Damage Identification Methodology Research Articles

Effectiveness of Vibration-Based Techniques for Damage Localization and Lifetime Prediction in Structural Health Monitoring of Bridges: A Comprehensive Review

Bridges are essential to infrastructure and transportation networks, but face challenges from heavier traffic, higher speeds, and modifications like busway integration, leading to potential overloading and costly maintenance. Structural Health Monitoring (SHM) plays a crucial role in assessing bridge conditions and predicting failures to maintain structural integrity. Vibration-based condition monitoring employs non-destructive, in situ sensing and analysis of system dynamics across time, frequency, or modal domains. This method detects changes indicative of damage or deterioration, offering a proactive approach to maintenance in civil engineering. Such monitoring systems hold promise for optimizing the management and upkeep of modern infrastructure, potentially reducing operational costs. This paper aims to assist newcomers, practitioners, and researchers in navigating various methodologies for damage identification using sensor data from real structures. It offers a comprehensive review of prevalent anomaly detection approaches, spanning from traditional techniques to cutting-edge methods. Additionally, it addresses challenges inherent in Vibration-Based Damage (VBD) SHM applications, including establishing damage thresholds, corrosion detection, and sensor drift.

Towards a comprehensive damage identification of structures through populations of competing models

AbstractModel-based damage identification for structural health monitoring (SHM) remains an open issue in the literature. Along with the computational challenges related to the modeling of full-scale structures, classical single-model structural identification (St-Id) approaches provide no means to guarantee the physical meaningfulness of the inverse calibration results. In this light, this work introduces a novel methodology for model-driven damage identification based on multi-class digital models formed by a population of competing structural models, each representing a different failure mechanism. The forward models are replaced by computationally efficient meta-models, and continuously calibrated using monitoring data. If an anomaly in the structural performance is detected, a model selection approach based on the Bayesian information criterion (BIC) is used to identify the most plausibly activated failure mechanism. The potential of the proposed approach is illustrated through two case studies, including a numerical planar truss and a real-world historical construction: the Muhammad Tower in the Alhambra fortress.

Damage Identification in Cement-Based Structures: A Method Based on Modal Curvatures and Continuous Wavelet Transform.

Modal analysis is an effective tool in the context of Structural Health Monitoring (SHM) since the dynamic characteristics of cement-based structures reflect the structural health status of the material itself. The authors consider increasing level load tests on concrete beams and propose a methodology for damage identification relying on the computation of modal curvatures combined with continuous wavelet transform (CWT) to highlight damage-related changes. Unlike most literature studies, in the present work, no numerical models of the undamaged structure were exploited. Moreover, the authors defined synthetic damage indices depicting the status of a structure. The results show that the I mode shape is the most sensitive to damages; indeed, considering this mode, damages cause a decrease of natural vibration frequency (up to approximately -67%), an increase of loss factor (up to approximately fivefold), and changes in the mode shapes morphology (a cuspid appears). The proposed damage indices are promising, even if the level of damage is not clearly distinguishable, probably because tests were performed after the load removal. Further investigations are needed to scale the methodology to in-field applications.

Cross-scale data-based damage identification of CFRP laminates using acoustic emission and deep learning

Damage characterization of laminated composites has undergone extensive research, leading to the development of several damage models that incorporate acoustic emission (AE). However, feature extraction from AE data will cause the loss of certain information in the construction of damage models, which manifestly falls short of feature analysis in the frequency domain. In this study, a novel cross-scale data-based damage identification methodology for carbon fiber-reinforced polymer (CFRP) laminates is proposed by combining the AE technique with deep learning approach. Elementary experiments involved with single damage mode are designed to avoid blind inference, and the cross-scale correlation of AE features between component materials and CFRP laminates is established by wavelet packet transform (WPT). The time–frequency spectrums of AE signals of CFRP laminates by continuous wavelet transform (CWT), which adequately preserve the frequency domain information, are adopted as inputs to the convolutional neural network (CNN) model. The proposed methodology achieves a high accuracy of 96.3% in detecting and classifying damage modes (i.e., matrix cracking, matrix/fiber debonding, and fiber breakage) in unidirectional CFRP laminates.

