Vibration-based Structural Health Monitoring Research Articles

Potential of Non-Contact Dynamic Response Measurements for Predicting Small Size or Hidden Damages in Highly Damped Structures.

Vibration-based structural health monitoring (SHM) is essential for evaluating structural integrity. Traditional methods using contact vibration sensors like accelerometers have limitations in accessibility, coverage, and impact on structural dynamics. Recent digital advancements offer new solutions through high-speed camera-based measurements. This study explores how camera settings (speed and resolution) influence the accuracy of dynamic response measurements for detecting small cracks in damped cantilever beams. Different beam thicknesses affect damping, altering dynamic response parameters such as frequency and amplitude, which are crucial for damage quantification. Experiments were conducted on 3D-printed Acrylonitrile Butadiene Styrene (ABS) cantilever beams with varying crack depth ratios from 0% to 60% of the beam thickness. The study utilised the Canny edge detection technique and Fast Fourier Transform to analyse vibration behaviour captured by cameras at different settings. The results show an optimal set of camera resolutions and frame rates for accurately capturing dynamic responses. Empirical models based on four image resolutions were validated against experimental data, achieving over 98% accuracy for predicting the natural frequency and around 90% for resonance amplitude. The optimal frame rate for measuring natural frequency and amplitude was found to be 2.4 times the beam's natural frequency. The findings provide a method for damage assessment by establishing a relationship between crack depth, beam thickness, and damping ratio.

Enhanced operational modal analysis and change point detection for vibration-based structural health monitoring of bridges

One of the most promising uses of vibration-based structural health monitoring (VBSHM) in bridge damage detection is the tracking of modes through long-term repeated or continuous operational modal analysis (OMA). Any shifts in modal parameters over time can signal structural damage. However, in real-world applications, noise and environmental uncertainties introduce variability in the data, potentially obscuring damage-related changes. To address this, it is essential to establish and understand the temporal trends and behavior of the estimated modal parameters, enabling accurate interpretation of the engineering data. This paper presents a detailed study focusing on data-driven techniques to improve the OMA results by determining the causes of modal variability and establishing modal models to filter out these known causes of variability. It explores the use of data continuously collected over a period of one month in November 2017 on the Confederation Bridge in eastern Canada. Operational modal analysis is conducted to extract modal frequencies and mode shapes, revealing correlations with environmental and operational factors such as wind, temperature and vehicular traffic. A novel approach using the residuals from regression modal models for damage detection is proposed, utilizing a change point detection algorithm. Results indicate the potential to detect shifts in modal frequencies corresponding to damage scenarios, at lower levels than was previously possible, highlighting the feasibility of using enhanced modal features for sensitive damage identification. Overall, the paper contributes to advancing the understanding of variability in vibration-based structural health monitoring and presents a promising practical technique for improving damage detection results using enhanced operational modal estimates in realistic field applications of a real-world structure.

Hybrid operational modal analysis of an operative two-bladed offshore wind turbine

As with any strategic structure, vibration-based structural health monitoring techniques are often used to ensure the structurally safe operation of offshore wind turbines. Among such techniques, Operational Modal Analysis (OMA) methods allow the identification of modal properties, such as natural frequencies, mode shapes and damping, which variation might be caused by damage or operational/environmental factors. This paper investigates the application of OMA techniques on a two-bladed offshore wind turbine, which poses multiple challenges: fundamental OMA assumptions about the applied loads are violated by environmental and operational loads; the closely spaced modes of an offshore wind turbine are hard to identify; and an operative two-bladed offshore wind turbine is a time-variant system. Within this study, three OMA procedures to overcome some of the preceding challenges are discussed: (1) a standard frequency domain decomposition method; (2) a proposed enhanced transmissibility-based approach with a post-processing technique based on the Kurtosis index; and (3) a proposed refined hybrid OMA procedure that combines a transmissibility-based approach, the dedicated post-processing technique based on the Kurtosis index, and the frequency domain decomposition method. A numerical model representative of an operative two-bladed offshore wind turbine is used to compare the three procedures. Based on the comparison, the hybrid method is proven to be a promising new OMA-based procedure that outperforms the stand-alone transmissibility-based approach and the frequency domain decomposition method in identifying the modal properties of a two-bladed offshore wind turbine.

