Proposed Damage Identification Method Research Articles

Exploration of Damage Identification Method for a Large-Span Timber Lattice Shell Structure in Taiyuan Botanical Garden based on Structural Health Monitoring.

Large-span spatial lattice structures generally have characteristics such as incomplete modal information, high modal density, and high degrees of freedom. To address the problem of misjudgment in the damage detection of large-span spatial structures caused by these characteristics, this paper proposed a damage identification method based on time series models. Firstly, the order of the autoregressive moving average (ARMA) model was selected based on the Akaike information criterion (AIC). Then, the long autoregressive method was used to estimate the parameters of the ARMA model and extract the residual sequence of the autocorrelation part of the model. Furthermore, principal component analysis (PCA) was introduced to reduce the dimensionality of the model while retaining the characteristic values. Finally, the Mahalanobis distance (MD) was used to construct the damage sensitive feature (DSF). The dome of Taiyuan Botanical Garden in China is one of the largest non-triangular timber lattice shells worldwide. Relying on the structural health monitoring (SHM) project of this structure, this paper verified the effectiveness of the damage identification model through numerical simulation and determined the damage degree of the dome structure through SHM measurement data. The results demonstrated that the proposed damage identification method can effectively identify the damage of large-span timber lattice structures, locate the damage position, and estimate the degree of damage. The constructed DSF had relatively strong robustness to small damage and environmental noise and has practical application value for SHM in engineering.

A Damage Identification Method Based on Minimum Mean Square Error Estimation for Wind Tunnel Flexible Plate Condition Monitoring System

In this paper, we propose a damage identification method based on minimum mean square error estimation for a wind tunnel flexible plate condition monitoring system. Critical structural members of important equipment are large in size, and the measurement systems used to monitor their condition are often complex. The proposed damage identification method is based on the minimum mean squared error estimator and the generalized likelihood ratio test. It introduced activation function to generate the standard deviation of the data, which can then simulate the sensor output. A single sensor damage only affects a single dimension of the output data matrix of the measurement system. However, structural damage affects the output of multiple sensors. The damage identification method proposed in this paper can not only distinguish the sensor damage from the structure damage, but also locate the damaged sensor or structure damage location. This method can identify the measurement system output anomalies caused by structural damage and locate the approximate location of the damage. It can be applied to damage identification of important structural members such as flexible wind tunnel plates. The damage identification method proposed in this paper is of great significance for damage identification and localization of key components and sensor systems.

Classification and regression-based convolutional neural network and long short-term memory configuration for bridge damage identification using long-term monitoring vibration data

Considerable attention has recently been focused on classification and regression-based convolutional neural network (CNN) and long short-term memory (LSTM) due to their excellent performance in capturing complex spatial and temporal information characteristics for structural damage identification. However, few studies have considered structural damage identification as a classification and regression problem. In addition, bridges in practical engineering are vulnerable to various environmental and vehicle loading conditions. Hence, this study proposed a new two-stage CNN–LSTM configuration for bridge damage identification using vibration data considering the influence of temperatures. First, a classification-based CNN–LSTM is designed to perform multiclass damage detection tasks, and then a regression-based CNN–LSTM is developed for damage localization and severity prediction tasks. The performance of the proposed damage identification method was evaluated through a simulation dataset of a concrete highway bridge model and a field experiment dataset of Z24-bridge (Switzerland). In addition, a set of statistical evaluation metrics such as sparse categorical cross-entropy loss, accuracy, confusion matrix, mean squared loss, mean absolute error, mean absolute percentage error, and coefficient of determination were used to compare the damage identification performance of the proposed CNN–LSTM configuration with a regular CNN model and conventional machine learning (ML) algorithms. Prediction results indicate that the proposed CNN–LSTM model outperforms the regular CNN model and conventional ML algorithms for bridge damage identification.

