Damage Identification Approach Research Articles

An output-only damage identification method based on reinforcement-aided evolutionary algorithm and heterogeneous response reconstruction

The absence of excitation measurements may pose a huge challenge in the application of many damage identification methods since it is difficult to acquire the external excitations, such as wind load, traffic load. To deal with this issue, a novel output-only structural damage identification approach based on reinforcement-aided evolutionary algorithm and heterogeneous response reconstruction with Bayesian inference regularization is developed. On the one hand, heterogeneous measurements (e.g., displacements, strains, accelerations) are rescaled and reconstructed with the aid of Bayesian inference regularization technique. Structural damages are identified by minimizing the discrepancies between the measured and reconstructed responses. On the other hand, to solve the optimization-based inverse problem, a reinforcement-aided evolutionary algorithm, named Q-learning hybrid evolutionary algorithm (QHEA), integrating Jaya algorithm, differential algorithm, and Q-learning algorithm is proposed as search tool. To validate the feasibility and applicability of the proposed method, numerical studies on a three-span beam structure and laboratory tests on a five-story steel frame structure are carried out. The effects of data rescaling and data fusion on response reconstruction and damage identification are also investigated. The results clearly demonstrate the superiority of QHEA over other heuristic algorithms and heterogeneous data fusion over a single type of measurement. It is shown that both the locations and extents of the damaged elements can be accurately identified by the proposed method without the information of input force.

Automatic damage detection and data completion for operational bridge safety using convolutional echo state networks

Structural response data might be partially missing during acquisition and transmission by a structural health monitoring (SHM) system, affecting subsequent structural damage identification. Conventional deep learning methods require a substantial amount of data and have low training efficiency. With this in mind, this paper proposes a hybrid approach for data reconstruction and damage identification using an echo state network embedded with convolutional layers. The network has a training-free reservoir layer and focuses on historical information and multi-channel spatial features. As data redundancy occurs in most measured structural vibration responses, the extended Frobenius norm-principal component analysis method is applied to select valuable samples for updating the network, which greatly enhances the training efficiency. Experimental and numerical data from a physical bridge model is used to verify the data reconstruction capability and damage identification accuracy of the proposed method, and its performance is further compared with several existing deep learning methods.

A new damage identification approach of HFSGCW based on LDV and wavelet packet band energy method

This study introduces a damage assessment system and a pivotal detection technique for hidden frame-supported glass curtain wall (HFSGCW) structural sealant. The vibration performance of HFSGCW is evaluated using a laser Doppler vibrometer (LDV). The acquired signals undergo wavelet packet transformation for decomposition. The damage conditions of the structural sealant are analyzed by calculating the sum of squared differences (SSD) of the wavelet packet bands' energy. This SSD indicator serves as the damage indicator. The study investigates the effect of structural sealant damage on the HFSGCW's SSD indicator. Meanwhile, the LDV test results are corroborated with those from numerical simulation analysis to evaluate LDV's viability for detecting the SSD indicator in HFSGCW. The results indicated a strong correlation between the SSD indicator and the numerical modeling analysis. When the structural sealant was damaged, the SSD indicator showed sudden fluctuations, coupled with specific positional indications corresponding to the degree of damage, the type of damage (penetration or non-penetration), and the damage location. This indicator provides a basis for evaluating structural integrity. The research's effectiveness in guiding the construction and assessment of facades is demonstrated by its practical application in the project context.

A novel procedure for cable damage identification of cable-stayed bridge using particle swarm optimization and machine learning

The cables are crucial components in the ensuring safety of the stayed-cable bridges. The early identification and quantification of cable damage based on the inherent structural health monitoring (SHM) system is a priority to prevent disasters. In this study, a procedure is proposed to identify the cable damage in the cable-stayed bridges using the particle swarm optimization (PSO) and the categorical gradient boosting (CatBoost) algorithm. The PSO-based finite element model updating method is implemented to establish the baseline model while a practical advanced analysis program is used to generate simulation data. As an efficient and up-to-date machine learning (ML) algorithm, CatBoost is utilized to capture the complex nonlinear correlations between the vibration characteristics and the cable damages. A case study of a benchmark bridge where cable damage has been identified is considered to evaluate the efficiency of the proposed procedure. The fivefold cross-validation and grid search methods are used to find the optimal model. The accuracy of the proposed cable damage identification model using CatBoost is also verified through the comparison with three existing ML methods: random forest, decision tree, and extreme gradient boosting. The identification results of both simulation and real cases of cable damage demonstrate that the proposed procedure is a novel and powerful approach for cable damage identification of the cable-stayed bridge using measurement data of the existing SHM system.

