Damage Identification Problem Research Articles

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.

Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data

For the problem of cable damage identification in cable-stayed bridges, we have presented a method for identifying cable damage based on deflection monitoring data from a small number of measurement points. We first describe the method to reduce the number of measurement points. We analyzed the distributional characteristics of the deflection difference before and after cable damage in cable-stayed bridges with optimized measurement points. The first derivative of the deflection difference is transformed by a wavelet transform to identify the location of the damaged cable. Then, the Kriging proxy model with exponential and deflection differences is established. The objective function is constructed from the residual deflection difference formed by the deflection difference and the measured deflection difference. With the particle swarm optimization algorithm, the damage parameters in the surrogate model are modified to minimize the objective function, and the damage to the cables is then identified. It is shown that the location of the damaged cable can be identified from the deflection data of a small number of measurement points with small error. The degree of damage can be accurately determined using the surrogate model.

Substructural damage identification using autoregressive moving average with exogenous inputs model and sparse regularization

Substructuring approaches possess many superiorities over traditional global approaches in damage identification because large-size global structures are replaced by small and manageable substructures. This paper proposes a substructural time series model for locating and quantifying the damage in complex systems. A substructural autoregressive moving average with exogenous inputs (ARMAX) model is established to extract the frequencies and mode shapes of substructures as indicators for damage detection. The detection of structural damage is essentially an inverse problem, and the damage in structure bears sparse properties. The inverse problem of substructural damage identification is efficiently solved via sparse regularization, and structural damage can be located and quantified through the nonzero terms in the solution vector. The accuracy of the proposed method is demonstrated by the numerical simulation of a frame structure and shaking table test of a shear building structure. As the substructural properties are more sensitive to local structural damage than the global properties, the substructural ARMAX model is quite accurate and efficient to be used in the damage identification of a complex system.

Structural Nonlinear Damage Identification Method Based on the Kullback–Leibler Distance of Time Domain Model Residuals

Under external load excitation, damage such as breathing cracks and bolt loosening will cause structural time domain acceleration to have nonlinear features. To solve the problem of time domain nonlinear damage identification, a damage identification method based on the Kullback–Leibler (KL) distance of time domain model residuals is proposed in this paper. First, an autoregressive (AR) model order was selected using the autocorrelation function (ACF) and Akaike information criterion (AIC). Then, an AR model was obtained based on the structural acceleration response time series, and the AR model residual was extracted. Finally, the KL distance was used as a damage indicator to judge the structural damage source location. The effectiveness of the proposed method was verified by using a multi-story, multi-span stand model experiment and a simulated eight-story shear structure. The results show that the proposed structural nonlinear damage identification method can effectively distinguish the structural damage location of multi-degree-of-freedom shear structures and complex stand structures, and it is robust enough to detect environmental noise and small damage.

A hybrid algorithm to solve a nonlinear inverse problem of boundary conditions and damage identification

In this communication we present a method exploiting displacement data measured at accessible parts of solid’s boundary, for the solution of boundary conditions and damage identification problems encountered in mechanics. Targeted applications are made of materials presenting a nonlinear mechanical behavior with damage-based approaches. A hybrid algorithm has been developed in order to identify the missing data by minimizing a gap-functional. It takes into account the incremental mechanical behavior of the material and the damage evolution inside the solid via the concept of piloting. An application on a solid made of mortar with synthetic measurements is presented to illustrate the efficiency and robustness of the algorithm.

Damage identification of structures based on sensitivity equations using PCA and PSD data

This article presents a novel model updating approach to the problem of structural damage identification. The approach is based on principal component analysis (PCA) of power spectral density (PSD) data obtained from incompletely measured structural responses. Damage detection was done by solving an innovative sensitivity equation based on least-squares optimization that could help solve the problem in a time-efficient manner and extend the results for industrial applications. Simulations of a 2D steel truss and a 2D steel frame were considered for a thorough comparison. The simulation results clearly showed the effectiveness of the method and its superior performance for solutions such as a PSD-only strategy and frequency response function (FRF) based data analysis along with singular value decomposition.