Combined Joint and Member Damage Identification of Semi-Rigid Frames with Slender Beams Considering Shear Deformation

A damage identification methodology considering shear deformation was presented in this paper to identify damage to semi-rigid frames with slender beams. On the basis of the successful identification of structural joint damage, the combined joint and member damage of the structure was identified. The objective function was formulated to minimize the discrepancies between the analytical and measured nodal displacements. Damage identification was performed on semi-rigid frame structures with different cross-sectional shapes, and the results were compared with those of ignoring shear deformation. Several frame structures were employed to verify the advantages and efficiency of the proposed method. The results demonstrate that the present method could significantly improve the accuracy of damage identification for semi-rigid frames compared with the method ignoring shear deformation.

Damage identification in beams using burst video-records during free-vibration

In Structural Health Monitoring, computer vision has emerged as a promising approach for dynamic measurement; especially for its full-field characteristic. However, real-world implementations are still sparse due to several reasons. One of them is that vibration-based damage identification methods usually rely on Output-Only Modal-Analysis which, to be accurate, requires very long records with stochastic excitation or relatively long free-vibration records. The first option hinders video taking, storage and processing; specially with high-speed cameras. The second is statistically difficult under common operating conditions. To overcome such difficulties, this paper presents a methodology for damage identification in beams; which is vibration-based and Output-Only, but does not rely on Modal (or other Fourier-based) Analysis. Therefore, it can be fed with very short free-vibration displacement time histories; e.g., extracted from burst video records. The key idea is using the recently developed Synthesis of the Healthy-Structure Model Response, but interpreting the accelerations in free-vibration as an inertial pseudo-loading. The likely problem of high levels of noise is addressed by separating damage detection and localisation (using a statistical approach) from damage quantification (which is physics based). In an experimental example, the methodology showed outstanding detection and localisation performance with low quantification error.

Design and testing of a decision tree algorithm for early failure detection in steel truss bridges

This paper develops a methodology for damage identification in steel truss bridges that uses vibration-based monitoring data and a model-based decision tree algorithm. The methodology resorts to a calibrated FE model with an optimization-based parameter identification procedure to simulate and analyze all the potential damages that might affect the structure. The effect of environmental conditions on the modal parameters is also accounted for, which is modeled as structure stiffness variations using the Young’s modulus and forecasted using a surrogate modeling strategy. The feasibility of the methodology is demonstrated on a full-scale bridge in Vilagarcía de Arousa, Spain. The underlying hypotheses used in the algorithm implementation were validated, and the error ponderation and selection bound employed to detect and identify damage were optimized. The results show an average success rate of 95.0% and an average false positive rate of 1.0% in identifying damage indicating its robustness to be extrapolated to other case studies.

Evaluating pod-based unsupervised damage identification using controlled damage propagation of out-of-service bridges

The effectiveness of Proper Orthogonal Decomposition (POD) for damage identification on out of service, rural bridges is examined using field data. Three similar, out of service, simply supported bridges, consisting of steel beams supporting a concrete deck, were tested prior to demolition and replacement. Strain time histories were recorded using optimal sensor networks with a vehicle of known weight crossed each bridge in different transverse locations and directions, and at varying speeds. The effectiveness with which the developed POD based methodology for damage identification was evaluated by progressively flame cutting select steel beam flanges and webs on each bridge. Snapshot matrices of structural response as measured by the sensors were used to create Proper Orthogonal Modes (POMs), and were obtained for each damage level and test, with the POMs being used to identify if changes in structural response were detected. The accuracy with which the POMs identified damage locations and their sensitivity to vehicle location and direction were evaluated. In addition, a previously developed novelty detection framework proposed by some co-authors was applied to identify changes after damage. It is shown that POD and the developed novelty index can effectively identify certain levels of imposed damage in tested bridges under known loads.