Comparative study of using rotation influence lines and their derivatives for structural damage detection

Structural damage detection has recently become one of the most important topics for engineering structures due to its benefits in enhancing safety, reducing life-cycle cost and providing guidance for system construction and maintenance. This research studies the accuracy of using rotation influence lines (RIL) and their derivatives (first and second derivatives) for detecting structural damage characteristics (location and severity). The study includes an analytical investigation of damage detection of multiple damages in simply supported beam. Then, numerical studies have been conducted to investigate the effectiveness of the proposed method for damage identification of simply supported beam (determinate structure), continuous beam (indeterminate structure) with different damage characteristics. Furthermore, the most effective inclinometer locations and number of inclinometers for structural damage detection on simply supported and continuous beams are reported. Furthermore, noise intensity effects from the limited inclinometer precision, inclinometer faults and transmission errors are discussed in this paper.

A Novel Structural Damage Identification Method Using a Hybrid Deep Learning Framework

In the past few years, structural health monitoring (SHM) has become an important technology to ensure the safety of structures. Structural damage identification methods based on machine learning techniques have gained wide attention due to the advantages of effectively extracting features from monitoring data. However, the existing machine learning-based methods heavily depend on manually selected feature parameters from raw signals. This will cause the selected feature to obtain the optimal solution for a specific condition but may fail to provide a similar performance in other cases. In addition, the feature selection takes a long time, which can fail to achieve real-time performance in a practical structure. To address these problems, this article proposes a hybrid deep learning framework for structural damage identification that includes three components, namely, ensemble empirical mode decomposition (EEMD), Pearson correlation coefficient (PCC), and a convolutional neural network (CNN). The proposed EEMD-PCC-CNN method is capable of automatically extracting features from raw signals to satisfy any damage identification objective. To evaluate the performance of the proposed EEMD-PCC-CNN method, a three-story building structure is investigated. The acceleration signal of the three-story building structure is first analyzed by EEMD. After obtaining the time-frequency information, PCC is utilized to select optimal time-frequency information as the input of the CNN for damage identification. Compared with other classical methods (SVM, KNN, RF, etc.), the experimental results show that the newly proposed EEMD-PCC-CNN method has significant performance advantages in damage identification. In addition, the accuracy of the proposed damage identification method is improved by more than 4% after utilizing EEMD in comparison with CNN alone.

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.

Damage identification using wave damage interaction coefficients predicted by deep neural networks

The ever-increasing demand for efficiency and cost improvements in lightweight structures with guaranteed safety and reliability is leading to the application of a damage-tolerant design philosophy. Here, accurate knowledge of structural health is critical to avoid catastrophic failures. This knowledge can be obtained by using advanced structural health monitoring (SHM) systems. For thin-walled lightweight structures, methods utilizing guided waves generated by piezoelectric transducers are well suited. The interaction between the guided waves and potential damages can be described by so-called wave damage interaction coefficients (WDICs). These WDICs are unique for each damage and depend solely on its characteristics for a given structure. Therefore, the comparison of known WDICs with estimated ones allows drawing conclusions about the current structural state. In this paper, a novel damage identification method for plate-like structures based on a database of such WDICs is presented. Selected damages are simulated numerically with finite elements to generate WDIC patterns. However, these simulations are computationally highly demanding, thus only a very limited number of damage scenarios can be simulated. This study proposes an innovative technique to substantially enhance the resulting WDIC database by using deep neural networks (DNNs). These DNNs enable smart interpolations and allow not only predicting WDICs for previously unseen damages at low computational costs but also the discovery of knowledge about the complex relationship between damage features and WDIC patterns. A comparison to other machine learning algorithms clearly shows the superior performance of the utilized DNNs for interpolating complex WDIC patterns. The proposed damage identification method is verified using advanced time-domain simulations of a large aluminum plate. A statistical analysis of correct identification rates in a common three-sensor setting is employed for assessing the general performance. It is demonstrated that carefully identified DNNs enable to accurately replicate and interpolate complex WDIC patterns. Furthermore, it is shown that these predicted WDICs allow identifying damage characteristics with high confidence.