Damage identification using noisy frequency response functions based on topology optimization

This paper proposes a robust damage identification method using noisy frequency response functions (FRFs) and topology optimization. We formulate the damage identification problem as an inverse problem of generating the damage topology of the structure from measured dynamic responses of the structure to given external dynamic loading. The method is based on the minimization of the objective function representing errors between measured FRFs of the structure obtained by experimental modal analysis, and those obtained by harmonic response analysis using finite element analysis. In the minimization process, material distribution, or the topology of the structure is varied and the optimal damage topology is identified as regions with no material assigned as a result of the minimization using the solid isotropic material with penalization (SIMP). In order to overcome the problems caused by the ill-posedness of the inverse problem, it is proposed that the least absolute shrinkage and selection operator (Lasso) regularization, or the penalization to the L1 norm of the design variable be applied to the original objective function. By applying Lasso regularization, the method is expected not only to eliminate spurious damaged regions but also to minimize the effect of measurement noises. This paper first presents the mathematical background and its numerical implementation of the proposed methodology. The method is then applied to the identification of a damage of cantilevered plates. The FRFs were experimentally obtained and the proposed method is applied. It is shown that the method successfully identifies the damage.

Solution to the problem of bridge structure damage identification by a response surface method and an imperialist competitive algorithm

To increase the efficiency of structural damage identification (SDI) methods and timeously and accurately detect initial structural damage, this research develops an SDI method based on a response surface method (RSM) and an imperialist competitive algorithm (ICA). At first, a Latin hypercube design method is used for experimental design and selection of sample points based on RSM. Then, a high-order response surface surrogate model for the target frequency response and stiffness reduction factor is established. Finally, analysis of variance is performed to assess the overall goodness-of-fit and prediction accuracy of the established model. Then the results obtained are combined with structural dynamic response data to construct objective functions; furthermore, the optimal solution of parameter vector in the objective function is solved based on the ICA. Then damage positioning and quantification can be achieved according to location and degree of change in each parameter; finally, the RSM-ICA-based SDI method proposed is applied to damage identification of high-dimensional damaged simply-supported beam models. To verify the effectiveness of the proposed method, the damage identification results are compared with the results obtained from traditional optimization algorithms. The results indicate that: average errors in the structural stiffness parameters and natural frequency that are identified by the proposed method are 6.104% and 0.134% respectively. The RSM-ICA-based SDI method can more accurately identify the location and degree of damages with more significantly increased identification efficiency and better precision compared to traditional algorithms. This approach provides a novel means of solving SDI problems.

Structural nonlinear damage identification based on Bayesian optimization GNAR/GARCH model and its experimental study

Cracks and bolt looseness are common damages in a steel structure. Under the time-history loading, nonlinear characteristics of variable stiffness exist in cracks, and bolt looseness has complex nonlinear characteristics of stiffness and damping. To effectively solve the problem of structural nonlinear damage identification, an output-only damage identification method based on the GNAR/GARCH (general expression for linear and nonlinear autoregressive/general autoregressive conditional heteroskedasticity) model with Bayesian optimization is proposed. First, the stationarity test and continuous difference processing was carried out on the measured acceleration data, and an order determination method based on Bayesian optimization was proposed to automatically select the optimal model order. Then, a model structure optimization method based on Bayesian optimization was proposed to select the optimal model structure of a GNAR/GARCH model, and a GNAR/GARCH model was established based on the measured acceleration data for damage identification. Finally, a new damage indicator PIDI (probabilistic Itakura distance indicator) was proposed to judge the structural healthy status. The nonlinear damage experiment of three-story frame and relay tower was used to verify the effectiveness of the proposed method. In addition, in order to illustrate the efficiency of the proposed method for nonlinear damage identification, a comparative study was carried out using other two damage identification methods. The results show that the nonlinear damage identification method based on the GNAR/GARCH model with Bayesian optimization can easily identify the nonlinear damage of the frame and relay tower structure, and it can effectively identify the structure damage with single or multiple nonlinear damage sources.

Output-only structural damage identification using hybrid Jaya and differential evolution algorithm with reference-free correlation functions

To solve the optimization-based structural damage identification problem, a novel hybrid algorithm based on Jaya and differential evolution algorithm (HJDEA) is proposed to detect, locate and quantify structural damages by effectively incorporating the powerful local exploitation capacity of Jaya algorithm and global exploration capability of differential evolution. Meanwhile, Hammersley sequence initialization and Lévy flight search mechanism are introduced into HJDEA to further improve convergence rate and refining the quality of the best solution. Four different algorithms, genetic algorithm, particle swarm optimization, Jaya and the proposed HJDEA are employed for comparative study. In addition, the objective function is established by adjacent acceleration correlation function so as to avoid false identification caused by defining improper reference point. The performance of the proposed damage identification strategy based on HJDEA and adjacent acceleration correlation function is investigated with numerical examples involving an 8-DOF lumped mass model and a cantilever beam, as well as an experimental study of the ASCE benchmark structure under white noise excitation. Results show that the proposed hybrid identification method is accurate, efficient and robust in the identification of the damage existence, location and severity of stiffness and mass parameters even with partial output-only responses and 20% noise-polluted measurements.

