Structural Health Monitoring Strategies Research Articles

Monitoring the changes in the dynamic properties of an RC building experiencing column loss

In this study, an existing six-storey reinforced concrete building with an asymmetric structural plan and soft storey irregularity was used as a test specimen and subjected to three-step progressive structural damages to detect the variations in its dynamic properties. Mode shapes and dominant frequencies of the undamaged building were determined by the ambient vibration survey (AVS) and it was seen that its first three modes were torsion coupled. Besides, soft storey irregularity was evident due to the lack of masonry infill walls on its ground floor. Later on, three-step progressive damages were applied to the building. The first step targeted three columns and one beam of the building, located on a corner region of its ground floor to peel off their clear covers. The second step razed two adjacent corner columns which were already moderately damaged in the first step, while the third step knocked the third moderately damaged column down. After each damage step, AVS was repeated with the same details as applied for the undamaged building. The obtained dynamic properties for the four phases of the building were evaluated with the sustained damage. Numerical analyses with the finite element model of the building representing its four different phases were also performed and the unique responses due to damage effects on the structure were investigated numerically. As a result of induced damage, the quantified amount of frequency change in modes and the new mode observed after particularly column loss scenarios can be utilized for efficient structural health-monitoring strategies of plan-asymmetric buildings and post-earthquake assessment of partially damaged buildings where timely objective assessment is important.

Multi-Antenna Global Navigation Satellite System/Inertial Measurement Unit Tight Integration for Measuring Displacement and Vibration in Structural Health Monitoring

Large-scale engineering structures deform and vibrate under the influence of external forces. Obtaining displacement and vibration is crucial for structural health monitoring (SHM). Global navigation satellite system (GNSS) and inertial measurement unit (IMU) are complementary and widely used in SHM. In this paper, we propose an SHM scheme where IMU and multi-antenna GNSS are tightly integrated. The phase centers of multiple GNSS antennas are transformed into the IMU center, which increases the observation redundancy and strengthens the positioning model. To evaluate the performance of tight integration of IMU and multiple GNSS antennas, high-rate vibrational signals are simulated using a shaking table, and the errors of horizontal displacement of different positioning schemes are analyzed using recordings of a high-precision ranging laser as the reference. The results demonstrate that applying triple-antenna GNSS/IMU integration for measuring the displacement can achieve an accuracy of 2.6 mm, which is about 33.0% and 30.3% superior than the accuracy achieved by the conventional single-antenna GNSS-only and GNSS/IMU solutions, respectively.

Autonomous Sensor System for Low-Capacity Wind Turbine Blade Vibration Measurement.

This paper presents the design, implementation, and validation of an on-blade sensor system for remote vibration measurement for low-capacity wind turbines. The autonomous sensor system was deployed on three wind turbines, with one of them operating in harsh weather conditions in the far south of Chile. The system recorded the acceleration response of the blades in the flapwise and edgewise directions, data that could be used for extracting the dynamic characteristics of the blades, information useful for damage diagnosis and prognosis. The proposed sensor system demonstrated reliable data acquisition and transmission from wind turbines in remote locations, proving the ability to create a fully autonomous system capable of recording data for monitoring and evaluating the state of health of wind turbine blades for extended periods without human intervention. The data collected by the sensor system presented in this study can serve as a foundation for developing vibration-based strategies for real-time structural health monitoring.

Heteroscedastic Gaussian processes for data normalisation in probabilistic novelty detection of a wind turbine

This study investigates the data normalisation of modal parameters of an operating concrete–steel hybrid onshore wind turbine tower considering also the identification uncertainty. In order to take into account the Environmental and Operational Condition (EOC)-dependent variance, sparse heteroscedastic Gaussian processes (GPs) are used for the data normalisation. Following a typical vibration-based Structural Health Monitoring (SHM) scheme, data normalisation of the natural frequencies and the mode shapes is performed first. Subsequently, a metric is defined which takes into account both the identification uncertainty and the operation-dependent uncertainty in order to enable novelty detection.The data normalisation methods must be able to handle uncertainties of different magnitudes due to EOCs in the data. In this context, GPs can be a suitable tool. However, standard GPs assume homoscedasticity, which is an unrealistic assumption in the case of EOC-dependent variance. Using a heteroscedastic GP instead, the variance of the data is better mapped and allows comparison with the identification uncertainties of Bayesian operational modal analysis (BAYOMA), taking into account the specifics of closely spaced modes of the tower structure. This leads to a better interpretation of the data and enables the introduction of a probabilistic novelty metric.This data normalisation approach, taking into account EOC-dependent uncertainties using heteroscedastic GPs, is being applied for the first time to a tower of a full scale 3.4 MW wind turbine in operation. Following this approach, it is possible to detect smaller changes in natural frequencies and second-order modal assurance criterion (S2MAC) compared to the assumption of homoscedasticity within the GP. In addition, a novelty was detected using the S2MAC during the period under study. Therefore, it can be illustrated that mode shape-based metrics tend to be more sensitive than purely frequency-based ones. However, it is difficult to assess the significance of such changes for structural integrity without further information.