Free Vibration Response Studies on Bridges

Abstract Bridges are essential components of the transportation network. These are unique constructions that serve as a transportation link between regions separated by physical barrier. Because of their critical role in ensuring passenger safety, these structures require constant maintenance. When vehicle cross a bridge, the structural components vibrate, which over time may cause damage to these components. The preferred method of maintaining structural health is currently seeing a rise in the use of Structural Health Monitoring (SHM) systems on infrastructure, especially bridges. In order to overcome the drawbacks of visual inspection methods, it is crucial to continuously monitor the integrity and analyse the dynamic properties of bridges. This work provides a comprehensive analysis of bridge health monitoring, emphasizing three key elements: vibration-based structural health monitoring (SHM), vehicle-bridge interaction analysis, and machine learning integration. The work improves monitoring, maintenance, and safety protocols by expanding our knowledge of structural integrity assessment for bridges by a detailed examination of the convergence of these domains. Notably, the work uses a condensed 2D model to forecast bridge natural frequencies Understanding the dynamic behaviour and structural integrity of the vital infrastructures is greatly aided by free vibration response research on bridges. The results, insights, and new directions in the subject of bridge free vibration analysis are summarized in this review paper. The uses and drawbacks of several analytical, numerical, and experimental techniques are covered. The research on the free vibration response of bridges is also reviewed in this review paper, with an emphasis on new developments incorporating machine learning principles.

Data-driven sensor fault diagnosis for vibration-based structural health monitoring under ambient excitation

In vibration-based structural health monitoring (SHM), the early detection of sensor faults is key to preventing false alarms and misleading conclusions on the condition of monitored structures. Since sensor networks are exposed to hostile environments, they are prone to unexpected errors that might influence the quality of measured data. This paper proposes a novel method for detecting and isolating faulty sensors from vibration response data by establishing an overdetermined system between the measured signals and the actual motion. The method assumes a rigid body motion of the monitored system, describable by a limited number of degrees of freedom (DOFs), to define the overdetermined relation between the sensor outputs and the system’s DOFs. The concept is later extended to systems not governed by rigid body motions by considering their vibration mode shapes. The robustness of the proposed methodology is demonstrated using vibration response data from an experimental monitoring campaign.

Deep-Learning-based damage detection of an experimentally tested full-scale wind turbine blade.

Reducing the Operation and Maintenance (O&M) costs of Wind Farms can substantially contribute towards adopting environmentally sustainable and cost effective energy sources. O&M costs in Wind Turbines can account up to 50% of the Turbine’s whole life cycle, with the majority of it being the frequent manual inspection needed for Wind Turbine Blades (WTB). Vibration-based structural health monitoring (SHM) approaches can be used for detecting WTB damages in an early state, before they lead to catastrophic failure, while they can be used to perform inspection on an informed basis, reducing O&M costs. In the present study, a data-driven damage detection framework is developed for WTBs. A Finite Element Model (FEM) of a full-scale WTB (the Aventa AV-7 “Light Wind Turbine”, sold under the Leichtwindanlagen© name) is developed in ABAQUS FEA using sparce material properties and dimensions and subsequently modified to match a physical WTB. Damage is modelled in the form of a Trailing Edge (TE) longitudinal crack propagated in a stepwise manner. The FE model is updated based on the geometry altering and material property degrading effects of the TE crack. Numerical simulations are performed to obtain the dynamic responses of the blade, under white noise and chirp excitation. A damage detection framework is then developed using Auto-Regressive, with exogenous excitation (ARX) or without (AR), models and its Nonlinear counterparts NARX and NAR models. In addition, model dimensionality reduction is performed using Principal Component Analysis (PCA) and Deep-Autoencoders (D-AE). The results are presented in terms of Receiver Operating Characteristics (ROC) curves.