Efficiency of the slime mold algorithm for damage detection of large‐scale structures

SummaryOptimization‐based methods are increasingly being implemented for structural damage detection problems through the minimization of the objective functions based on vibration data. The adopted optimization algorithm and objective function play an important role in the accurate detection and quantification of damages. Meanwhile, the challenge of long computational time is another aspect of structural damage identification problems, especially upon addressing large‐scale structures. In this paper, recently developed optimization techniques called slime mold algorithm (SMA) and marine predators algorithm (MPA) are applied to damage assessment of large‐scale structures for the first time. The performance of these algorithms is compared with those obtained using other well‐known optimization techniques such as ant lion optimizer (ALO), whale optimization algorithm (WOA), and grasshopper optimization algorithm (GOA). Furthermore, the sensitivity of three objective functions based on modal assurance criterion (MAC), modified total modal assurance criterion (MTMAC), and natural frequency vector assurance criterion (NFVAC) are examined. Two numerical studies, including the 53‐bar planar truss and the Guangzhou New TV tower, and a full‐scale three‐story frame as an experimental investigation are conducted to present a statistical comparison. The overall results show that the combination of SMA and objective function based on MTMAC provides an accurate tool for damage identification. However, improved SMA (ISMA) has been introduced to enhance the capability of standard SMA for damage detection, and especially finite element model updating in the experimental example. Five benchmark functions are also used to evaluate the global optimization capacity of ISMA and SMA. The results show that the ISMA has many benefits in terms of tackling global optimization problems. MPA‐MTMAC can provide promising results compared with ALO‐MTMAC, WOA‐MTMAC, and GOA‐MTMAC. However, excessive computation time is a big drawback for MPA. The overall results confirm the perfection of the objective function based on MTMAC compared with two others based on MAC and NFVAC.

A new metaheuristic optimization based on K-means clustering algorithm and its application to structural damage identification

This paper develops a new metaheuristic optimization algorithm named K-means Optimizer (KO) to solve a wide range of optimization problems from numerical functions to real-design challenges. First, the centroid vectors of clustering regions are established at each iteration using K-means algorithm, then KO proposes two movement strategies to create a balance between the ability of exploitation and exploration. The decision on the movement strategy for exploration or exploitation at each iteration depends on a parameter that will be designed to recognize if each search agent is too long in the region visited with no self-improvement. To demonstrate the effectiveness and reliability of KO, twenty-three classical benchmark functions, CEC2005 and CEC2014 benchmark functions, are employed as a first example and compared with other algorithms. Then, three well-known engineering problems are also considered and their results are compared to the results obtained by the other algorithms. Finally, KO is applied to structural damage identification (SDI) problem of a complex 3D concrete structure including seven stories building having a 25.2 m total height. For this purpose, SAP2000 is used to solve the finite element (FE) model of this structure. Then, for the first time, we successfully developed a sub-program that allows two-way data exchange between SAP2000 and MATLAB through the Open Application Programming Interface (OAPI) library to update the FE model. From the results, we found that KO has the best performance for the considered benchmark functions based on the Wilcoxon rank-sum test and Friedman ranking test. The results obtained in this work have proved the effectiveness and reliability of KO in solving optimization problems, especially for SDI. Source codes of KO is publicly available at http://goldensolutionrs.com/codes.html.

Damage identification of thin plates using multi-stage PSOGSA and incomplete modal data

ABSTRACT The present work proposes a multi-stage optimization technique for damage detection of the thin plate-like structure using sparse modal data collected from the limited sensors. The response of the thin plate structures is obtained from a finite element model, which is developed with constant strain triangle elements. The iterated improved reduction system (IIRS) is applied to simulate the state of sparse sensors numerically. Next, the damage identification problem is formulated as a constrained optimization problem. A weighted linear combination of the natural frequencies and modes shapes of the structure is used to construct the objective function. The damage extent of each element is considered as the design variable. The formulated objective function is minimized by the proposed multi-stage strategy employing a hybrid metaheuristic algorithm, which is based on particle swarm optimization and gravitational search algorithm (PSOGSA). The algorithm identifies the healthy elements in each stage and eliminates them from the search space of the next stage. Thus, the search space dimension of the optimization problem reduces in each stage, and actual damaged elements are identified effectively. The efficiency of the proposed method is demonstrated through three numerical examples with different damage scenarios.

