Identification of Structural Damage in Bridges Using High-Frequency Vibrational Responses

As sensor monitoring technology continues to evolve, structural online monitoring and health management have found numerous applications across various fields. However, challenges remain concerning the real-time diagnosis of structural damage and the accuracy of dynamic reliability predictions. In this paper, a structural online damage identification and dynamic reliability prediction method based on Unscented Kalman Filter (UKF) is presented. Specifically, in the Wiener degradation process with random effects on structural performance, the structural damage identification is initially realized using UKF. Following that, the EM algorithm is employed for estimating the performance model parameters. Eventually, dynamic reliability prediction is realized based on conditional probability. The simulation results indicate that the method effectively estimates the damage state during the structure’s use while providing accurate, real-time, and dynamic reliability predictions for the system.

Purpose Aiming at the problems of high cost, high risk and traffic impact of in situ load testing of Bridges after fire, this paper proposes a fire bridge structural damage identification method based on a fire dynamics simulator (FDS)-Kriging model modification and a unit modal strain energy damage index, so as to effectively assess the damage of bridge structures after fire. Determine whether it can continue to be used and whether it affects driving safety. Design/methodology/approach Propose a fire bridge structural damage identification method to establish the FDS-Kriging model correction. Findings The finite element model of the fire bridge modified using FDS-Kriging approach can reflect the actual situation of the bridge structure more accurately, and the damage location and degree identified by the modal strain energy method are consistent with the actual bridge. Therefore, the effectiveness of the proposed method has been verified, and a new strategy for fire bridge damage identification has been provided. Research demonstrates that the finite element model of the fire-damaged bridge, modified using the FDS-Kriging method, can more accurately reflect the actual structural state. Moreover, the damage location and severity identified via the modal strain energy method align well with the actual bridge conditions. Thus, the effectiveness and engineering applicability of the proposed method are validated, offering a comprehensive new strategy and technical support for the integrated performance evaluation, damage identification and reliability analysis of fire-damaged bridges. Originality/value By determining the finite element correction interval and combining it with the Kriging model, the method realizes the fine correction of the finite element model of the fire bridge. Structural damage identification is carried out by using the unit modal strain energy damage index. This verifies the effectiveness and engineering applicability of the proposed method, which offers a novel approach and technical support for the comprehensive performance evaluation, damage identification and reliability analysis of fire-damaged bridges.

Vibration-based damage identification and structural health monitoring have been developed in recent years. However, it is still a challenge to effectively extract the information of structural damage from a large amount of monitoring data and to realize the rapid detecting damages and assessing structural conditions of bridge structures under seismic excitation. To address this problem, transmissibility function (TF) is used in this paper, and based on the seismic monitoring data of bridge structures, an intelligent algorithm integrating transmissibility function and support vector machine (SVM) is developed for multi-level/scale structural damage identification under seismic excitation. Firstly, the TF is calculated with the measured structural vibration responses. Then, the indicator takes the sum of transmissibility along the specific frequency range is used as input for SVM. Finally, the classification algorithm of support vector machine is employed to structural damage alarming under earthquake. The proposed method could eliminate the influence of external excitations because transmissibility function only depends on the measured seismic responses of structures and the information of external excitations is not required. Further, the proposed method is sensitive to the structural damage. To illustrate the performances and feasibility of the proposed algorithm, the damage identification of numerical simulation under seismic excitation is studied.

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

Considering the issue of misjudgment in railway bridge damage identification, a method combining the step-by-step damage detection method with the statistical pattern recognition is proposed to detect the structural damage of a railway continuous girder bridge. The whole process of damage identification is divided into three identification sub-steps, namely, damage early warning, damage location, and damage extent identification. The multi-class pattern classification algorithm of C-support vector machine and the regression algorithm of ε-support vector machine are engaged to identify the damage location and damage extent, respectively. For verifying the proposed method, both of the proposed method and the optimization method are used to deal with the measured data obtained from a specific railway continuous girder model bridge. The results show that the proposed method can not only identify the damage location correctly, but also obtain the damage extent which is consistent with the experimental results accurately. By uncoupling finite element analysis and damage identification, normalizing the index, and seeking the separation hyper plane with maximum margin, the proposed method has more favorable advantages in generalization and anti-noise. As a result, it has the ability to identify the damage location and extent, and can be applied to the damage identification in real bridge structures.