Identification of damages in a concrete beam: a modal analysis based method

Structural Health monitoring (SHM) strategies can play a pivotal role in the perspective of enhancing structures and infrastructures life cycle and maintenance operations. A plethora of sensors and technologies can be employed in this field; in a seismic context, vibrational tests are particularly relevant, being able to give an insight on the dynamic characteristics of the structure itself. In particular, modal parameters can be considered in order to detect damages. A comparison between a certain test time and 0 test time (i.e., undamaged structure) is commonly performed; to this aim numerical models result particularly useful to provide baseline data (often unavailable in pre-existing structures), but they need to be validated before use. Non-contact techniques, like scanning laser Doppler vibrometry, can be exploited to do this. In this paper a numerical model of a scaled concrete beam is realized and validated through LDV data, then it is used to design load tests for progressive damages generation. Modal analysis is conducted after different load trials to evaluate changes of modal parameters in relation to the damage occurred; also, damage-related indices are proposed. The results confirmed the suitability of LDV for dynamic analyses of cement-based structures and this can be particularly useful when big structures (e.g., bridges) have to be monitored in-field. The numerical model was validated with acceptable absolute errors in terms of natural frequencies (between 26 Hz and 131 Hz) and high Modal Assurance Criterion (MAC) values (0.85-0.93). Moreover, the proposed methodology allows to detect damages also in a concise way through synthetic indices (with changes >50% in damaged vs undamaged conditions) and early warnings could be generated according to their values, hence supporting decision-making procedures in the building management scenario.

On the hierarchical Bayesian modelling of frequency response functions

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.

Advancements in Optimal Sensor Placement for Enhanced Structural Health Monitoring: Current Insights and Future Prospects

Structural health monitoring (SHM) is critical to maintaining safe and reliable civil infrastructure, but the optimal design of an SHM sensing system, i.e., optimal sensor placement (OSP), remains a complex challenge. Based on the existing literature, this paper presents a comprehensive review of OSP strategies for SHM. It covers the key steps in OSP, from evaluation criteria to efficient optimization algorithms. The evaluation criteria are classified into six groups, while the optimization algorithms are roughly categorized into three classes. The advantages and disadvantages of each group of methods have been summarized, aiming to benefit the OSP strategy selection in future projects. Then, the real-world implementation of OSP on bridges, high-rise buildings, and other engineering structures, is presented. Based on the current progress, the challenges of OSP are recognized; its future development directions are recommended. This study equips researchers/practitioners with an integrated perspective on state-of-the-art OSP. By highlighting key developments, persistent challenges, and prospects, it is expected to bridge the gap between theory and practice.

Vibration-based health monitoring of the offshore wind turbine tower using machine learning with Bayesian optimisation

Structural health monitoring of towers for offshore wind turbines (OWTs) is critical because they serve as support structures. Although the modal parameters of the tower can be identified through operational modal analysis, evaluating the realistic characteristics of structures is challenging, and the modal parameters are susceptible to fluctuations owing to shifting environmental and operational conditions (EOCs). In this study, we aim to improve the vibration-based structural health monitoring strategy for monitoring OWT towers. Remote-sensing products from ERA5, which provide information regarding the marine state, are used to extend the data source. Machine learning (ML) with Bayesian optimisation is utilised to minimise the effects of EOCs on modal parameters. We demonstrate that the modal parameters of the tower are significantly influenced by the EOCs by applying the developed approach to an offshore wind turbine. Moreover, the effects of the EOCs on the modal parameters are minimised by applying ML models with Bayesian optimisation. A comparison between the monitoring divided into different operating conditions and the monitoring of all operating conditions indicate that there is minimal room for performance improvement of ML models with thorough condition segmentation. Consequently, employing global models directly is preferred in the present application environment.