A Novel Mayfly Algorithm with Response Surface for Static Damage Identification Based on Multiple Indicators

This paper proposes a novel structural damage identification approach coupling the Mayfly algorithm (MA) with static displacement-based response surface (RS). Firstly, a hybrid optimal objective function is established that simultaneously considers the sensitivity-based residual errors of static damage identification equation and the static displacement residual. In the objective function, the static damage identification equation is addressed by the Tikhonov regularization technique. The MA is subsequently employed to conduct an optimal search and pinpoint the location and intensity of damages at the structural element level. To handle the inconformity of the static loading points and the measurement points of displacements, the model reduction and displacement extension techniques are implemented to reconstruct the static damage identification equation. Meanwhile, the static displacement-based RS is constructed to calculate the displacement residual in the hybrid objective function, thereby circumventing the time-consuming finite element calculations and improving computational efficiency. The identification results of the numerical box girder bridge demonstrate that the proposed method outperforms the particle swarm optimization, differential evolution, Jaya and whale optimization algorithms about both convergence rate in optimal searching and identification accuracy. The proposed method enables more accurate damage identification compared to methods solely based on the indicator of the residual of static damage identification equations or displacement residual. The results of identifying damage in the 21 element-truss structure and the static experiments on identifying damage in an aluminum alloy cantilever beam confirm the high efficiency of the proposed approach.

Research on damage identification of jacket structure under wind and wave loads based on variational mode decomposition and chirplet transform

Research on damage identification of jacket structure under wind and wave loads based on variational mode decomposition and chirplet transform

A novel damage identification algorithm by combining the boundary element method and a series connection neural network

A novel damage identification approach based on a model-driven and a data-driven combined algorithm is developed. By using this approach with only boundary strains, the existence, location, classification as well as the extent of the damage in a plate can be predicted at once with high accuracy and efficiency. To accumulate the data, the boundary element method (BEM) is applied as a model-driven in modeling plates with one or multiple damages in the forms of circular or elliptical holes or cracks and for solving the boundary strains of the defective plates. The dimensionality reduction and semi-analytical characteristics of BEM not only can compress the feature data also can improve the accuracy of the database for the data-driven algorithm. The boundary strains are obtained directly from BEM models, which are also easily collected through the use of strain gauges mounted on the surfaces of structures being monitored in real applications. A series connection neural network algorithm is established to accomplish the novel damage identification assignments with deep learning. The number of the existing damages is firstly detected by a classification neural network model, then the extracted features are transmitted to the corresponding regression neural network model to prognosis the location, classification as well as the extent of each flaw. A high accuracy of about 99.86 % is achieved by the present combined neural network algorithm, which is promising in applications of actual structural health monitoring.

Experimental Investigation and Damage Identification of High-Pile Wharf Framed Bents under Horizontal Impact Loads.

This study investigates damage characteristics, dynamic structural performance changes, and quantitative damage assessment of high-pile wharf framed bents exposed to horizontal impact loads. Through extensive testing of wharf framed bents under such loads, a damage identification approach based on stiffness, natural vibration period, and acceleration data derived from experiments is presented. The findings reveal that under horizontal impact loads, framed bents initially exhibit tensile damage and leaning piles, followed by short straight piles. Additionally, structural damage results in a reduced self-oscillation frequency and an increased amplitude decay rate. Both stiffness-based and cycle-based damage indicators effectively track the cumulative damage progression of the structure. However, the cycle-based damage indicators demonstrate superior stability and accuracy, while acceleration-based indicators precisely identify the moment of damage mutation. This research contributes to enhancing local components, implementing damage identification methods, and advancing health monitoring practices in high-pile wharf projects, aligning with the standards of scientific publications in the field.

A small sample piezoelectric impedance-based structural damage identification using Signal Reshaping-based Enhance Attention Transformer

The electromechanical impedance-based (EMI) method is a non-destructive damage identification approach that can effectively reveal the health condition of a structure. Conventional deep learning-based EMI methods require massive amounts of data. However, the number of structural EMI samples that can be obtained in applications is limited. In this research, a new Signal Reshaping-based Enhanced Attention Transformer network is proposed for small sample structural health monitoring. The proposed network constructs a Signal Reshaping-based Enhance Attention module in the Transformer-based network, which can effectively enhance the overall feature extraction by reshaping each of the input signals. Optimal parameters of the Signal Reshaping-based Enhance Attention module were analyzed. The experiment shows that the proposed network can detect the state and location of bolts and mass variation in a cantilever beam with an overall accuracy of 98.61 % with small samples, which is 23 % better than a conventional Transformer. In addition, the performance of the proposed network was compared to one-dimensional convolutional neural network (1DCNN), very deep convolutional network (VGG), recurrent neural network (RNN), and long short-term memory (LSTM). Besides, the advantage of the proposed system is evaluated in a case study of glass plate damage identification.