Experimental vibration-based output-only damage localization of mechanical systems

Vibration-based Structural Health Monitoring (SHM) is becoming increasingly widespread in a variety of engineering sectors as its variability enables diverse applications. It enables full-time assessment of the status of a structure, allowing for early maintenance and repair to prevent further damage or even failure. The presented study is part of the SPP100+ program, which has the objective of extending the usability of complex structures. This study presents an experimental investigation of the damage localization of mechanical structures while considering varying environmental and operational conditions (EOC). For this, a testing field was designed and applied, enabling one to study the effects of the varying conditions in a comparable way. The research is based on the approach to modify the state projection estimation error (SP2E) method to account for the influence of EOC. The proposed method utilizes output-only system identification and state space parameterization to estimate the current structural state. Stochastic subspace identification (SSI) and H_∞ -estimation are employed to identify the state space model during a learning phase. Subsequently, the SP2E method is applied during the monitoring phase to localize structural alterations. Moreover, an algorithm is introduced to estimate the weighting factors of different environmental and operational conditions. To verify the efficacy of the proposed method, an extensive experimental study is conducted. The numerical analysis successfully demonstrates the applicability of the method for localizing mass perturbation and stiffness degradation under various EOC. This work contributes to proactive maintenance and damage detection for mechanical structures under varying conditions.

Smartphone-Based Vibration Monitoring Studies of a Pedestrian Link Bridge

Vibration-based structural health monitoring has gained a new pace with the rise of emerging consumer-grade sensor technologies. Accelerometers embedded in smartphones are among those which provide ubiquitous data potential with minimal burden to administrators. This research presents the preliminary findings of modal identification results obtained from smartphone sensors positioned on a single-span pedestrian link bridge. While a good number of modal identification studies are available in the literature, most of them correspond to short-term tests which are unlikely to capture seasonal features impacting identification results. In this study, the authors summarise a large vibration dataset from the testbed bridge with repetitive measurements taken over a month. The identification results presented herein are early representations of the daily and monthly temporal behaviour of modal frequencies observed throughout the tests.

Bayesian Parameter Estimation of Vibrating Systems via Physics-Based Biases from Neural Networks

Often, Structural Health Monitoring (SHM) campaigns draw damage-identifiers from vibration-based monitoring data. One of the downstream tasks of vibration-based SHM is that of system identification, i.e., inference of a model which is able to describe the system. For vibration-based monitoring, such models often rely on partial-differential equation formulations, where the identification task may be tied to inference of the parameters of such models. The probabilistic nature of SHM incentivises the extraction of stochastic estimates of these parameters, allowing for propagation of uncertainties associated to measurement noise or model imprecision. Common approaches to generating such stochastic estimates require an assumption on the shape and independence of the posterior distributions. To circumvent these assumptions, the Hamiltonian Monte-Carlo (HMC) sampling approach simulates the posterior distribution, allowing for freedom in the posterior. However, this requires intensive computational effort, especially when the governing equations have increased complexities, such as nonlinearities. Physics-informed neural networks (PINNs) utilise physics-based objective functions in the form of known PDEs, exploiting the automatic differentiation of NN architectures. This implies that additional terms in the PDE, such as nonlinearities, or boundary conditions can be accounted for. When the domain of observations encapsulates the collocation domain of the PINN, they can be exploited for parameter identification, by optimising over these parameters in addition to the network weights. However, classic objective functions in the form of mean-squared-norms underperform on noisy data, as a consequence of their deterministic nature. In this work, PINNs are combined with a HMC sampler to estimate the posterior distribution of the parameters of dynamic systems with noisy measurements. Multiple types of dynamic systems are simulated, varying the system complexity in terms of type and strength, and with different levels of noise. The results are assessed against the known values, and the codependent posterior distributions of each system are discussed.

