Damage Identification Problem Research Articles

Structural damage identification with output-only strain measurements and swarm intelligence algorithms: a comparative study

The identification of structural damage with the unavailability of input excitations is highly desired but challenging since structural dynamic responses are affected by the coupling effect of structural parameters and external excitations. To deal with this issue, in this paper, an output-only damage identification strategy based on swarm intelligence algorithms and correlation functions of strain responses is proposed to identify structures subjected to single or multiple unknown white noise excitations. In the proposed strategy, four different population-based optimization algorithms—particle swarm optimization, the butterfly optimization algorithm, the tree seed algorithm, and a micro search Jaya (MS-Jaya)—are employed and compared. The micro search mechanism is integrated into a basic Jaya algorithm to improve its computational efficiency and accuracy by eliminating some damage variables with small values for the identified best solution after several iterations. The objective function is established based on the proposed auto/cross-correlation function of strain responses and a penalty function. The effectiveness of the proposed method is verified with numerical studies on a simply supported beam structure and a steel grid benchmark structure under ambient excitation. In addition, the effect of the reference point, number of sensors, and arrangement of strain gauges on the performance of the proposed method are discussed in detail. The investigated results demonstrate that the proposed approach can accurately detect, locate, and quantify structural damage with limited sensors and 20% noise-polluted strain responses. In particular, the proposed MS-Jaya algorithm presents a more superior capacity in solving the optimization-based damage identification problem than the other three algorithms.

Structural damage identification through unscented Kalman inversion with l 1-norm regularization

Advances in modal analysis have motivated finite element model updating for damage identification based on vibration monitoring. However, the model updating problem is usually ill-posed owing to limited and noisy measurement data, and the predominant sensitivity-based optimization approaches may suffer from large linearization errors. To address these challenges, this article proposes using the unscented Kalman inversion with l 1 -norm regularization (UKI- l 1 ) to solve the damage identification problem in a derivative-free manner. UKI- l 1 implements an explicit transformation converting the l 1 -norm regularized least-squares problem into the l 2 -norm regularized problem, which can be readily solved by the UKI with measurement augmentation. The effectiveness of the proposed approach is evaluated through identifying damage of a continuous beam structure and a reinforced concrete frame structure. Numerical investigation confirms the robustness of UKI- l 1 to measurement noise, and experimental study demonstrates that the damage identification results obtained by UKI- l 1 match well with expectations.

Vibration characterization of rolling bearings with compound fault features under multiple interference factors.

As a key component of rotating machinery power transmission system, rolling bearings in gas turbines are often required to serve in complex working conditions such as the high speed, the heavy load, the variable load, the variable rotational speed, and so on. The signals of bearing failures are easily drowned out by strong background noise and disturbances of related components. In the mechanical transmission system, the signals of bearing failures are easily submerged by the strong background noise and the disturbance of related components, especially for the composite bearing failures, which seriously hinders the effective identification of the vibration characteristics of the bearing operating state and increases the difficulty of fault diagnosis. In order to investigate the impact of interference on the bearing, through the establishment of rolling bearing composite fault vibration model, analyze the relationship between the vibration signals caused by different types of bearing failures and the corresponding vibration characteristics, to reveal the transmission system of the parts of the perturbation of the main multi-interference factors on the bearing fault signal influence law. The experimental verification shows that disturbance yp(t) caused by the sum of gear meshing frequency, and installation errors of the shaft, and coupling in the transmission system and background noise ni(t), which makes the fault frequency relatively weak and difficult to observe, and makes it difficult to accurately separate the fault information of the bearing. It provides a theoretical basis to solve the problem of damage identification and fault diagnosis of rolling bearings under multi-interference state.

Efficient structural damage detection via the lω regularization and randomized extended Kaczmarz algorithm

Structural damage detection (SDD) is an important aspect of structural health monitoring. This study aimed to explore a new method IT ω-REK θ(ω = 1, 1/2; θ = 1,2,3) for SDD based on the lω sparse regularization model and the randomized extended Kaczmarz (REK) type algorithms. When ω = 1/2, the l1/2 sparse regularization model was applied to enhance the ill-posedness of the damage identification problem and ensure the sparsity of the solution. The REK, θ = 1, partially randomized extended Kaczmarz (θ = 2), and fast maximum-distance extended Kaczmarz (θ = 3) algorithms with different threshold operators were used to solve the lω regularization model. These algorithms could obtain optimal identification results and significantly improve computational efficiency by randomly and partially selecting the data of the sensitivity equations. Numerical and experimental studies on different structures showed that the proposed method could fast locate structural damage and accurately identify the damage extents, which was robust to the SDD problem with noise.