An Enhanced Structural Damage Identification Approach Using Statistical Analysis and Optimization

An Enhanced Structural Damage Identification Approach Using Statistical Analysis and Optimization

Probabilistic damage identification of FGM structures using model updating procedure based on expansion of incomplete FRF data

The spatial incompleteness and uncertainties of measured data are unavoidable factors that could significantly affect the reliability of damage detection results. It is therefore essential to utilize a method that can effectively deal with the spatial incomplete and contaminated data, and presents probabilistic damage identification results instead of deterministic results. In this regard, a probabilistic damage identification approach for functionally graded materials (FGM) structures using model updating procedure based on expansion of incomplete frequency response function (FRF) data with measurement noise is presented. The model updating procedure is formulated as an optimization scheme which is accomplished by minimizing a cost function based on the changes in expanded FRF data. To expand the incompletely measured FRFs, an iterative order reduction method is performed, which makes the identification resistant to the adverse effects of measurement noise. For the minimization process, we adopt a novel meta-heuristic algorithm called bald eagle search algorithm (BES), which has not yet been tested in the field of model updating. Based on the statistical distributions of the identified stiffness parameters in the damaged and undamaged states, the probability of damage existence (PDE) is established to describe the damage probability for each element. The performance of the proposed model updating procedure is verified using three FGM structures: a simple beam, a two-span beam and a cantilever plate. The statistical results indicate that under a high level of noise (15%), the proposed procedure can provide the prediction of damage localization with a high level of confidence and yield damage estimation results with acceptable errors.

Damage Identification of Railway Bridges through Temporal Autoregressive Modeling.

The damage identification of railway bridges poses a formidable challenge given the large variability in the environmental and operational conditions that such structures are subjected to along their lifespan. To address this challenge, this paper proposes a novel damage identification approach exploiting continuously extracted time series of autoregressive (AR) coefficients from strain data with moving train loads as highly sensitive damage features. Through a statistical pattern recognition algorithm involving data clustering and quality control charts, the proposed approach offers a set of sensor-level damage indicators with damage detection, quantification, and localization capabilities. The effectiveness of the developed approach is appraised through two case studies, involving a theoretical simply supported beam and a real-world in-operation railway bridge. The latter corresponds to the Mascarat Viaduct, a 20th century historical steel truss railway bridge that remains active in TRAM line 9 in the province of Alicante, Spain. A detailed 3D finite element model (FEM) of the viaduct was defined and experimentally validated. On this basis, an extensive synthetic dataset was constructed accounting for both environmental and operational conditions, as well as a variety of damage scenarios of increasing severity. Overall, the presented results and discussion evidence the superior performance of strain measurements over acceleration, offering great potential for unsupervised damage detection with full damage identification capabilities (detection, quantification, and localization).

Hybrid modal-machine learning damage identification approach for beam-like structures

Data-driven damage detection methods are widely researched and implemented due to the availability of advanced sensing and cloud technologies, where machine learning models are used to process the various data collected from the sensors for damage diagnosis. Supervised methods have shown to be accurate in identifying damages given that they are trained with sufficient historical damage samples. As damage events are rare, supervised methods often face the cold-start problem, that is, insufficient labelled damage samples to initiate model training. Unsupervised methods do not require training and have shown to be practical up to detecting damage presence, but prior knowledge or labelled historical damage samples are still required as references to further locate and quantify the damage. To solve the cold-start issue, a modal-based method, which relates the modal parameters with damages, is proposed to bridge the gap between the unsupervised and supervised methods. Therefore, this work proposes an integration strategy of a modal-based method into a hybrid machine learning-based method for damage detection and localisation of beam-like structures. A two-stage scheme with unsupervised, supervised, and modal-based methods is proposed. The first stage detects damage presence using PCA-FRF as the damage-sensitive feature in an unsupervised manner, and the second stage locates the damages using the first mode shape. Mode shape assessment-based damage localisation is performed at the second stage when no trained model is available due to zero or limited damage samples for training to solve the cold-start and manual labelling issue. Supervised model training starts once there are at least two classes of damage, and the trained model is only used for damage localisation when it surpasses the 90% validation accuracy benchmark. Results showed accuracies of 100%, 100%, and 95.85% for the first stage unsupervised detection, second stage mode shape assessment-based damage localisation and the supervised model-based damage localisation, respectively.

