Progresses and Perspectives of Damage Identification in Bridge Structure

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.

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

This paper first describes the importance of structural damage identification and diagnosis in civil engineering, and introduces domestic and foreign status of damage identification and diagnosis methods, and on the basis of this, it also introduces all kinds of methods for damage identification and diagnosis of civil engineering structures, and finally puts forward the development direction of civil engineering structure damage identification and diagnosis.

Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm

Structural damage identification for large-scale structures with uncertainties consumes numerous computational time and memory. The substructuring method is a model order reduction method, which is helpful to alleviate the computational burden. This paper proposes a statistical damage identification method using substructure-based stochastic finite element model updating to rapidly identify the damage of each element. First, the reduced order model of global structure is derived by retaining a few substructural modes as the reduction basis, where the inertial and elastic effects of discarded modes are considered. Next, the mean and standard deviation of identified structural parameters are rapidly solved by performing first-order perturbation analysis on the finite element model updating-based damage identification function of the reduced order model. Finally, the damage probability of each element is obtained using the mean and deviation of identified structural parameters before and after damage. A numerical one-bay plane frame and a numerical highway bridge are applied to verify the accuracy and efficiency of the proposed method.

The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extens of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies.

Structural damage identification based on metaheuristic algorithms is an important part of structural health monitoring with great potential. However, the metaheuristic intelligent algorithms probably have flaws of slow convergence speed and low calculation accuracy, which need to be improved to address engineering optimization problems. In this paper, the black widow optimization (BWO) algorithm is used for structural damage identification. In addition, a multistrategy fusion–improved BWO (IBWO) algorithm is proposed by introducing the tent chaotic mapping, the golden sine equation, the gazelle wandering equation, and the boundary treatments. First, in the population initialization stage, tent chaotic mapping is introduced to improve the quality of the initial solution. Second, the golden sine strategy is used to acquire the optimal solution quickly in local search. Then, the motion equation of the gazelle algorithm is employed to enhance the global search ability and avoid the algorithm falling into the local optimal solution. Finally, the boundary processing strategy is presented to reduce the calculation of solutions and improve the optimization efficiency. A novel damage identification objective function is redefined by combining the modal assurance criterion and the modal flexibility. Then, a two‐story rigid frame structure is utilized for numerical simulations. Moreover, experimental studies with a simply supported beam were carried out to verify the performance of the proposed damage identification method. Simulation results and experimental studies demonstrate that, even with the interference of strong noise, the IBWO algorithm has a higher accuracy and efficiency in damage identification compared to the BWO algorithm.

Quantifying uncertainty for structural damage identification in the presence of model errors from a deterministic sensitivity-based regime

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

In the past few decades, various techniques for structural damage identification based on dynamic properties have been widely studied, among which modal curvature is the research hotspot, due to its high sensitivity to local damage. To realize story-level structural damage identification of shear buildings, this paper first derives the patterns of variation in modal curvature of the damaged structure. Then, a new damage index sensitive to local damage called the Modal Curvature Change modified with Softmax (MCCSM for short), is proposed to improve the accuracy of structural damage identification. Afterward, the validity of the damage index is verified by a shaking table test of a scaled RC frame structure model and its corresponding numerical model. The research results show that the damage location indicated by MCCSM corresponds well with the damage phenomenon in the shaking table test and numerical simulation; compared with using the theoretical mode shape, the MCCSM index can give comparable damage identification results using the mode shape obtained through operational modal analysis from vibration time-history data, which proves the feasibility in engineering practices; through numerical simulation, it is proved that the MCCSM index is applicable in a wide range of damage cases with different story position, number of damaged story, and damage extent.

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.

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 method of damage identification of piles was established by using vibration characteristics. The approach focused on the application of the element strain energy and sensitive modals. A damage identification equation of piles was deduced using the structural vibration equation. The equation contained three major factors: change rate of element modal strain energy, damage factor of pile, and sensitivity factor of modal damage. The sensitive modals of damage identification were selected by using sensitivity factor of modal damage firstly. Subsequently, the indexes for early-warning of pile damage were established by applying the change rate of strain energy. Then the technology of computational analysis of wavelet transform was used to damage identification for pile. The identification of small damage of pile was completely achieved, including the location of damage and the extent of damage. In the process of identifying the extent of damage of pile, the equation of damage identification was used in many times. Finally, a stadium project was used as an example to demonstrate the effectiveness of the proposed method of damage identification for piles. The correctness and practicability of the proposed method were verified by comparing the results of damage identification with that of low strain test. The research provided a new way for damage identification of piles.