Accuracy Of Damage Identification Research Articles

An ultrafast and robust structural damage identification framework enabled by an optimized extreme learning machine

Artificial intelligence (AI) has recently been implemented in structural health monitoring (SHM) systems for damage detection and identification. However, existing AI methods often involve a high number of parameters, resulting in the laborious workload during model training and implementation. In this study, we develop a lightweight damage identification framework embedded with an optimized extreme learning machine (ELM) using a significantly reduced number of parameters from frequency features of the structural vibrational signals. Coupled with the ensemble empirical mode decomposition (EEMD), the frequency features were abstracted from the structural vibrational signals in the data pre-processing step. Then, a metaheuristic algorithm (chaos game optimization, CGO) was chosen to optimize the model weight of the ELM to guarantee damage identification accuracy. Once the model is trained, the structural damages can be precisely identified despite the noise-contaminated data. We validated the efficiency and accuracy of the proposed framework with a public database and compare its performance with several common metaheuristic algorithms. With the proven capability, the proposed framework shows a promising future as a handy and reliable AI-assisted digital tool for robust development in next-generation structural monitoring systems.

Unsupervised structural damage identification based on covariance matrix and deep clustering

SummaryStructural damage identification is a major task in structural health monitoring. Machine learning and deep learning algorithms have been widely applied in the research of structural damage identification. Supervised algorithms require expert labeling, making it difficult to implement in engineering applications. Unsupervised structural damage identification algorithms are generally divided into two parts: damage‐sensitive factor extraction and damage determination. Existing algorithms all perform these two steps separately. This paper proposes a damage identification method combining covariance matrix and improved deep embedding clustering network (IDEC). IDEC can perform damage‐sensitive factor extraction and damage determination operations at the same time. The covariance matrix that introduces delay information contains rich damage features, and the combination of the two has been proven to effectively mine the damage‐sensitive feature space. After network hyperparameter optimization via Bayesian optimization, the proposed method is applied to the damage identification and quantification using real bridge acceleration response data under vehicle load. The results show that this method can identify structural damage with an accuracy of up to 97% with better performance than existing technologies, and it also has great performance in identifying small damages. The proposed method is expected to increase the damage identification accuracy if applied in engineering practice.

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.

A Probabilistic Structural Damage Identification Method with a Generic Non-Convex Penalty

Due to the advantage that the non-convex penalty accurately characterizes the sparsity of structural damage, various models based on non-convex penalties have been effectively utilized to the field of structural damage identification. However, these models generally ignore the influence of the uncertainty on the damage identification, which inevitably reduces the accuracy of damage identification. To improve the damage identification accuracy, a probabilistic structural damage identification method with a generic non-convex penalty is proposed, where the uncertainty corresponding to each mode is quantified using the separate Gaussian distribution. The proposed model is estimated via the iteratively reweighted least squares optimization algorithm according to the maximum likelihood principle. The numerical and experimental results illustrate that the proposed method improves the damage identification accuracy by 3.98% and 7.25% compared to the original model, respectively.

Damage identification of truss bridges based on feature transferable digital twins

In this study, a novel damage identification method based on feature transferable digital twins is proposed to improve the damage identification accuracy of truss bridges. Specifically, a finite element model is employed as a benchmark digital model to obtain simulated data for multiple types of truss damage scenarios. The simulated data from the model and the measured signals from the truss bridge are fed into the deep residual sub-domain adaptation network for feature alignment to identify truss bridge damage. The effectiveness of the proposed method is verified by a truss experiment, where the classification accuracies of the training and validation datasets reach more than 80 % and 77 %, respectively. The classification accuracy of the method is maximally improved by 54.51 % compared to conventional identification approaches. The results show that the method has the potential to extract and transfer knowledge of damage patterns from finite element models to identify truss bridge damage.

Damage detection and location using a simulated annealing-artificial hummingbird algorithm with an improved objective function

Swarm intelligence algorithms and finite element model update technology are important issues in the field of structural damage detection. However, the complexity of engineering structural models normally leads to low computational efficiency and large detection errors in structural damage detection. To solve these problems, a simulated annealing-artificial hummingbird algorithm (SA-AHA) is proposed based on the artificial hummingbird algorithm (AHA). The Sobol sequence is used to improve the identification efficiency by optimizing the initial population distribution of the AHA. Then, the simulated annealing strategy is introduced to improve the detection accuracy by enhancing the global search ability of the AHA. In addition, a novel objective function is presented by combining modal flexibility residual, natural frequency residual, and trace sparse constraint of the structural model. Numerical simulations of a simply supported beam and a two-story rigid frame are carried out to verify the superiority of the proposed SA-AHA and the objective function. Simulation results demonstrate that the SA-AHA is better than the AHA in terms of damage computational efficiency and damage identification accuracy. Moreover, the new objective function can be more excellently applied to the SA-AHA than the previous one, which can be effectively used to locate and estimate the damage of the proposed SA-AHA in structure. Finally, experimental studies are carried out to verify the proposed method.