A Novel Improved Dragonfly Algorithm-Based Structural Damage Identification Approach Using Flexibility Assurance Criterion and Trace Lasso

Aiming to enhance the accuracy, stability, and noise robustness of swarm intelligence-based algorithms for structural damage identification (SDI), a novel improved dragonfly algorithm (IDA) is proposed. The IDA integrates the dragonfly algorithm (DA) with three key strategies including enhanced Lévy flight, optimal solution bidirectional search, and greedy preservation. These strategies are introduced to enhance the exploration capability of the original DA and improve the IDA’s capacity to obtain global optima. An objective function is defined using frequency change ratio and flexibility assurance criterion (FAC). Additionally, trace sparse regularization is also incorporated into the objective function since most of the damages in structures tend to be sparsely distributed, so that a sparse result is ensured to improve SDI accuracy. To evaluate the performance of the proposed algorithm, a comparison of the original DA and IDA is conducted using four benchmark functions. The results demonstrate that the proposed algorithm achieves improved convergent rate and accuracy. Furthermore, numerical simulations are performed on a 10-element simply-supported beam and a 31-element planar truss to validate the effectiveness and efficiency of the proposed algorithm in SDI. Significantly, the utilization of IDA instead of DA leads to a substantial reduction in the average calculated relative error for the truly damaged element within the considered damage cases of the simply-supported beam, decreasing from 13.05% to 6.15%. Moreover, an experimental simply-supported beam structure with several assumed damage cases is fabricated in the laboratory. The experimental results further confirm the robustness and capability of the proposed method in real-world SDI applications.

Damage identification of Chi River bridge based on vehicle excitation and WPEVCR

In order to identify damage of Chi River bridge’s superstructure, a damage identification indicator is implemented in the field test, which involves the wavelet packet energy analysis with the feature of dynamic response signals caused by vehicle excitation. On the basis of the field test, a series of numeric models with varied service conditions were developed. The wavelet packet analysis method was utilized to decompose the bridge’s acceleration signals at both healthy and damaged status, and the values of Wavelet Packet Energy Variance Change Rate (WPEVCR) were obtained. Then, according to the acceleration signal data measured from the field test, the damage assessment of the condition of Chi River bridge was performed by means of the obtained WPEVCR. The results demonstrate the capability of WPEVCR in localizing and quantifying the bridge damage status. Moreover, another damage indicator based on the Hilbert-Huang Transform (HHT) has been also employed to verify the assessment of WPEVCR, and both damage identification approaches indicate that the Chi River bridge is in a healthy service condition.

Integration of measured data for the seismic fragility assessment of deteriorated bridges

AbstractThe reliable assessment of bridge capacity is necessary in order to ensure the functionality of the roadway network under normal, or emergency operating conditions. Damage due to the interaction with the environment (e.g., corrosion of coastal reinforcement) has been observed in many cases, even in newly built interurban road networks. Therefore, the need for obtaining and including measured data in fragility assessment analysis has emerged. In line with the above, the proposed research introduces a framework for the assessment of bridges considering integrating data obtained with NDTs. The scope of NDTs is to track down the evolution damage on bridges and determine its risk of failure. The work presents a structural damage identification approach that successfully updates a digital twin of the structure. Subsequently the digital twin is used in order to determine the bridge fragility. Fiber modeling is used for creating the digital twin of the structure. Modified uniaxial material laws are applied for the fragility assessment of a deteriorated case study bridge also providing comparative assessment results for the pristine bridge, i.e. without damage.

Exploration of using acoustic vibration technology to non-destructively detect moldy kernels of in-shell hickory nuts (Carya cathayensis Sarg.)

The feasibility of acoustic vibration signals acquired by a self-made device was assessed as a non-destructive technology for identifying moldy kernels of in-shell hickory nuts (MK-IHNs). Twenty-two acoustic features extracted from time-domain and frequency-domain signals were used to establish a precious model for classifying MK-IHNs and healthy kernels of in-shell hickory nuts (HK-IHNs). Based on a combination of time-domain and frequency-domain features, an optimized LIB support vector machine (Lib-SVM) model was obtained with accuracy of 91.67% for identifying MK-IHNs. Some physical parameters of hickory nuts such as weight and bulk density were significantly correlated with most of the acoustic features (P < 0.05). Moreover, the microstructures of HK-IHN and MK-IHN shells, whatever outside surfaces or cross-sections, were obviously different. It follows then that the physical forms and structures of hickory nuts would play important roles in building a reliable model for identifying MK-IHNs based on acoustic vibration technology. Results of satisfactory proved that the acoustic vibration technology combined with Lib-SVM method could serve as a feasible approach for non-destructive damage identification of MK-IHNs.