A two-stage approach for structural damage detection using a damage localization indicator vector and cluster particle swarm optimization

A two-stage approach based on the finite element model is proposed for structural damage detection. In the first stage, the damage identification problem is transformed into an -minimization issue related to the natural-frequencies-based sensitivity matrix, which can be quickly solved using the alternating directional multiplication method. Based on this, a damage localization indicator vector (DLIV) is proposed for dimensionality reduction. In the second stage, a particle swarm optimization (PSO) algorithm with four-cluster topology, namely Cluster PSO, is employed to identify the location and severity of the actual damage after dimensionality reduction. Therefore, the proposed two-stage approach can be denoted as the DLIV-based Cluster PSO. In the numerical simulations, the performance of the proposed DLIV-based Cluster PSO is evaluated for single and multiple damage detection of a cantilever beam. The simulation results demonstrate that the proposed DLIV-based Cluster PSO approach provides faster convergence, less computational time and better noise tolerance.

Structural damage identification considering uncertainties based on a Jaya algorithm with a local pattern search strategy and L0.5 sparse regularization

This study develops an improved Jaya algorithm for structural damage identification considering various uncertainties using vibration data. Most studies consider uncertainties, such as measurement noise, modeling uncertainties, and temperature variations separately but few on their coupling effects. On the other hand, the Jaya algorithm may trap local minimums in optimizing complex objective functions. Therefore, a novel modified Jaya algorithm is developed by integrating the one-step K-means clustering, Hooke–Jeeves pattern search, and linear population reduction strategies. The three modification strategies greatly improve the global optimization performance of the standard Jaya algorithm, which is verified by a series of high-dimension test functions. An L0.5 regularization is applied to improve the ill-posedness of the damage identification problem and ensure the sparsity of the solution. Numerical and experimental studies on two structures show that the proposed algorithm can identify the structural damage accurately, even considering the coupling effects of various types of uncertainties.

Research on Damage Localization of Steel Truss–Concrete Composite Beam Based on Digital Orthoimage

Most structural health monitoring is carried out for a limited number of key measurement points of a bridge, and incomplete measurement data lead to incomplete mechanical equation inversion results, which is a key problem faced in bridge damage identification. The ability of digital images to holographically describe structural morphology can effectively alleviate the problem of damage identification due to incomplete test data. Based on digital image processing technology, a matrix similarity damage identification method based on a structural digital orthoimage was proposed. Firstly, a steel truss–concrete composite beam specimen with a complex support bar system was designed and fabricated in the laboratory, and the digital orthoimage of the test beam was obtained by the perspective transformation of the original image of the test beam. The body contour of the structure was extracted from the digital orthoimage of the test beam, and wavelet threshold denoising was performed on the lower edge profile to obtain the deflection curves of the structure under different working conditions. The verification results show that the maximum error of the deflection curve is 3.42%, which proves that the digital orthoimage can accurately and completely reflect the deformation of the structure. Finally, based on the digital orthophoto of the test beam, a matrix similarity test before and after the damage was carried out, and the results show that the singularities of the similarity distribution are consistent with the location of the damage; furthermore, the accurate positioning of the damage in different working conditions is achieved.

SDFormer: A Novel Transformer Neural Network for Structural Damage Identification by Segmenting the Strain Field Map.

Damage identification is a key problem in the field of structural health monitoring, which is of great significance to improve the reliability and safety of engineering structures. In the past, the structural strain damage identification method based on specific damage index needs the designer to have rich experience and background knowledge, and the designed damage index is hard to apply to different structures. In this paper, a U-shaped efficient structural strain damage identification network SDFormer (structural damage transformer) based on self-attention feature is proposed. SDFormer regards the problem of structural strain damage identification as an image segmentation problem, and introduces advanced image segmentation technology for structural damage identification. This network takes the strain field map of the structure as the input, and then outputs the predicted damage location and level. In the SDFormer, the low-level and high-level features are smoothly fused by skip connection, and the self-attention module is used to obtain damage feature information, to effectively improve the performance of the model. SDFormer can directly construct the mapping between strain field map and damage distribution without complex damage index design. While ensuring the accuracy, it improves the identification efficiency. The effectiveness and accuracy of the model are verified by numerical experiments, and the performance of an advanced convolutional neural network is compared. The results show that SDFormer has better performance than the advanced convolutional neural network. Further, an anti-noise experiment is designed to verify the anti-noise and robustness of the model. The anti-noise performance of SDFormer is better than that of the comparison model in the anti-noise experimental results, which proves that the model has good anti-noise and robustness.