This paper proposes a novel method for structural damage identification that integrates the power spectral density (PSD) of structural acceleration responses with densely connected convolutional networks (DenseNet). The method transforms the training object of the DenseNet into a numerical matrix (PSD matrix) for structural damage identification. Leveraging transfer learning, the DenseNet models are initially trained on simulated data and further fine‐tuned using experimental data to enhance robustness and generalization. Results demonstrate that frequency‐domain signals processed by PSD significantly enhance model performance, achieving lower mean squared error (MSE), higher Pearson’s correlation coefficient ( R value), and reduced mean absolute error (MAE) compared to time‐domain signals. The effectiveness of this method was verified on a six‐story frame structure. This study underscores the efficacy of transfer learning in bridging the gap between simulated and real‐world data, thereby facilitating effective structural health monitoring and damage identification.

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

Composite structures are generally more susceptible to impact damage than non-composite structures, and early identification of damage is the primary goal of structural health monitoring (SHM). If such damage remains undetected or reaches a critical size, it can lead to sudden collapse and catastrophic failure. Modern SHM methods aim to preserve the integrity of composite structures through continuous inspection, monitoring, and damage assessment, including detection, localization, quantification, classification, and prognosis. These methods use sensor-based technologies to assess vibration, extension, and acoustic and thermal emission. This paper provides a review of various computational methods including physics-based methods (signal processing techniques, modal analysis, and finite element model updating) and optimization methods (inverse problems, particle swarm optimization, topology optimization, genetic algorithms, time series analysis, and hybrid techniques), alongside machine learning methodologies employing neural networks as well as deep learning for damage identification in composite structures. These computational and learning-based techniques are widely applied in the development of algorithms, optimization strategies, and hybrid frameworks for SHM. The review further summarizes the applications, advantages, and limitations of each method according to structure type and damage characteristics. The key emphasis of this review is on integrating computational approaches, as well as machine learning, to enhance the efficiency of damage identification. The conclusion is drawn based on an overview of the literature, focusing on the contributions of different computational methods and machine learning for damage identification in composites.

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.

This paper proposes a novel structural damage identification approach by modifying the original Tree Seeds Algorithm (TSA), termed as M-TSA. To enhance the performance of the standard TSA, a one-step K-means clustering mechanism is conducted before starting the seeds search, which turns out to be beneficial to improve the standard algorithm’s global search capacity. The objective function is formulated by using the frequency do-main data. The first several natural frequencies and the incomplete mode shape are employed to build up the objective function and the proposed M-TSA is adopted to obtain the optimal solution and reflect the structure’s health status. Numerical studies on a 26-bar truss are conducted to investigate the accuracy of using the proposed approach for structural damage identification. Two types of uncertainties including the finite element modelling errors and measurements noise, are considered simultaneously. The final identification results show that with the assistance of the K-means clustering, the proposed M-TSA is able to identify the damages accurately and the performance is better than the standard TSA.

A novel procedure for damage identification of framed structures is proposed, where both the location and the extent of structural damage in framed structures can be correctly determined using only a limited number of measured natural frequencies. No knowledge of the modal shapes of the damaged structure is required. On the basis of the characteristic equations for the original and the damaged structure, a set of equations is generated. Two computational techniques, the direct iteration (DI) technique and the Gauss–Newton least-squares (GNLS) technique, are utilized to determine structural damage from the derived equations. Finally, different numerical examples are used to demonstrate the effectiveness of the proposed method. © 1997 John Wiley & Sons, Ltd.