A novel multistage damage detection method for trusses using time-history data based on model order reduction and deep neural network

This article first proposes a multistage damage identification approach for trusses using time-series data relied upon model order reduction (MOR) and deep neural network (DNN). In the first step, an acceleration–displacement-based strain energy indicator (ADSEI), which is computed from the acceleration and displacement data incompletely measured at limited sensors and unmeasured ones inferred from a second-order Neumann series expansion (SNSE)-relied MOR technique, is utilized to eliminate low-risk damage candidates, aiming to only keep high-potential ones. This can dramatically reduce the number of output neurons of the DNN model in the second step. The input data employed to build such a DNN are the finite element method (FEM)-simulated acceleration and displacement signals corresponding to measured degrees of freedom (DOFs). Low-risk flawed candidates predicted by this DNN are then excluded via a suggested damage threshold. By repeating such a manner, the accuracy of the DNN models constructed in the subsequent stages is therefore enhanced continuously, although these upgraded DNNs only require a moderate dataset, simple architectures, and much less computational cost for their training and testing processes. Accordingly, both the location and severity of damaged members can be reliably and precisely diagnosed by time-series data measured in a fairly short interval at a few sensors, even with high noises. Several numerical examples of 3D trusses are tested to affirm the efficiency and feasibility of the current approach.

A novel multi-level data fusion and anomaly detection approach for infrastructure damage identification and localisation

In this paper, a new damage identification and localisation framework utilising multi-level data fusion and anomaly detection techniques is introduced. The performance of the framework is presented via case studies of a simply supported bridge and a continuous bridge, whose data were numerically generated through a surrogate model. Accelerations, deflections and bending moments obtained at multiple sensor locations when the bridge was subjected to a moving vehicle were used as the input. A damage-sensitive feature was established via coupling principal component analysis and Mahalanobis distance, allowing for initial data dimensionality reduction and information integration. Anomaly detection using a deep convolutional autoencoder was performed to identify the presence of single and multiple damages on the bridge. It is demonstrated that the proposed approach is independent of the mass and speed of moving vehicles, and is applicable to different types of bridges (i.e. simply supported bridge and continuous bridge) with only minor modifications to the data augmentation/preparation processes. The accuracy of damage identification for the simply supported bridge is shown to be consistently greater than 93%, even for extremely small damage severity (e.g. 1% reduction in local stiffness), and multiple damages. While the damage identification accuracy for the continuous bridge is greater than 94.9% for the majority of the tested cases. The proposed damage localisation approach can accurately determine damage location(s) for both single and multiple damage cases. It is shown how the proposed framework can be adopted for early damage detection to enable reliable decision-making and maximise structural safety.

Unsupervised Domain Adaptation Damage Identification Approach of High Arch Dams after Earthquakes

In actual concrete arch dam engineering scenarios, the dynamic data obtained by the health monitoring system of an arch dam are incomplete. The data acquired typically depend on the state of the dam structure, that is, whether it is intact or incomplete. Besides, the future environmental loads of the structure are unpredictable. Thus, environmental noise is also uncertain. In practical engineering, the use of a damage identification model constructed based on incomplete information is problematic in scenarios with variable loads. Consequently, detecting the water level in actual arch dam projects after an earthquake and determining the impact of environmental uncertainty are necessary. Accordingly, this paper proposes a denoising contractive sparse deep autoencoder (DCS-DAE) model based on domain adaptation. The core idea of the proposed method is to constrain the data probability distribution of feature spaces in the source and target domains using maximum mean discrepancy. This fusion enables the DCS-DAE model to be capable of feature extraction. Moreover, it resolves the problem in which the objective function cannot be applied to other similar scenarios because of the lack of consistency constraints of feature spaces in the source and target domains. Four working conditions are designed to reproduce the uncertainty of structural modeling and the variability of water levels. The conditions are based on the postseismic water level detection requisites of dams in practical engineering. The results show that the proposed anomaly detection model enhances the generalization performance of the DCS-DAE in terms of feature design. Hence, the constructed model can “infer other things from one fact.” The results of this study are meaningful for the real-time cross-domain monitoring of structures under variable load conditions, providing a driving force to apply similar methods to practical arch dam projects.