Substructure Damage Identification Based on Model Updating of Frequency Response Function

Model updating of large-scale structures is difficult to carry out when using a frequency response function (FRF) for damage identification, as the solutions for the global system matrices with too many degrees of freedom are required in each iteration. In this paper, a substructure damage identification method is proposed based on the model updating of the acceleration FRF. The original finite element model is divided into several substructures using the improved reduced system (IRS) by the dynamic condensation method, resulting in the simplified substructure model. The final simplified model is composed of the simplified mass matrix and stiffness matrix of the substructure considered. The damage acceleration FRF to be identified is used to iteratively update the simplified model. The locations and extents of the damage elements are obtained by updating the results, which reduces the number of uncertain parameters to be updated and leads to the rapid convergence of the optimization process. In the iteration, L1 norm regularization is introduced to solve the ill-posed problem, which improves the stability of the identification results. A numerical simulation of a six-story steel frame structure under various working conditions was carried out to verify the effectiveness of the proposed method, which was also validated by the experiments. The robustness and performance of the proposed damage identification method based on substructures have been demonstrated.

Bayesian damage identification of simply supported beams from elastostatic data

This paper considers a statistical method for damage identification of simply-supported elastic beams from static data. The problem is cast as an inverse problem and analyzed in Bayesian inversion framework. The main goal is to obtain a probabilistic model for the spatial distribution of the damage across a beam given limited and noisy data on the static beam response. A principal distinction of the proposed method is its ability to identify not only an estimate of the damage occurred to the beam but also the uncertainty (error) associated with such an estimate. Moreover, the proposed method uses simple static data instead of dynamic or spectral data, unlike the majority of research work published in the literature. This is a challenge as static data is a rather limited source of information about the underlying physical system compared to dynamic data. Finally, the proposed method does not involves any computationally expensive high-dimensional integration algorithms unlike the existing methods. According to the numerical analysis conducted in this paper, the result obtained by using the proposed damage identification method lies within one standard deviation interval.

Baseline-Free Damage Identification Method for Lattice Sandwich Structures Based on Operational Deflection Shapes

ABSTRACT A novel baseline-free damage identification method based on high frequency operational deflection shapes (ODSs) is presented for debonding detection in lattice sandwich structures (LSSs). Two numerical models with different unit cells are constructed to analyze the vibration characteristics of a structure with debonded defect in the high-frequency band. The mode shapes and ODSs are computed numerically to investigate the local defect vibration effects. The results show that there will be obvious local vibration at the damaged location at a certain and appropriate frequency band. A baseline-free damage index calculated from ODSs is originally proposed for damage imaging. For experimental validation, we suggested an intermittent periodic excitation signal for vibration actuating, which may excite multiple ODSs at different frequencies using one measurement that significantly improve the detection efficiency. The experimental results also indicated that the proposed damage identification method is effective to locate the debonding damage in LSSs. The conclusions derived from this study are expected to provide an efficient vibration measurement technique and a practical damage detection method for LSSs, as well as other plate-like structures.

Residual Mode Vector-Based Structural Damage Identification with First-Order Modal Information

Damage identification based on the change of dynamic properties is an issue worthy of attention in structure safety assessment, nevertheless, only a small number of discontinuous members in existing structure are damaged under service condition, and the most remaining members are in good condition. According to this feather, we developed an effective damage location and situation assessment algorithm based on residual mode vector with the first mode information of targeted structure, which utilized the quantitative relationship between first natural modes of global structure with the change of the element stiffness. Firstly, the element damage location is determined with exploitation of the sparseness of element stiffness matrices based on the discontinuity of damaged members. Then, according to the distribution characteristics of the corresponding residual mode vector, the nodal equilibrium equation about the damage parameter is established based on the residual mode vector, and the damage coefficients of structural elements are evaluated with the proposed equations. Two numerical examples are given to verify the proposed algorithm. The results showed that the proposed damage identification method is consistent with the preset damage. It can even accurately identify large-degree damages. The proposed algorithm only required the first-order modal information of the target structures and held few requirements of analysis resource; hence when compared with existing methods, it has obvious advantages for structural damage identification.