Delamination identification in sandwich composite structures using machine learning techniques

Recent advances in machine learning have enabled powerful strategies for autonomous data-driven damage detection and identification in structural systems. This work proposes a novel method for 3D delamination identification in sandwich composite structures. Sandwich structures are prone to damage that is difficult to detect, necessitating efficient and complex inspection techniques. In this context, automated structural health monitoring techniques are of great impact and importance in engineering. Such techniques use data obtained by sensors to identify damage to the structure. In this paper, a complete methodology for damage (delamination) identification in sandwich composite structures using machine learning is proposed. The damage was parameterized in two different ways: as parametrized two- and three-dimensional ellipses, and it was considered in three different groups: the core, interface, and skin. The modal data, obtained by the finite element method, was used to train several machine learning models in order to classify the location of the damage. In addition, modal dataset was also used to train artificial neural network regression models for damage localization and sizing. Both classification and regression strategies showed substantial results, and the models proved to be robust enough to identify a wide variety of damages. Results showed that classification models correctly identified the damage on the composite skin and incorrectly identified the interface core-skin damage. The regression model proved to be reliable in identifying an approximate location with an average accuracy of 85%. However, damage sizing is still a challenge to predict based only on modal datasets.

Nonlinear static analyses to improve the seismic damage assessment of monitored masonry palaces: application to the Consoli Palace of Gubbio, Italy

The conservation of the built architectural heritage in seismic-prone regions is a topical task in modern civil engineering. The ongoing diffusion of structural health monitoring systems calls for innovative and efficient methodologies for damage identification and condition assessment from field data. For this purpose, the paper proposes a novel model-based and data-informed approach relating the variation of the modal properties of the structure to increasing levels of seismic damage, looking into the outcomes of pushover analysis from a frequency-domain perspective. The direct problem is addressed through nonlinear static analyses, in which the structure is progressively damaged by applying simplified distributions of earthquake-induced horizontal forces, followed by a modal analysis at each degraded damage state. Addressing the inverse problem, starting from experimentally identified variations of the spectral properties and exploiting the simulation results, efficiently provides valuable insights into potential damage levels suffered by the structure. The forward methodology is exemplified by the application to a continuously monitored monumental masonry palace, the Consoli Palace of Gubbio, Italy, which was recently hit by a low-to-medium intensity earthquake and exhibited a slight reduction of its natural frequencies.

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies.

Quasi-Static Influence Line Identification and Damage Identification of Equal-Span Bridges Based on Measured Vehicle-Induced Deflection

The bridge influence line (IL) reflects the response of a certain section due to varying load positions. As a result, IL has a wide application prospect in damage identification and condition assessment. Up to date, studies regarding IL have been focused on the structure condition evaluation. A feasible and practical method for damage identification is still not yet available. The present paper proposes a comprehensive damage identification methodology based on IL under a moving vehicle is composed of data pre-processing, IL extraction, and damage detection. Firstly, a thorough review of existing IL identification methods based on signal processing is provided. Then three quasi-static IL identification methods based on measured data are discussed. Consequently, the study proposes a two-stage damage identification approach for simply supported bridges with equal span length. Also, the effectiveness of this approach is verified through field tests on a real girder bridge. At last, conclusions are drawn, and potential issues for the application of the proposed method in practice are discussed.

Least Angle Regression for early-stage identification of earthquake-induced damage in a monumental masonry palace: Palazzo dei Consoli