Deep residual learning for acoustic emission source localization in A steel-concrete composite slab

Large errors can be introduced in traditional acoustic emission (AE) source localization methods using extracted signal features such as arrival time difference. This issue is obvious in the case of irregular structural geometries, complex composite structure types or presence of cracks in wave travel paths. In this study, based on a novel deep learning algorithm called deep residual network (DRN), a structural health monitoring (SHM) strategy is proposed for AE source localization through classifying and recognizing the AE signals generated in different sub-regions of critical areas in structures. Hammer hits and pencil-leak break (PLB) tests were carried out on a steel-concrete composite slab specimen to register time-domain AE signals under multiple structural damage conditions. The obtained time-domain AE signals were then converted into time-frequency images as inputs for the proposed DRN architecture using the continuous wavelet transform (CWT). The DRNs were trained, validated and tested by AE signals generated from different source types at various damage states of the slab specimen. The proposed DRN architecture shows an effective potential for AE source localization. The results show that the DRN models pre-trained by the AE signals obtained in the undamaged specimen are able to accurately classify and identify the locations of different types of AE sources with 3–4.5 cm intervals even when multiple cracks with widths up to 4–6 mm are present in the wave travel paths. Moreover, the influence factors on the model performance are investigated, including structural damage conditions, sensor-to-source distances and AE sensor mounting positions; in accordance with the parametric analyses, recommendations are proposed for the engineering application of the proposed SHM strategy.

Hierarchical verification and validation in a forward model-driven structural health monitoring strategy

This paper presents a demonstrative application of a forward model-driven approach to structural health monitoring (SHM), incorporating hierarchical validation methods. A key tenet of the approach is that an SHM system can be constructed that is capable of diagnosing damage at the full system level, without full system damage-state data having been used in its development; achieving this would be highly impactful as the system-level damage state data is generally not feasible to acquire (previous SHM methods such as data-driven SHM have been hindered by their dependence on these data). This is achieved by carrying out validation activities on the damage model at the subassembly level of the structure. The particular focus of the present paper is on damage detection and assessment, although the approach offers a natural basis for extension to other damage identification activities such as damage location and prognosis. The present study focuses on two of the key elements of the model-driven approach: validation of the predictive substructure models and their application in the assembled model. The ideas discussed are demonstrated in a case study based on a laboratory-scale truss bridge structure.

Characterization of multiple debris cloud impacts-induced pitting damage evolution in spacecraft structures using temperature-dependent ultrasonic nonlinearity

Facing the harsh cryogenic-elevated temperature condition, evaluation of the debris cloud-induced pervasive but highly complex pitting damage in manned spacecraft structures, featuring multitudinous small-scale craters and cracks disorderedly scattered over a wide region, accompanied by a diversity of micro-damages, is hitherto still a challenging task. In line with the fact that the use of ultrasonic nonlinearity is restricted by its intrinsic vulnerability to measurement contamination, in particular temperature fluctuations. With this motivation, an insight into the modulation mechanism of typical elastic parameters of material at macroscopic under varying temperatures, as well as microscopic interatomic potential, is achieved via theoretical analysis. Based on this, temperature sensitivity coefficients (TSCs) of these elastic parameters are obtained, and a nonlinear temperature sensitivity index (TSI) is established. On this basis, an active TSI-based evaluation method is proposed to improve the accuracy and reliability of pitting damage evaluation by controlling detection temperature (i.e., exploitation of the benign aspects of the temperature sensitivity of ultrasonic nonlinearity), rather than eliminating or compensating for its adverse effect, when other nonlinear sources interference. This method is experimentally corroborated, in which the multiple debris cloud impacts-induced accumulated pitting damage are accurately and intuitively characterized through temperature control, in terms of its severity and distribution characteristics, which is consistent with the actual results. These findings provide an active structural health monitoring (SHM) strategy for promoting the practical application of this proposed approach for characterizing pitting damage evolution in manned spacecraft in qualitative and semi-quantitative manners.

STRUCTURAL HEALTH MONITORING OF CURVED ROADWAY BRIDGES THROUGH SATELLITE RADAR INTERFEROMETRY AND COLLAPSE SIMULATION

AbstractThis paper presents the application of a Structural Health Monitoring (SHM) strategy for bridges affected by slow deformation phenomena to a curved roadway bridge undergoing slow landslide‐induced movements. The chosen multidisciplinary approach is based on the combination of remote sensing data through satellite Synthetic Aperture Radar Interferometry (InSAR) and structural analysis up to collapse investigated through the Applied Element Method (AEM). The results of the application to the considered case study demonstrate that the integration of the displacement information obtained through InSAR technique with the numerical analysis allows for improving the comprehension of the health state of the bridge undergoing slow movements and identifying potentially critical conditions for the structure.