Damage assessment in plate structures using an enhanced Jaya algorithm

The article proposes a new method called enhanced Jaya (EJaya) to identify and evaluate damage in plate structures. The method uses a nonlinear optimization task to solve the inverse damage identification problem. The damage magnitudes of the plate elements are taken as design variables, and the objective function to be minimized uses the variations in the plate flexibility matrix and includes a penalization term to avoid false alarm detection. EJaya is developed by incorporating three modifications in the traditional Jaya algorithm: generating an initial population of candidate solutions using prior knowledge of the damage scenario, integrating a component that takes into account the individual distribution through the search space, and implementing a new local search technique. The simulation results show that EJaya is efficient in both solution quality and computational effort and can detect and evaluate damage even in large-sized problems and under noisy data conditions.

A novel graph structure data-driven crack damage identification for compressor blade based on vibro-acoustic signal

The evolution of advanced sensing techniques and intelligent algorithms has significantly underpinned the growth of structural health monitoring and damage identification. Modern industry equipment like compressors, which are indispensable to the petrochemical and other process industries, usually operate under complex conditions including variable speed. The more vulnerable compressor components, such as the blades, are prone to diverse levels of damage over time. Existing research usually discusses the damage identification problem of blades under the Euclidean space, facing challenges in linking multi-source heterogeneous signals. This study introduces a novel approach, employing a graph-structured data-based method for identifying compressor blade cracks. It specifically focuses on variable rotating speed conditions, subsequently proposing an intelligent identification framework based on vibro-acoustic graph-structured data. Firstly, the affinity graphs made of vibro-acoustic damage signal are constructed to express the latent damage information beyond Euclidean space after Fourier transform and residual learning-based feature extraction for one-dimensional data. Then the developed multi-order graph convolutional network and domain discriminator layers are used to extract the domain-invariant damage features, which will be fed into the linear layer for class prediction. Finally, the method's efficacy is cross-verified through experiments with actual measurements on a compressor platform, specifically focusing on variable rotating speed cases.

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Response Surface Model of the Reduced Flexibility Matrix for Bayesian Damage Identification.

The present work is concerned with the use of a Response Surface Model of the reduced flexibility matrix for structural damage identification. A Response Surface Model (RSM) is fitted with the aim at providing a polynomial relationship between nodal cohesion parameters, used to describe the damage field within the structure, and elements of the reduced flexibility matrix. A design of experiment built on combinations of a relatively small number of nodal cohesion parameters is used to fit the RSM. The damage identification problem is formulated within the Bayesian framework and the Delayed Rejection Adaptive Metropolis method is used to sample the posterior probability density function of the uncertain cohesion parameters. Numerical simulations addressing damage identification in plates were carried out in order to assess the proposed approach, which succeeded in the identification of the different damage profiles considered. Besides, the use of a RSM, instead of a FEM of the structure, resulted in reductions of up almost 78% in the required computational cost.

A damage-informed neural network framework for structural damage identification

In this work, an effective Damage-Informed Neural Network (DINN) is first developed to pinpoint the position and extent of structural damage. Instead of resolving the damage identification problem by conventional numerical methods, a Deep Neural Network (DNN) is employed to minimize the loss function which is designed by combining multiple damage location assurance criterion and flexibility matrices to guide the training process. In our computational framework, the parameters of the network, which include both weights and biases, are treated as new design variables instead of damage ratios. Therein, the training data consists only of a set of spatial coordinates of elements, whilst corresponding the damaged ratios of elements unknown to the network are factored into the output. To achieve the goal, the loss value is calculated relying on the predicted damage ratios with supporting Finite Element Analysis (FEA). Additionally, Bayesian Optimization (BO) algorithm is used to automatically tune hyperparameters of the network for enhancing reliability in damage identification. Several numerical examples for damage localization of truss and frame structures are investigated to evaluate the effectiveness and reliability of the suggested methodology. The obtained results point out that our model not only correctly locates the actual damage sites but also requires the least number of structural analyses and faster convergence rate compared with other algorithms.