A comparative analysis of damage identification methods based on multi-channel data fusion and convolutional neural network for frame structures

Vibration-based damage identification methods are crucial for structural health monitoring. In recent years, deep learning methods, such as convolutional neural networks (CNNs), have been widely used for damage identification due to their superior performance. To enhance the accuracy of damage identification for complex frame structures and effectively utilize data measured from multiple sensors, this study proposes and compares four different damage identification methods based on CNN and four different kinds of multi-channel data fusion approaches, i.e., data splicing, matrix reconstruction, data dimension elevation, and sub-model integration. Firstly, the original one-dimensional vibration signals of the frame structures are measured. Then, multi-channel data fusion is performed on the signals to construct datasets used as input to train the CNN model. Finally, the sensitive damage features are automatically extracted using the trained CNN models after parameter tuning, and the structural damage patterns are identified. The effectiveness of these damage identification methods is evaluated using the numerical model of IASC-ASCE Benchmark structure, the Qatar University Grandstand Simulator test, and a three-layer stainless steel frame structure test. The efficiency of damage identification methods using multi-channel data is compared to those using only single-channel data. Additionally, the impact of noise on damage identification accuracy is also analyzed. The results demonstrate that the proposed four different damage identification methods based on CNN and multi-channel data fusion, outperform the methods that solely rely on single-channel data. Furthermore, these methods significantly enhance the accuracy of damage identification.

A joint fraction function regularization model for the structural damage identification

The sparse regularization (SReg) model has been prove to be an effective tool for the structural damage identification. However, the SReg model overly penalizes the larger components in the damage parameter leading to extra estimation bias, and it ignores the similarity information among different measurements that is useful for improving the performance of damage identification. To further improve the accuracy of damage identification, this study proposes a joint fraction function regularization model by jointing multiple fraction function regularization models, where fraction function regularizers are utilized to overcome the excessive penalty drawback, and the similarity of different measurements is employed through a data fusion technique. The numerical and experimental study show that, compared with the previous SReg models, the damage identification errors of the proposed model are reduced by 4.15% and 2.12% on average, respectively.

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.

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.

A robust sparse Bayesian learning method for the structural damage identification by a mixture of Gaussians

Sparse Bayesian learning methods have been successfully applied to the community of structural damage identification, which commonly assumes that the uncertainties from modeling errors and measurement noises follow Gaussian distribution. However, this assumption typically compromises the accuracy of damage identification because these real uncertainties may not follow a Gaussian distribution. A mixture of Gaussian distributions can more accurately quantify the real uncertainties of damage identification than a Gaussian distribution because it can theoretically approximate any continuous distribution. Motivated by this, we utilized a robust sparse Bayesian learning method to investigate the problem of structural damage identification and further improve the accuracy of damage identification. This method includes a sparse Bayesian learning model with a mixture of Gaussians and an expectation–maximization algorithm based on the Laplace approximation technique. The numerical and experimental results illustrate that our method improves the damage identification accuracy by 6.78% and 11.92% than the existing two sparse Bayesian learning ones, respectively.

The Wrinkles Characterization in GFRP Composites by Infrared Active Thermography.

An experimental study has been carried out to assess the effectiveness of infrared thermography in wrinkle detection in composite GFRP (Glass Fiber Reinforced Plastic) structures by infrared active thermography. Wrinkles in composite GFRP plates with different weave patterns (twill and satin) have been manufactured with the use of the vacuum bagging method. The different localization of defects in laminates has been taken into account. Transmission and reflection measurement techniques of active thermography have been verified and compared. The section of a turbine blade with a vertical axis of rotation containing post-manufacturing wrinkles has been prepared to verify active thermography measurement techniques in the real structure. In the turbine blade section, the influence of a gelcoat surface on the effectiveness of thermography damage detection has also been taken into account. Straightforward thermal parameters applied in structural health monitoring systems allow an effective damage detection method to be built. The transmission IRT setup allows not only for damage detection and localization in composite structures but also for accurate damage identification. The reflection IRT setup is convenient for damage detection systems coupled with nondestructive testing software. In considered cases, the type of fabric weave has negligible influence on the quality of damage detection results.