Combined two-level guided wave structural health monitoring strategy using multifeature integration and machine learning: application to early-age grouted sleeves

Structural health monitoring of grouted sleeves is one of the assembly industry’s huge challenges. In this study, a combined two-level damage detection was introduced. It comprises defect classification (healthy, rebar eccentricity, and grout leakage) and severity evaluation for early-age grouted sleeves using guided waves. Multiple features (MF) from time-, frequency-, and time-frequency domains were extracted and defined according to the diverse defects and ages of grouted sleeves to represent complex damage characteristics. Moreover, the egret swarm algorithm optimization–extreme learning machine (ESAO-ELM) models were proposed to avoid the influence of subjective experience and judgment from experts. ESAO optimized the initial random parameters (input weights and hidden layer bias) of ELM. Then, two MF-ESAO-ELM models were trained for two-level damage detection on the experimental dataset. The performance of the proposed models was comprehensively evaluated using accuracy, recall, precision, and confusion matrix. MF-ESAO-ELM performs better than ELM and PSO-ELM in accuracy. In this strategy, the defect classification model works in the outer layer to distinguish the state and types of defects of grouted sleeves (healthy, eccentric, or leakage). In comparison, the inner layer starts predicting the severity only if the defect type is leakage. MF-ESAO-ELM offers advantages in terms of accuracy, strategy, and calculation time.

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

This paper proposes a new in-situ damage detection approach for wind turbine blades, which leverages blade-internal non-stationary acoustic pressure fluctuations caused by the mechanical loading as the main source of excitation. This acoustic excitation was leveraged for the detection of fatigue-related damage modes on a full-scale wind turbine blade undergoing edgewise fatigue testing. An unsupervised, data-driven structural health monitoring strategy was developed to learn the normal cavity-internal acoustic sequences generated by the blade’s load cycles and to detect damage-related anomalies in the context of those sequences. A linear cepstral-coefficient based feature set was used to characterize the cavity-internal acoustics and LSTM-autoencoders were trained to accurately reconstruct healthy-case sequences. The reconstruction error was then used to characterize anomalous acoustic patterns within the blade cavity. The technique was able to detect a damage event earlier than a strain-based system by 120,000 load cycles.

Wind turbine blade damage detection using data-driven techniques

This work presents a simple damage detection strategy for wind turbine blades. In particular, a vibration analysis-based damage detection methodology is proposed that requires only healthy data and detects damage in different locations of the blade. The stated structural health monitoring strategy is based on the extraction of characteristics using statistical metrics as a technique for the recognition and differentiation of healthy test experiments from damaged test experiments with simulated faults created by added mass. In this manner, several metrics are approached to find those that show better classification in processing the data provided by the sensors. Finally, an evaluation process is performed to detect blade damage. The results show that the proposed RMSE metric performs at an ideal level, making it a promising strategy for the detection of blade damage.

Damage detection in a RC-masonry tower equipped with a non-conventional TMD using temperature-independent damage sensitive features

Many features used in Structural Health Monitoring strategies are not just highly sensitive to failure mechanisms, but also depend on environmental or operational fluctuations. To prevent incorrect failure uncovering due to these dependencies, damage detection approaches can use robust and temperature-independent features. These indicators can be naturally insensitive to environmental dependencies or artificially made independent. This work explores both options. Cointegration theory is used to remove environmental dependencies from dynamic features to create highly sensitive parameters to detect failure mechanisms: the cointegration residuals. This paper applies the cointegration technique for damage detection of a concrete-masonry tower in Italy. Two regression models are implemented to capture temperature effects: Prophet and Long Short-Term Memory networks. Results demonstrate the advantages and limitations of this methodology for real applications. The authors suggest to combine the cointegration residuals with a secondary temperature-insensitive damage-sensitive set of features, the Cepstral Coefficients, to address the possibility of capturing undetected structural damage.