Deep learning based structural damage identification for the strain field of a subway bolster

Strain-based structural health monitoring technology has been widely used in the field of transportation. The existing strain damage identification methods have defects such as complex process, lag in state evaluation, and low intelligence. This paper adopts the deep learning method to establish a network model that uses the strain field information to map directly to the damage information, and takes a subway bolster as the engineering background to realise the end-to-end automatic damage identification. Firstly, the problem of damage identification in strain field is described, combined with the idea of fully convolutional network. The basic structure of damage identification network is modularised, and the overall design framework is proposed. Then, the damage simulation method is determined, and the feasibility of using this method to construct a strain field damage dataset is verified. The batch random damage model generation and the random noise signal addition program are coded, and the datasets of the bolster under static/dynamic force are obtained. Finally, the deep learning model is applied to the bolster damage dataset, and a residual module BolRes_Att that integrates spatial attention and channel attention mechanism is proposed. It has better damage identification performance without an increase in model parameters. The average number of faulty elements on the two bolster test sets of the improved damage identification network is 3.02 and 2.92, respectively, accounting for about 0.016% of all elements. The average processing time for a set of data is only 0.014 s. The results show that the deep learning model constructed in this paper can accurately and quickly identify the damage information of elements according to the strain field information.

A wavelet based data coupling method for spatial damage detection in beam-type structures.

Spatial damage identification is of great significance in mechanical, aerospace, and civil engineering. In this study, a data coupling method based on continuous wavelet transform (CWT) is proposed to identify the spatial damage location of beam-type structures. The singularity of the wavelet coefficient can be used to identify the signal singularity, and data coupling method calculates the spatial location of the damage. Numerical simulations and experimental analyses of different type of beams with transfixion damage are carried out to evaluate the accuracy of the method. The results show that the wavelet based data coupling method (W-DCM) can identify the minimum 4.9% damage severity of fixed beam and continuous beam, and can also identify the damage of non-free end of cantilever beam. However, the 9.7% damage severity of the free end of the cantilever beam cannot be identified. It is also found that the W-DCM can effectively circumvent the problem of wavelet coefficients edge effect. This method and wavelet singularity are used to provide a solution to the problem of structural edge damage identification.

A conveyor belt characterization information extraction method based on YOLOv5 and the skeleton method

To resolve the inaccurate localization of conveyor belt surface damage identification problem and to address the insufficiencies of the methods for extracting surface characterization information, this paper proposes a conveyor belt characterization information extraction method that integrates YOLOv5 deep learning and the skeleton method. By constructing a conveyor belt surface damage recognition model based on the YOLOv5 target detection algorithm, the identification, localization and cropping of the conveyor belt’s surface damage are implemented. After that, edge extraction and surface information extraction are also performed on the damaged parts. Finally, the collected data are analyzed and processed in real time by edge computing equipment to determine the degree of damage of the parts. Finally, intelligent operation of the belt conveyor is achieved with autonomous operations, unattended operations and decision alarms. The experimental results show that the recognition accuracy of YOLOv5 is approximately 93.11%, the speed is approximately 57 frames per second and the error of the data acquired by image processing is between 2% and 10%, which meets the real-time detection requirements of conveyor belt surface damage detection, and assists in the safety management supervision of the belt conveyer.

Identification of Nonlinear Behavior of Bridge Structures Using Time Series Analysis of Vibration Signals

ABSTRACT One of the most widely used linear techniques for solving damage identification problems and prediction of the system dynamic behavior is modal testing and analysis. However, most of real structures exhibit nonlinear behavior during their lifetime caused by a number of various reasons. The presence of nonlinearity in a structural system changes its dynamic characteristics; hence, the use of linear techniques is improper for prediction of the system behavior. In this paper, a vibration-based nonlinearity identification technique was proposed to identify early changes in a dynamic system prior to significant damages. This technique combines the time series Autoregressive Moving Average with Exogenous Inputs (ARMAX) modelling, probability theory, and Fuzzy C-Means (FCM) clustering algorithm to categorize the linear and nonlinear dynamic behaviors of a dynamic system. The technique aims to categorize the linear and nonlinear behaviors of a structure, when it is subjected to various levels of excitation source. To show the validity of the proposed method, a series of shake table tests was performed on a six-span steel truss bridge model. The bridge model was excited using different amplitudes of ground motion to control the nonlinearity degree of rubber-based supports. It can be concluded from the analysis results that the new vibration-based nonlinearity identification technique was able to identify nonlinear behavior of the bridge model once it was subjected to different levels of earthquake excitations. This algorithm was able to categorize the linear and nonlinear behaviors of the bridge model and define a specific threshold for different excitation sources.

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation.