Automated modal parameter tracking with neural network based normalization of environmental perturbations

Automated modal parameter tracking is an essential part of vibration-based structural health monitoring of engineering structures, e.g., bridges, high-rise building, wind turbines, amongst others. In this work, we conceive a robust algorithm for accomplishing this task. First, the modal parameters are extracted with by greedy clustering the stabilization diagram, which is based on covariance-driven subspace identification and the related uncertainty quantification. After the selection of a set of relevant modal parameters to be monitored, the tracking is performed by an active search of stable modes close to the reference ones, again using statistical criteria and automated clustering. The applied strategies allow for a robust method, allowing also for relatively strong variations in the natural frequencies or the MAC without losing track of the monitored modes. Finally, when EOVs (Environmental and Operational Variables) are measured and training and testing periods are defined, a neural network is trained based on the tracked frequencies and the EOVs, with the goal of correcting the environmental influence on the estimated frequencies based on the model predictions. In doing so, a distance metric is defined for damage detection, which is robust to environmental perturbations under the measured EOVs. The whole framework is showcased on monitoring data of a full-scale bridge.

Full life-cycle vibration-based monitoring of a full-scale masonry arch bridge with increasing levels of damage

Structural health monitoring (SHM) is of great significance to maintain the safe operation of infrastructure, particularly for historic structures such as masonry arch bridges, as their material properties are deteriorating under effects such as climate change, but the demands of modern transport systems increase significantly. Vibration-based SHM has become a prevalent method for assessing the vulnerability of masonry arch bridges. However, one of the most critical limitations lies in the inability of correlate bridge’s damage accumulation with the variation trends in its modal parameters. To address this issue, in the present study, full life-cycle vibration-based monitoring was conducted on a full-scale masonry arch bridge, progressing from an undamaged state to failure under laboratory conditions. Accumulated damage was induced by applying point loads with increasing magnitudes until the bridge failed. Vibration data, collected after each loading test, were analysed utilizing the stochastic subspace identification (SSI) method, and frequency and damping ratio for the first five modes were identified. The results demonstrated that the first-order frequency had the most pronounced correlation with the stiffness degradation and the damage accumulation in the masonry arch bridge. The evolution of early-stage damage, the formation of the first hinge, and the activation of a four-hinge mechanism were successfully captured through the frequency degradation. The findings of the study may provide valuable insights into the damage assessment of historic masonry arch bridge infrastructures.

Vibration-based SHM of railway steel arch bridge with orbit-shaped image and wavelet-integrated CNN classification

The study presents the data-driven applications of continuous wavelet transforms (CWT), orbit-shaped analysis and convolutional neural networks (CNN) using GoogLeNet for Dębica railway bridge health monitoring in Poland. Training and validation data sets are the dynamic behavior of the bridge deck recorded through an IEPE vibration sensor with a sampling frequency of 128 Hz from vibration-based structural health monitoring (SHM) system over a nine-month period from December 2019 to September 2020. Utilizing Morse, Morlet, and Bump wavelet, the vibration signal scalogram images are produced in the frequency–time domain as the input for CNN classification models, while the output is to predict health states based on the experimental tension force of eight hangers using label thresholds developed by calibrated finite element model. Moreover, the vibration-based orbit-shaped image patterns, acquired through a bidirectional sensor on each hanger are processed with CNN classification models for automated hanger health diagnostic. The accuracies and weighted F1-scores of the wavelet-assisted and orbit-shaped CNN models are more than 83% on imbalanced validation images. The results show that the proposed CNN approaches can classify the potential structural problems.

Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring

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.

Improving the dynamic behaviour of historic buildings using experimental data: application to a Baroque church

Vibration-based Structural Health Monitoring represents an efficient and reliable tool to build models that can predict the dynamic behaviour of heritage structures for, but not limited to, monitoring purposes. Indeed, built heritage consists of unique structures often characterized by high fragility, and the design of interventions implies several challenges, also related to restrictions on the application of modern regulations. In this context, the corroboration of the mechanical model should accompany the entire process, from design to implementation, with test campaigns performed before and after the strengthening operations to assess their effectiveness. Thus, the work presents the experimental process used for the tailoring of the seismic upgrading interventions on the church of Santa Caterina, in Casale Monferrato. More recently, the model has been updated with the results acquired from a permanent monitoring system installed in 2022, following the interventions, hence allowing assessments of their effectiveness.