This paper presents a novel methodology for earthquake-induced damage identification of historical constructions through sparse multivariate regression. The proposed methodology comprises a first data cleansing stage using the minimum covariance determinant (MCD) method to mitigate the adverse effects related to the existence of outliers in the training feature dataset. Afterwards, a sparse multiple linear regression model (SMLR) is trained using the least-angle regression (LAR) model to eliminate the influence of environmental effects upon the selected features set. The proposed SMLR model allows to identify the optimal set of predictors in a fully automated way, minimizing the need for expert judgement in the process. The effectiveness of the proposed approach is demonstrated with an application case study of a monumental masonry palace, the Consoli Palace in Gubbio (Italy). The palace has been monitored with an aggregated static/dynamic/environmental SHM system since July 14th 2020. A recent seismic sequence of small intensity hit the palace on May 15th 2021 with a main earthquake of magnitude Mw 4.0. The epicentres of the main seismic event and the following aftershocks were located at a distance of 2–3 km far from the palace, making this case study a prominent example of a monumental construction subjected to near-field ground motion. The presented results demonstrate that a new damage condition arises in the Consoli Palace after the seismic sequence, although its severity remains at an early stage not detectable by visual inspections.

Structural damage assessment in lattice towers based on a novel frequency domain-based correlation approach

The goal of this experimental study is to validate a novel vibration-based damage identification methodology for lattice structures. To this end, this work has determined experimentally the degradation state of a lattice tower under different damage conditions by assessing variations to its vibration response, which lead to the identification of spectral-domain indicators that correlated with the structure’s severity of damage. These indicators are derived from the complex frequency domain assurance criterion (CFDAC) matrices, which are obtained by comparing the vibration response of the structure between damaged and undamaged scenarios. The results showed that there exists a correlation between the proposed spectral correlation indices (SCI) and changes to stiffness and mass in the lattice tower. The results obtained in this work further demonstrate the viability of this technology as an innovative tool for autonomous and remote structural monitoring.

Neural network-based damage identification in composite laminated plates using frequency shifts

Delamination is the principal mode of failure of laminated composites. It is caused by the rupture of the fiber–matrix interface and results in the separation of the layers. This failure is induced by interlaminate tension and shear that is developed due to a variety of factors such as fatigue. Composites are known for excellent structural performance, which can be significantly affected by delamination. Such damages are not always visible on the surface and can lead to sudden catastrophic failures. To ensure a structural performance and integrity, accurate methods to monitor damages are required. This work presents a methodology for damage detection and identification on laminated composite plates using artificial neural networks fed with modal data obtained by finite element analysis. The proposed neural network to quantify damage severity achieved up to 95% success rate. A comparative study was done to evaluate the effect of boundary conditions on damage location. With the comparative study results, another neural network was proposed to locate damage position, achieving excellent results by successfully locating or by significantly reducing the search area. Both proposed ANNs use only frequency variation values as inputs, an easily obtainable quantity that requires few equipment to be acquired. The obtained results from these numerical examples indicate that the proposed approach can detect true damage locations and estimate damage magnitudes with satisfactory accuracy for this particular geometry, even under high measurement noise.

A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining

Implementation of data mining (DM) techniques in different areas of civil engineering has recently given very good results. However, application of DM in structural health monitoring (SHM) is not used as much as expected, thus, many challenges are still ahead. Therefore, it seems a vital need is required to develop the applicability of DM in SHM. To this end, the current study attempts to present a DM-based damage detection methodology using modal parameter data, which trained by means of a hybrid artificial neural network-based imperial competitive algorithm (ANN-ICA). Likewise, the hybrid ANN is optimized by a new optimization-based evolutionary algorithm, called ICA, to predict the severity and location of multiple damage cases obtained from experimental modal analysis of intact and damaged slab-on-girder bridge structures. Furthermore, the applicability of DM approach was developed to detect the hidden patterns in vibration data using Cross Industry Standard Process for DM (CRISP-DM) tool. The performance of the model was carried out using comparison of a pre-developed ANN and ANN-ICA model.

Vibration-based damage identification on the suspension of a railway waggon – Findings from snap-back experiments with transient excitation

ABSTRACT This article deals with the damage detection at the suspension of a railway freight waggon and reveals that the measured accelerations change with the damage in a characteristic way. The authors show that the damage of the suspension is accompanied by a reduction in spring energy which causes a shift of the higher vibration modes in the measured signals. In this context, the paper did not intend to present new methodologies for damage identification. However, the simple findings from the experimental tests can be useful for the design of Structural Health Monitoring (SHM) systems for railway vehicles. At the end, a simple SHM approach for a low number of sensors with practice-oriented positions at the waggon is proposed.