Structural nonlinear damage identification based on the information distance of GNPAX/GARCH model and its experimental study

In the structural health monitoring (SHM) of civil engineering, most of the structural damage is nonlinear damage, such as breathing cracks and bolt looseness. Under the excitation of external loads, the time-domain response data of the structure produced by these nonlinear damages have nonlinear features. In order to solve the time-domain nonlinear damage identification problem of complex structures, this paper proposes a nonlinear damage identification method based on the information distance of GNPAX/GARCH (general expression of system identification for linear and nonlinear with polynomial approximation and exogenous inputs/generalized autoregressive conditional heteroskedasticity) model. First, an order determination method based on Bayesian optimization to select the order of the GNPAX/GARCH model was proposed, and the GNPAX/GARCH model was established for damage identification. Then, the redundant structural items of GNPAX/GARCH model were removed by the model optimization method based on the structural pruning algorithm. Finally, the information distance of the GNPAX/GARCH model conditional heteroscedasticity series between the baseline state and test state was derived, and the structural damage source locations were determined according to the information distance. A three-story frame structure experiment and a stand structure experiment were used to verify the effectiveness of the proposed method. The results show that the proposed method can effectively identify the nonlinear damages caused by the component breathing crack and joint bolt looseness, verifying its robustness to the nonlinear damage identification of the multi-story and multi-span complex structures.

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.

Statistical topology optimization scheme for structural damage identification

Statistical topology optimization (TO) scheme, which statistically records and analyzes local optima at random frequencies of the excitation force, is developed for material damage and open crack. To identify the alternations of stiffness, mass, and strength due to the damages, the structural responses of healthy and suspicious systems can be compared and optimized by TO for fault identification. Owing to the local optima problem in TO, slight modifications in responses caused by relatively minimal damage, and inadequate comparison of insufficient vibration information, the application of TO to a damage identification problem is often complicated. To overcome this complication, this study establishes a new approach that records local optima (layouts) and performs a statistical process (clustering and averaging of layouts). To illustrate the concept of the present scheme, several examples with material damage and open crack are solved.

Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts

Bridges and viaducts are critical components of railway transport infrastructures, providing safe and efficient means for trains to cross over natural barriers such as rivers and valleys. Ensuring the continuous safe operation of these structures is therefore essential to avoid disastrous economic consequences and even human losses. Drive-by methodologies have emerged as a potential and cost-effective monitoring solution for accurately and prematurely detecting damage based on instrumented vehicles while minimizing disruptions to train operations. This paper presents a critical review of drive-by methodologies applied to bridges and viaducts. Firstly, the premises of the method are briefly reviewed, and the potential applications are discussed. In sequence, several works involving the use of drive-by methodologies for modal characteristic extraction are presented, encompassing the most important methodologies developed over time as well as recent advancements in the field. Finally, the problem of damage identification is discussed—both in relation to modal and non-modal parameter-based techniques considering the most promising features and the current advancements in the development of methodologies for damage detection based on machine learning algorithms. A comprehensive conclusion is presented at the end of the article, summarizing the achievements and providing perspectives for future developments. By critically assessing the application of drive-by methodologies to bridges and viaducts, this paper contributes to the advancement of knowledge in this crucial area, emphasizing the significance of continuous monitoring for ensuring the integrity and safety of these vital transport infrastructures.

Spatial Damage Identification of 3D Structures based on Wavelet-Sparrow Search Algorithm

Based on the singularity principle of the wavelet transform, the continuous wavelet transform is applied to the strain mode of the damaged structure, and the data is normalised and coupled to each 3D position in space. The modal maxima of the wavelet coefficients of each layer of data are compared to identify the spatial location of the damage to the complex structure, and then the Sparrow Search Algorithm (SSA) is used to quickly and accurately identify the data and obtain the degree of damage to the structure. In this paper, the proposed method is validated by numerical finite element simulations and experiments on cantilever structures. The results of the study show that the method can be used to solve the 3D spatial damage identification problem in a simple and fast way, which can be used as a reference for structural inspection problems in practical engineering.

Performance evaluation of the artificial hummingbird algorithm in the problem of structural damage identification

Recently, Structural Health Monitoring (SHM) has become a critical component of the maintenance and safety of lifeline infrastructures such as dams, skyscrapers, and bridges, thanks to its ability to detect structural failures at the early stages. In this paper, we evaluate the performance of the SHM damage identification tool using a novel metaheuristic algorithm called the Artificial Hummingbird Algorithm (AHA). The proposed approach is evaluated by two case studies of different bridge structures in Vietnam with different simulated damage scenarios. The potency of the AHA is compared against the other well-known metaheuristic algorithms such as Cuckoo Search (CS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching-Learning Based Optimization (TLBO). The results show that the AHA performs much better than the other algorithms in terms of accuracy and computational cost. The application of AHA can help to reduce the cost and time required for structural maintenance significantly, as well as improve the lifecycle of the structure.