An approach to seismic damage detection and evaluation in RC bridge piers through vibration data

A timely post-earthquake damage evaluation in existing bridges is fundamental for an efficient emergency management. Structural Health Monitoring (SHM) systems can allow a fast damage assessment; however, a lack of methods for the SHM data interpretation is recognised. The present paper proposes an approach for the probabilistic seismic damage detection and quantification in ductile bridge piers from vibration-based SHM data by comparing time variations of modal properties. To this aim, the selected modal properties are natural frequencies and mode-shapes, expressed in terms of CoMAC and ECoMAC indices. Experimental results of quasi-static cyclic tests and output-only modal identification tests are adopted to calibrate a reliable finite element model able to predict the variations in fundamental modal properties of ductile RC piers due to seismic damage. The calibrated model is used to perform probabilistic analyses to derive probability distributions for the variation of the considered modal-based features of piers at different seismic damage levels. The approach provides fragility models to support a timely assessment of serviceability levels in monitored assets in the immediate aftermath of an earthquake by monitoring the variation of natural frequencies. Conversely, CoMAC and ECoMAC resulted slightly sensitive to seismic damage for the investigated piers portfolio.

Vision-based algorithm for structural response measurement using movable camera and damage localization

Vibration-based Structural Health Monitoring (SHM) is widely recognized as a prominent approach for evaluating the safety and integrity of civil infrastructure. This research presents a novel algorithm for vibration measurement using a movable camera, effectively overcoming the issue of signal drift caused by camera motion. The proposed algorithm comprises three modules: optical flow computation, camera pose estimation, and motion compensation. Validation experiments by virtual images demonstrate accurate camera pose estimation and effective mitigation of drifting signals. The dynamic characteristics of the frame structure are extracted using a movable camera and the proposed algorithm. Several challenges are addressed, including accurately estimating the scale factor to compensate for the drifting signals. Experimental validation is conducted to verify the proposed approach. The results demonstrate the efficacy and promising potential of the proposed approach. Finally, this study demonstrates the practical application through damage localization experiments.

УСПЕХИ В МОНИТОРИНГЕ СОСТОЯНИЯ КОНСТРУКЦИЙ: ОБЗОР ПОДХОДОВ МАШИННОГО ОБУЧЕНИЯ ДЛЯ ОБНАРУЖЕНИЯ И ОЦЕНКИ ПОВРЕЖДЕНИЙ

Structural Health Monitoring (SHM) is a crucial discipline geared towards detecting damage in engineering structures early, aiming to prevent failures and facilitate condition-based maintenance. Traditional SHM methodologies, relying on visual inspections, analytical models, and signal processing, exhibit inherent limitations. The advent of machine learning has introduced data-driven solutions to automate various aspects of SHM, including damage detection, localization, classification, and prognosis.
 This paper provides a comprehensive review of recent studies exploring supervised, unsupervised, and deep learning techniques in vibration-based, image-based, and multi-sensor SHM. Support vector machines, neural networks, deep convolutional neural networks, and other advanced algorithms have demonstrated exceptional performance in assessing damage using real-world structural datasets.
 Despite these successes, practical challenges persist, particularly in addressing variability and deploying machine learning models effectively on full-scale structures. Overcoming these challenges necessitates a more integrated, cross-disciplinary approach, merging mechanical engineering fundamentals with machine learning expertise. This synergy can pave the way for robust field implementation and further enhance the reliability of SHM systems.
 The transformative potential of machine learning in SHM cannot be understated. Beyond merely shifting from time-based maintenance to condition-based strategies, machine learning can automate and continuously evaluate structural integrity, ensuring the longevity of engineering structures. As we delve deeper into the intersection of mechanical engineering and machine learning, the prospect of a future where SHM seamlessly integrates with advanced technologies becomes increasingly tangible.