An EMI-Based Clustering for Structural Health Monitoring of NSM FRP Strengthening Systems.

The use of fiber-reinforced polymers (FRP) in civil construction applications with the near-surface mounted (NSM) method has gained considerable popularity worldwide and can produce confident strengthening and repairing systems for existing concrete structures. By using this technique, the FRP reinforcement is installed into slits cut into the concrete cover using cement mortar or epoxy as bonding materials, yielding an attractive method to strengthen concrete structures as an advantageous alternative to the external bonding of FRP sheets. However, in addition to the two conventional failure modes of concrete beams, sudden and brittle debonding failures are still likely to happen. Due to this, a damage identification technology able to identify anomalies at early stages is needed. In this work, some relevant cluster-based methods and their adaptation to electromechanical impedance (EMI)-based damage detection in NSM-FRP strengthened structures are developed and validated with experimental tests. The performance of the proposed clustering approaches and their evaluation in comparison with the experimental observations have shown a strong potential of these techniques as damage identification methodology in an especially complex problem such as NSM-FRP strengthened concrete structures.

Crack identification at welding joint with a new smart coating sensor and entropy

Abstract A damage identification module is designed and studied to realize the both close and open crack detection and severity increment estimation at a welding joint area using a new smart coating sensor and entropy measurement. A new piezoelectric composite coating sensor is developed and applied at a welding joint of an operating structure to send warning and dynamic signals for the damage identification, when the crack occurs. Entropy calculation is introduced to measure the weak dynamic perturbations, which is caused by the strain concentration and/or crack breathing at the crack tip and hidden in the dynamic signal at the welding joint of the operating structure. A finite element model (FEM) of a welded beam experiencing dynamic base motions is established as a case study to show the efficiency of the proposed smart coating design and the entropy measurement method. The effects of strain concentration and crack breathing on the structural dynamic response are simulated by creating the nonlinear material property around the crack area and contact pair of crack walls, respectively. After obtaining the time domain vibration signal, crack severity increment is estimated using Sample Entropy. From simulations, it is found that even at very early stages of the crack (5% of the beam thickness), the entropy variation is significant for both closed and open cracks compared with the healthy beam indicating the high crack identification sensitivity with the smart coating and entropy. In addition, the variation ratio increases corresponding to the increment of the strain level at the crack tip, and it reaches a limit at a critical strain value. Different case studies are presented to prove the feasibility of the damage identification methodology on different beam structures. After that, an experimental study of an open crack with strain singularity at the crack tip is conducted to validate the simulation studies. The results show a consistent trend with the simulations. In this way, the augmentation of the damage severity or possibility to further propagate during operation of the structure can be noticed with the help of the new sensor and the entropy measurement. Detection and estimation of the open crack and its induced local strain concentration are realized by the new sensor, wavelet and entropy measurement of vibration signal for the first time.

Damage Identification of Periodically-Supported Structures Following the Bayesian Probabilistic Approach

This paper presents a probabilistic damage identification methodology tailor-made for periodically-supported structures with finite-length. The free wave motion of a general periodically-supported structure with a single disorder is analyzed through the characteristic receptance approach, and the corresponding frequency characteristic equation is developed. In addition, a concept of nondimensional frequency is introduced, and the sensitivity matrix of the nondimensional frequencies with respect to changes in stiffness of periodic cells is obtained by solving the frequency characteristic equation and utilizing the sensitivity analysis technique. Following the sensitivity-based identification equation with nondimensional frequency information, the probabilistic methodology for identifying the damage occurring in the periodically-supported structures is developed by implementing the Bayesian approach and the Markov chain Monte Carlo (MCMC) simulation with the Metropolis–Hasting sampling algorithm. The validity of the proposed methodology is demonstrated by both numerical simulations for a periodically-supported flanged pipeline example and experimental case studies conducted for a multi-span aluminum beam model endowed with bolted connections in the laboratory.