Damage identification for bridge structures based on correlation of the bridge dynamic responses under vehicle load

In order to realize the damage identification of the bridge, a method based on the correlation of vehicle-induced response of the bridge is proposed. Firstly, using the measured vibration responses of the bridge under vehicle load, damage indexes based on Pearson correlation coefficient, Spearman correlation coefficient and Kendall correlation coefficient are defined, respectively. Then, the proposed method is verified by numerical simulation and compared with the method based on Correlation function amplitude Vector (CorV). The effectiveness of the method for single damage and multiple damage of the bridge is examined. Numerical results demonstrate that the proposed method can effectively identify and locate the damage, and the effectiveness of the index based on Pearson correlation coefficient is the best. Finally, the proposed damage identification method is verified by experiments, and the results manifests that the comprehensive decision-making based on different damage indexes is helpful to improve the accuracy of damage location. Besides, the influence of road-surface roughness on damage identification is discussed. Results indicate that the damage indexes are affected by road-surface roughness, and the statistical mean value is beneficial to identify and locate the damage.

Damage identification by wavelet analysis of modal rotation differences

In this paper, a novel damage identification approach based on differences in modal rotation fields obtained from shearographic measurements of structures and post-processed with the 2D continuous wavelet transform-based algorithm is presented. It is shown that the baseline approach, which assumes analysis of differences in modal rotation fields, allows to improve the sensitivity of a method to damage, making it possible to identify smaller damage with respect to the baseline-free approach. Moreover, a new approach for the reduction of the boundary effect is presented, which allows for the improvement visualization of the resulting maps using the proposed damage identification method. The proposed method was tested with eight damage scenarios, which allowed to prove repeatability of the obtained results, as well as to define a detectability threshold which is on the level of 3.5% of the thickness reduction.

A novel structural damage identification scheme based on deep learning framework

Deep learning algorithm can autonomously mine the representative information which is hidden in the data and provides a new idea for damage identification of building structures. Keeping in view that a structural damage identification based on time series data has low accuracy, a new method for structural damage identification based on IASC-ASCE SHM benchmark is proposed which combines the advantages of Hilbert-Huang transform (HHT) and deep neural network. The damage signal of the Benchmark model is first analyzed by HHT. After that the obtained time-frequency graph and the marginal spectrum of the signal are used as the input of the convolutional neural network. In addition, the structural parameters of CNN model are adaptively optimized by particle swarm optimization (PSO) algorithm to ensure better performance of CNN. Compared with other traditional methods (ANN, SVM), the experimental results show that the newly proposed damage identification method has significant performance advantages. Accuracy of the proposed CNN model is improved more than 10% after getting optimized by PSO in comparison with non-adaptive CNN model. CNN model also showed better robustness against noise interference.

Bearing Damage Detection of a Reinforced Concrete Plate Based on Sensitivity Analysis and Chaotic Moth-Flame-Invasive Weed Optimization.

This article proposes a novel damage detection method based on the sensitivity analysis and chaotic moth-flame-invasive weed optimization (CMF-IWO), which is utilized to simultaneously identify the damage of structural elements and bearings. First, the sensitivity coefficients of eigenvalues to the damage factors of structural elements and bearings are deduced, the regularization technology is used to solve the problem of equation undetermined, meanwhile, the modal strain energy-based index is utilized to detect the damage locations, and the regularization objective function is constructed to quantify the damage severity. Then, for the subsequent procedure of damage detection, CMF-IWO is proposed based on moth-flame optimization and invasive weed optimization as well as chaos theory, reverse learning, and evolutional strategy. The optimization effectiveness of the hybrid algorithm is verified by five benchmark functions and a damage identification numerical example of a simply supported beam; the results demonstrate it is of great global search ability and higher convergence efficiency. After that, a numerical example of an 8-span continuous beam and an experimental reinforced concrete plate are both adopted to evaluate the proposed damage identification method. The results of the numerical example indicate that the proposed method can locate and quantify the damage of structural elements and bearings with high accuracy. Furthermore, the outcomes of the experimental example show that despite the existence of some errors and uncertain factors, the method still obtains an acceptable result. Generally speaking, the proposed method is proved that it is of good feasibility.

