demonstrate the improvement and contribution made in the proposed approach on the automated OMA.

Analyses and Interpretations of Structural Health Monitoring Data on Reinforced Concrete Buildings: A Critical Review

Analyses and Interpretations of Structural Health Monitoring Data on Reinforced Concrete Buildings: A Critical Review

Lost data recovery for structural health monitoring based on convolutional neural networks

Signal transmission loss of using wireless sensors for structural health monitoring is a usual case, which undermines the reliability of the sensors for monitoring the structural conditions. The measured vibration data with a high data loss ratio can hardly be used for the analysis, that is, modal identification, as it will lead to significant errors in the results. This paper proposes a novel approach based on convolutional neural networks for recovering the lost vibration data for structural health monitoring. The used network is a fully feed-forward convolutional neural network with bottleneck architecture and skip connection, which constructs the nonlinear relationships between the incomplete signal with data loss measured from the sensors with the transmission loss and the complete true signal. The trained network extracts the robust higher representation features of the measured incomplete signals using the compression layers and expands those features gradually throughout the reconstruction layers to recover and obtain the complete true signals. The long-term vibration data from Dowling Hall Footbridge are employed to validate the effectiveness and robustness of the proposed approach for the lost data recovery. Two case studies are conducted to validate the recovery accuracy for single-channel and multiple-channel cases, respectively. The effect of sampling rate on the recovery accuracy is also investigated. The proposed approach exhibits the outstanding capability of lost data recovery, even when the signals have severe data loss ratios up to 90%. To further demonstrate the reliability of the recovered signals for data analysis, modal identification results by using the recovered signals with different data loss ratios show a very good agreement with those obtained from the complete true data.

Measurement and Forecasting of High-Speed Rail Track Slab Deformation under Uncertain SHM Data Using Variational Heteroscedastic Gaussian Process.

Uncertainty in sensor data complicates the construction of baseline models for the measurement and forecasting (M&F) of high-speed rail (HSR) track slab deformation. Standard Gaussian process (GP) assumes a uniform noise throughout the input space. However, in the application to modelling of HSR structural health monitoring (SHM) data, this assumption can be unrealistic, because of its unique heteroscedastic uncertainty that is induced by dynamic train loading, electromagnetic interference, large temperature variation, and daily maintenance actions of railway track infrastructure. Therefore, this study firstly develops a novel online SHM system enabled by fiber Bragg grating (FBG) technology to eliminate electromagnetic interference on SHM data for continuous and long-term monitoring of track slab deformation, with the capacity of temperature self-compensation. To deal with different sources of uncertainty, the study explores Variational Heteroscedastic Gaussian Process (VHGP) approach while using variational Bayesian and Gaussian approximation for data modelling, estimation of the monitoring data uncertainty, and further data forecasting. The results demonstrate that the VHGP framework yields more robust regression results and the estimated confidence level can better depict the heteroscedastic variances of the noise in HSR data. Higher accuracy for both regression and forecasting is gained through VHGP and the position with maximum noise can be more accurately forecasted with a smooth varying confidence interval. Based on in-situ measurement data, the uncertainty levels for all sensors are estimated together with corresponding deformation profiles for the instrumented segment and three typical types of uncertainty are summarized during the M&F process of HSR track slab deformation.

Wind turbine lifetime extension decision-making based on structural health monitoring

In this work, structural health monitoring data is applied to underpin a long-term wind farm lifetime extension strategy. Based on the outcome of the technical analysis, the case for an extended lifetime of 15 years is argued. Having established the lifetime extension strategy, the single wind turbine investigated within a wind farm is subjected to a bespoke economic lifetime extension case study. In this case study, the local wind resource is taken into consideration, paired with central, optimistic, and pessimistic operational cost assumptions. Besides a deterministic approach, a stochastic analysis is carried out based on Monte Carlo simulations of selected scenarios. Findings reveal the economic potential to operate profitably in a subsidy-free environment with a P90 levelised cost of energy of £25.02 if no component replacement is required within the nacelle and £42.53 for a complete replacement of blades, generator, and gearbox.