Structural damage identification of subseabed shield tunnels based on distributed fiber optic sensors and information fusion

Subseabed shield tunnels are constantly subjected to erosion induced by seepage and corrosive ions and electrochemical effects of diffuse currents from subway traction locomotives during service, resulting in damage to the outer wall of the segment. However, the outer wall cannot be directly observed, making damage identification a challenge. In this research, an approach based on distributed fiber optic sensors (DFOSs) and information fusion is proposed to solve this issue. To monitor strain, DFOSs were installed along the circumference of the segment inner wall. The strain curvature was adopted to solve the adverse influence of the spatial resolution (SR) averaging effect in DFOSs. Using the strain and strain curvature as information sources, the strain damage probability index (SDPI) and strain curvature damage probability index (SCDPI) were developed. Based on Dempster-Shafer (D-S) evidence theory, the SDPI and SCDPI were further utilized to build basic probability assignment (BPA) functions and perform information fusion for damage identification. To validate the feasibility of this approach, model experiments were conducted. The analysis of the test data indicated that information fusion can suppress the independent features of the two information sources while emphasizing their common features. The SR averaging effect, which can cause data distortion and diminish or even mask the damage anomaly feature in strain, and multiple extrema in the strain curvature that can interfere with damage localization are simultaneously eliminated. The damage identification accuracy is improved, and the damage localization error for the segment outer wall can reach 0.79 cm.

A fraction function regularization model for the structural damage identification

The conventional model updating based on sensitivity analysis generally employs l1-norm regularizer to characterize the sparsity of the structural damage. However, the l1-norm regularizer inevitably excessively penalizes the larger components in the damage parameter, which certainly causes the extra estimation bias of the damage parameter and reduces the damage identification accuracy. A fraction function regularizer not only well characterizes the sparsity, but also overcomes the excessive penalty drawback of the l1-norm regularizer. Based on this, a fraction function regularization model is proposed to improve the damage identification accuracy. Numerical and experimental results illustrate that the damage identification accuracy of the proposed model is averagely improved 4.96% and 3.68% than that of the l1 regularization one, the iteratively reweighted l1 regularization one and the elastic net one, respectively.

Electromechanical Impedance Temperature Compensation and Bolt Loosening Monitoring Based on Modified Unet and Multitask Learning

Bolts are frequently subjected to loosening due to time varying external loads during service. The electromechanical impedance (EMI) technique based on piezoelectric ceramic wafers (PZT) is sensitive to the initial bolt preload looseness. However, the change in environmental temperature has a great effect on EMI monitoring. Deep convolutional neural network (CNN) is a promising technique for EMI monitoring. Nevertheless, it is difficult to train a deep CNN with limited training data to accurately identify damages under a wide range of temperature variations. To this end, this study proposes a multitask CNN for identifying bolts loosening. The network consists of a temperature compensation subnetwork to compensate for the temperature effect, and a lightweight damage identification subnetwork to identify bolt loosening states. The temperature compensation subnetwork is a modified Unet, and both the impedance and temperature are used as its input. The damage identification subnetwork is connected in series behind the temperature compensation subnetwork. A multiloss function is proposed in which a TV regularizer is used. Experimental results show that the validation accuracy of the multitask network is 97.71% when the network is trained by only about 30 samples from each loosening state. Moreover, the generalization abilities of the proposed multitask model to unexpected temperatures and bolt torques are investigated. The model is interpreted by the integrated gradients method, and is also compared with single-task damage identification CNNs. It is proved that the multitask network trained by limited samples can achieve accurate damage identification in temperature varying environments.

Modal strain energy based enhanced approaches for damage detection and severity estimation

Structural health monitoring (SHM) has been utilized to assess structural deficiency for preventing the invisible failure turning into collapses. Damage localization and severity assessment based on vibration-based characteristics have received considerable attention in civil and engineering fields during recent decades. This study presents a modal strain energy (MSE) based damage detection of a beam-like system. Particularly, based on the performances of existing MSE based damage indices (DIs), a robust and fast technique, called averaging scheme is developed. Observing from the two original DIs that one of them leads to underestimations while overestimations are witnessed using the other one when estimating damage severity, the average of their severity estimations absolutely is supposed to result in better anticipations. Furthermore, another MSE-based updating procedure that was also established based on the original method to reach more accurate predictions is also improved in this study. However, the updating technique considers only the fundamental mode while higher modes are ignored. It is assumed that damage can be caused by higher modes and therefore accounting for more modes possibly lead to better failure identification. Hence, in this study, a numerical investigation is carried out to examine the feasibility of the averaging scheme’s deployment on a cantilever beam. The contribution of higher modes to the performance of the proposed technique as well as the updating procedure is also evaluated. The numerical validation shows that the proposed averaging scheme is outstanding since it results in damage identification results comparable to that of the updating procedure but more promptly. Furthermore, compared to using only the fundamental mode, the cumulative contribution of higher modes, particularly, the lowest four modes, tremendously leads to more accurate damage identification, especially under noisy conditions.