The accuracy of structural damage identification is affected by the uncertainties in the vibration measurements and the finite element modeling. This paper proposes a novel approach based on sparse deep belief network (DBN) for structural damage identification with uncertain and limited data. Vibration characteristics, that is, natural frequencies and mode shapes, are extracted as the input to the network, while the output are the damage locations and severities of the structure. DBN is chosen to train the generated data sets and identify structural damages. Restricted Boltzmann Machines (RBMs) are used as building blocks to composite a DBN. To further enhance the capacity of the RBMs, an arctan-based sparse constraint is utilized to enable the hidden units to become sparse. This is achieved by adding an arctan norm constraint on the whole of the hidden units' activation probabilities. Numerical and experimental studies are conducted to verify the accuracy and performance of the proposed method. Undetermined damage identification is conducted, in which the quantity of input modal data is less than that of the system parameters to be identified. The identification results show that the proposed sparse DBN based on arctan can identify the damage effectively, and its accuracy is better than those obtained by other methods, even when the modeling uncertainty and the measurement noise exist and only limited data is available.

Structural damage identification (SDI) serves as an indirect approach that has the potential to meet real‐time monitoring of structures. However, the identification accuracy and efficiency of some methods need to be improved, especially when there are some uncertain interfering factors or noise. This paper presents a new optimization algorithm and an improved objective function for inverse problems of SDI, offering an effective solution for bridge damage identification under uncertain noise interference and incomplete modal data. In this study, by hybridizing the whale optimization algorithm and the sand cat swarm optimization, a novel whale‐sand cat swarm optimization (W‐SCSO) method is proposed for SDI. The cubic chaotic mapping is introduced for initialization of the W‐SCSO method, and then the lens opposition‐based learning and the stochastic differential mutation are employed to enhance the search capability and convergence accuracy of the proposed algorithm. Besides, the mode shape curvature, the frequency change ratio, and the L 1/2 sparse regularization are used to improve the objective function. Four other existing state‐of‐the‐art methods are used to verify the performance of the proposed W‐SCSO method by the CEC2017 benchmark functions and a simply supported beam finite model. The comparative analysis highlights the feasibility and effectiveness of the proposed method in the considered cases. Moreover, an aluminum alloy simply supported beam was conducted for the SDI experiment to further prove the effectiveness of the improved method in practice. Simulation and experimental results show that the proposed method effectively locates and quantifies stiffness reduction in bridge structures, which maintains high accuracy in damage identification despite potential modal incompleteness and uncertain measurement noise interference.

Structural damage identification using strain mode differences by the iFEM based on the convolutional neural network (CNN)

PurposeThe paper aims to identify both the location and severity of damage in complex framed buildings using limited noisy vibration measurements. The study aims to directly adopt incomplete measured mode shapes in structural damage identification and effectively reduce the influence of measurement errors on predictions of structural damage.Design/methodology/approachDamage indicators are properly chosen to reflect both the location and severity of damage in framed buildings at element level for braces and at critical point level for beams and columns. Basic equations for an iterative solution procedure are provided to be solved for the chosen damage indicators. The Tikhonov regularisation method incorporating the L‐curve criterion for determining the regularisation parameter is employed to produce stable and robust solutions for damage indicators.FindingsThe proposed method can correctly assess the quantification of structural damage at specific locations in complex framed buildings using only limited information on modal data measurements with errors, without requiring mode shape expansion techniques or model reduction processes.Research limitations/implicationsFurther work may be needed to improve the accuracy of inverse predictions for very small structural damage from noisy measurements.Practical implicationsThe paper includes implications for the development of reliable techniques for rapid and on‐line damage assessment and health monitoring of framed buildings.Originality/valueThe paper offers a practical approach and procedure for correctly detecting structural damage and assessing structural condition from limited noisy vibration measurements.