A Hybrid Perspective of Vision-Based Methods for Estimating Structural Displacements Based on Mask Region-Based Convolutional Neural Networks

Abstract Vision-based methods have shown great potential in vibration-based structural health monitoring (SHM), which can be classified as target-based and target-free methods. However, target-based methods cannot achieve subpixel accuracy, and target-free methods are sensitive to environmental effects. To this end, this paper proposed a hybrid perspective of vision-based methods for estimating structural displacements, based on Mask region-based convolutional neural networks (Mask R-CNNs). In proposed methods, Mask R-CNN is used to first locate the target region and then target-free vision-based methods are used to estimate structural displacements from the located target. The performances of proposed methods were validated in a shaking table test of a cold formed steel (CFS) wall system. It can be seen that Mask R-CNN can significantly improve the accuracy of feature point matching results of the target-free method. The comparisons of estimated structural displacements using proposed methods are conducted and detailed into accuracy, stability, and computational burden, to guide the selection of the proper proposed method for the specific problem in vibration-based SHM. Proposed methods can also achieve even 1/15 pixel-level accuracy. Moreover, different image denoising methods in different lighting conditions are compared.

Internet of things (IoT)-based structural health monitoring of laboratory-scale civil engineering structures

Rapid advances in the Internet of Things (IoT) domain have made it a crucial technology for the real-time structural health monitoring (SHM) of civil engineering infrastructures. The availability of quick and accurate vibration data is essential for SHM, and such data can be obtained through IoT devices mounted on the structures. This study proposes a real-time damage prediction and localization approach using a low-cost "do-it-yourself" wireless sensor node with IoT capabilities for SHM. The proposed sensor node comprised a microcontroller (NODE MCU ESP8266) and a 6-axis accelerometer (MPU6050). The IoT devices track the real-time frequency of the laboratory-scale structure indirectly via measurement of acceleration-time history, and their results are compared with conventional industry-standard accelerometers. Promising results, with a <6% average difference from the conventional accelerometer (difference ranging from 1.3 to 14.3%), provided an innovative SHM for vibration-based real-time SHM using the IoT paradigm. The performance of the proposed methodology was validated numerically and experimentally on two laboratory-scale structures, and the potential of IoT technology for enhancing the efficiency of SHM was demonstrated. The proposed method thus can enable the early detection of damages in infrastructures such as buildings and bridges and thus can reduce the likelihood of accidents via continuous SHM.

Damage Classification of a Three-Story Aluminum Building Model by Convolutional Neural Networks and the Effect of Scarce Accelerometers

Structural health monitoring (SHM) plays a crucial role in extending the service life of engineering structures. Effective monitoring not only provides insights into the health and functionality of a structure but also serves as an early warning system for potential damages and their propagation. Structural damages may arise from various factors, including natural phenomena and human activities. To address this, diverse applications have been developed to enable timely detection of such damages. Among these, vibration-based methods have received considerable attention in recent years. By leveraging advancements in computer processing capabilities, machine learning and deep learning algorithms have emerged as promising tools for enhancing the efficiency and accuracy of vibration-based SHM. This study focuses on the application of convolutional neural networks (CNNs) for the classification and detection of structural damage within a steel-aluminum building model. An experimental platform was devised and constructed to generate data representative of building damage scenarios induced by bolt loosening. Both the typical placement of sensors on each floor and the utilization of only one accelerometer were employed to understand the effect of scarcity of accelerometers. By subjecting the building model to controlled vibrations and environmental conditions, the response data from both sensor configurations were collected and analyzed to evaluate the effectiveness of the CNN approach in detecting structural damage under varying sensor deployment strategies. The findings demonstrate that the CNNs exhibited high accuracy in both damage classification and detection, even under scenarios with limited sensor coverage. Moreover, the proposed method proved effective in identifying structural damage within building structures.