Numerical Simulations in Ultrasonic Guided Wave Analysis for the Design of SHM Systems—Benchmark Study Based on the Open Guided Waves Online Platform Dataset

Structural health monitoring (SHM) strategies based on ultrasonic guided waves are very promising regarding thin-walled lightweight structures. To study the performance of such systems, validated numerical analysis tools have to be used. For that procedure, a benchmark between two numerical methods will be presented. The first promising approach is the elastodynamic finite integration technique (EFIT). Miscellaneous research shows that its capability of capturing wave characteristics and interactions is advanced in various media and structures, including thin-walled composites. The second approach employs conventional shell-type finite elements following the Reissner–Mindlin theory for modelling layered composite structures. The advantage of using such finite element methods (FEM) is their high availability in general purpose simulation tools. As a reference model, the measurement data coming from the Open Guided Waves Project (OGW) was taken into account. The OGW dataset provides the experimental data of ultrasonic guided wave propagation in carbon fiber composite plates with an additional omega stringer. By using this contribution, this experiment was reproduced by simulation. The paper presents the results of a validation and motivates further research, such as in research related to the probability of detection analysis and numerical performance.

Deep learning for automatic assessment of breathing-debonds in stiffened composite panels using non-linear guided wave signals

This paper presents a new structural health monitoring strategy based on a deep learning architecture that uses nonlinear ultrasonic signals for the automatic assessment of breathing-like debonds in lightweight stiffened composite panels (SCPs). Towards this, nonlinear finite element simulations of ultrasonic guided wave (GW) response of SCPs and laboratory-based experiments have been undertaken on multiple composite panels with and without baseplate-stiffener debonds using fixed a network of piezoelectric transducers (actuators/sensors).GW signals in the time domain are collected from the network of sensors onboard the SCPs and these signals in the frequency domain represent nonlinear signatures as the existence of higher harmonics. These higher harmonic signals are separated from the GWs (raw)and converted to images of time–frequency scalograms using continuous wavelet transforms. Adeep learning architecture is designed that uses the convolutional neural network to automatically extract the discrete image features for the characterization of SCP under healthy and variable breathing-debond conditions. The proposed deep learning-aided health monitoring strategy demonstrates a promising autonomous inspection potential with high accuracy for such complex structures subjected to multi-level breathing-debond regions.

Structural health monitoring for corrosion induced thickness loss in marine plates subjected to random loads

In the highly corrosive environment where marine structures operate, the current industry practice is to overcompensate for corrosion induced thickness loss (CITL) and replace any parts that score below specified allowable limits during scheduled maintenance. Hence, there would be immediate benefits from the implementation of a Structural Health Monitoring (SHM) system that would allow for predictive, condition-based maintenance. The objective of this study is to investigate and assess the effectiveness of state-of-the-art statistical pattern recognition (SPR) and machine learning (ML) methods in association with alternative sensor grid architectures as an SHM scheme for detecting CITL, under highly variable operational conditions. For this purpose, a simple rectangular plate at different corrosion levels (uniform and pitting) was considered as a reference structural element, which was subjected to a stochastic pressure load. Strain response data were produced using Finite Element (FE) simulations and were treated under a probabilistic framework. Elements from detection theory and ML were taken under consideration in order to construct alternative detectors and assess their performance.

Vibro-Acoustic Modulation with broadband pump excitation for efficient impact damage detection in composite materials

In the past few decades, the need for efficient and reliable Structural Health Monitoring strategies has led to the development of several approaches for damage detection and characterization purposes. Among them, the Nonlinear Vibro-Acoustic Modulation (VAM) exploits the modulation arising from the interaction of two concurrently applied driving waves, namely the probe and the pump excitations, in the presence of nonlinear scatters such as cracks and defects. Therefore, the VAM provides information on the emergence of internal damage by extracting the nonlinear modulated components of the response of a damaged system. Originally proposed for granular media, the method has shown to be effective in detecting the presence of defects also in metals and composite materials. Nonetheless, its efficacy is highly affected by the excitation frequencies, which are usually chosen among the system resonances. The need for a preliminary modal analysis and, at once, the risk of selecting pump-probe frequency combinations with low sensitivity to damage may make the procedure time-consuming and not fully reliable, preventing the VAM technique from being widely accepted as a robust monitoring tool. To overcome these limitations, a broadband excitation may be used. This study assesses the effectiveness of the VAM technique when a combination of a frequency-swept pump excitation and a mono-harmonic probe wave is applied to drive the sample. Experimental tests were conducted on a composite laminated beam mounted on an electrodynamic shaker and tested in both pristine and damaged conditions. Low-profile surface-bonded piezoceramic transducers were used for both probe excitation and sensing. Barely visible impact damage (BVID) was introduced in the composite beam to examine the potential of the approach for the detection of very small, localized damage. The results show that the use of VAM with a broadband low-frequency excitation may be an effective option for identifying nonlinearities associated with typical damage occurring in composite structures.