A new hybrid PSO-YUKI for double cracks identification in CFRP cantilever beam

The durability and simplicity of the programming of meta-heuristic algorithms make them important in the optimization field. This paper presents a novel application for double cracks identification in Carbon Fiber Reinforced Polymer (CFRP) cantilever beams based on experimental and numerical analyses using enhanced optimization techniques. A new hybrid algorithm Particle Swarm Optimization and YUKI (PSO-YUKI) is proposed and combined with Radial Basis Functions (RBF) for solving fast inverse problems. The direct problem is based on the results of the dynamic experimental test of CFRP laminate, measuring the dynamics characteristics of a healthy beam, and the variation in the response corresponding to different scenarios of double cracks with different depths. The Finite Element Method (FEM) is used to simulate this vibrational behavior considering double cracks in different locations. The goal is creating an accurate damage identification method that has a high computational performance, based on the idea of building models that combines the vibrational responses issued from the experiments and simulations. The suggested method is tested based on collected data from numerical and experimental modal analyses in the case of undamaged and damaged CFRP laminates to demonstrate its accuracy and efficiency. The provided results show the robustness of PSO-YUKI compared with PSO for double cracks depth identification. The Matlab Code of PSO-YUKI can be found at https://github.com/Samir-Khatir/Hybrid-PSO-YUKI-.git.

Bridge damage identification under the moving vehicle loads based on the method of physics-guided deep neural networks

Modeling uncertainty or modeling error has been widely recognized as one major challenge for damage identification of bridge structures by moving vehicles. This study proposed a physics-guided deep neural network approach to improve the accuracy of damage identification in vehicle-induced bridge vibration response, which aims to minimize the influence of the modeling uncertainty on damage identification. Firstly, the physical relationship between the damage to the bridge with the acceleration signal of the bridge, the physics-guided deep neural network, and its damage diagnosis method is introduced. The feasibility of the damage identification method based on physics-guided deep neural networks is theoretically explained. Secondly, a numerical case of damage identification of the simply supported bridge under moving vehicle loads is conducted to compare the accuracy of damage identification between the proposed method and the traditional method. Finally, a bridge field test is implemented to verify the applicability and effectiveness of the proposed method. The results illustrate that the method can be utilized to accurately identify the damage locations of simply supported bridges under a moving vehicle.

Fully distributed strain-based output feedback for enhanced sensitivity in damage diagnosis of structures

The closed-loop damage diagnosis method has shown significant improvement in recent years, while closed-loop eigenvectors are seldomly used in previous damage diagnosis research due to the lack of adequate measurement points, by which a better damage diagnosis effect can be achieved. With the maturity of fully distributed optical fiber sensing technology, strain data acquisition with many (high-density) measurement points can be realized; in this manner, fully distributed strain-based output feedback for sensitivity enhancement is first investigated in this study, while the main contribution is to first develop a calculation algorithm of closed-loop strain modal shape sensitivity with high-density strain measurements. First, the desired eigenstructure assignment scheme is constructed, and a reasonable range of closed-loop pole selection is determined. On this basis, a sensitivity-based damage diagnosis method using closed-loop strain modal shapes is proposed, with sensitivity and robustness selected as the optimization goals. The optimal gain is obtained, which can benefit the closed-loop damage identification accuracy and stability. The effectiveness of the proposed method is validated and compared with the closed-loop eigenvalue-based method by numerical simulation, and the results indicate that the proposed method can effectively enhance the performance of damage diagnosis and improve system robustness.

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

A huge number of old arch bridges located in rural regions are at the peak of maintenance. The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge, owing to the absence of technical resources and sufficient funds in rural regions. There is an urgent need for an economical, fast, and accurate damage identification solution. The authors proposed a damage identification system of an old arch bridge implemented with a machine learning algorithm, which took the vehicle-induced response as the excitation. A damage index was defined based on wavelet packet theory, and a machine learning sample database collecting the denoised response was constructed. Through comparing three machine learning algorithms: Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (R.F.), the R.F. damage identification model were found to have a better recognition ability. Finally, the Particle Swarm Optimization (PSO) algorithm was used to optimize the number of subtrees and split features of the R.F. model. The PSO optimized R.F. model was capable of the identification of different damage levels of old arch bridges with sensitive damage index. The proposed framework is practical and promising for the old bridge’s structural damage identification in rural regions.