A Nonparametric Method for Identifying Structural Damage in Bridges Based on the Best-Fit Auto-Regressive Models

An enhanced method to determine the best-fit auto-regressive model (AR model) for structural damage identification is proposed in this paper. Whereby, two parameters of the model, including the number of model order and the window size of data, are analyzed simultaneously in order to accomplish the optimized values by means of Akaike’s Information Criterion (AIC) algorithm. The damage condition of structures can be detected by defined damage indicators obtained from the first three AR coefficients of the best-fit AR models. The ability of the proposed damage identification method is compared with the process that only utilizes conventional AR models without concern of parameter selection. The proposed method is verified using experimental data previously collected from a large-size bridge structure in the Structural Laboratory at the University of Central Florida. The results indicate that this method can detect and locate damage more effectively.

Damage identification of bridge structure based on frequency domain decomposition and strain mode

Because it is difficult to determine the degree and location of damage to bridge structures under environmental excitation, a new method combining frequency domain decomposition and strain mode identification for bridge structure damage assessment is proposed and the related identification principle is derived. General variations in the strain mode parameters of different reinforced concrete beam damage states are accordingly studied under ambient excitation. The results show that the proposed strain mode identification method based on frequency domain decomposition has good anti-noise performance and can identify the strain mode parameters of a structure relying solely on the strain response information of the structure, even under strong background noise. The mutation of the strain mode was effectively used to determine the damage condition of reinforced concrete beams, and the adaptability, feasibility, and reliability of the proposed method for modal parameter and damage identification of reinforced concrete beams under environmental excitation were verified.

Application of a mode shape derivative-based damage index in artificial neural network for structural damage identification in shear frame building

Artificial Neural Networks (ANN) have been proven applicable for updating finite-element (FE) baseline model and structural damage assessment. Most ANN-based damage identification methods use natural frequencies and mode shapes as input layer, limiting their application to quantifying single symmetrical damage in small structures. However, getting higher modal information of a structure is a crucial challenge in practice. As of late, researchers began utilizing mode shape derivatives as input layer in ANN to defeat the challenges for damage assessment in real-life structures. This study, therefore, proposes an ANN-based damage assessment method that employs the change in the first mode shape slope (CFMSS) damage index (DI) as input layer in ANN. For single-damage scenarios, the CFMSS-based DI has been able to detect, locate, and quantify the damage. For multiple-damage scenarios, the DI and corresponding stiffness reduction (SR) are fit as input and output layers, respectively, in ANN to measure the damage severity. Structural damage intensity is indicated as rate of decrease in story stiffness compared to baseline model. The efficiency of the proposed damage identification method is demonstrated through a nine-story numerical shear frame model and an experimental test on a three-story steel shear frame model.

Non-contact structural health monitoring of a cable-stayed bridge: case study

In this article, the condition assessment of a cable-stayed bridge using remote sensing is presented. The displacement influence line (DIL) of the bridge under the live load tests is measured for a discrete number of target points. Three different remote sensing techniques including, laser scanning, terrestrial robotic total station and digital levelling are adopted for this purpose. It is demonstrated that DIL obtained by non-contact system is capable of identifying an emulated damage in an actual operating system. The contribution of the work is fourfold. First, a damage index based on the displacement profile of the bridge under the weigh-in-motion is extracted from the non-contact sensing system. Second, our study compares three different remote sensing techniques, namely, digital levelling, robotic total station and laser scanning and uses the measurements to validate the finite element model. Third, the effectiveness of the proposed method for structural damage identification is validated in a real-world large-scale operating structure. Finally, it is validated that strain-based influence line is highly likely to misidentify damage especially when the location of damage is not in the close proximity of the sensor; however, DIL is a better damage indicator even if damage occurs far from the measurement point.

Accelerometer Optimization Placement Using Improved Particle Swarm Optimization Algorithm Based on Structural Damage Identification

In this paper, an Accelerometer optimization arrangement method based on Structural Damage Identification (SDI) is proposed. Firstly, the method constructs a fitness function according to the selection of the optimal measuring point of the displacement mode based on SDI, which selects a group of points with the largest displacement. Secondly, the efficient intelligent optimization is adopted. The algorithm, the improved particle swarm optimization algorithm, searches for the optimal placement position of the sensor. Finally, the feasibility and effectiveness of the proposed method for sensor optimal placement and damage identification are verified by an example of the wheel frame structure. Simulation and experimental results demonstrate the effectiveness of the proposed method.