Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter

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.

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

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.

Structural damage identification techniques are methodologies that detect and locate damage in structures and quantitatively assess its severity via measured responses. During the service periods, structures are confronted with different unexpected situations which may likely not be considered in the design process. And, more often than not, these loads will introduce into the structures various damages. By comparing the dynamic response of the structure under investigation before and after possible damage, it is possible for people to detect these damages. The Two-Stage Structural Damage Identification is a promising scheme in the field. By this way, structural damages can be located and quantified in the two stages independently.

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 for structural damage identification using residual force vector and mode shape expansion is presented in this paper. To deal with the incompleteness of measured modal data, a mode shape expansion method is first introduced to match with all degrees of freedom of the finite element model under consideration. And then, the residual force vector is employed to locate the damage position and quantify the damage extent. In order to eliminate the influence of extra errors that caused by mode shape expansion and improve the efficiency of the methodology, residual force vector assurance criterion (RFVAC) is developed to analyze the sensitivity between two sets of residual force vectors in the health and damaged structures after mode shape expansion. Finally, the proposed method is numerically illustrated by a simply supported truss beam with three damage cases. Results indicate that the proposed method is effective for the structural damage identification when the measured mode shapes are incomplete.

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

Structural damage identification is a critical task in civil engineering for ensuring the safety and serviceability of infrastructures. Traditional methods often rely on manually selected features from vibration data, which may lack sensitivity to incipient or localized damage. In recent years, convolutional neural networks (CNNs) have demonstrated remarkable capability in automatically extracting discriminative features from raw data, providing a powerful data-driven alternative for structural health monitoring. This paper proposes a novel structural damage identification method based on a CNN framework. Numerical simulations of the free vibration of a simply supported beam were conducted using the finite element method. The modal strain energy of the first-order element was collected as the primary damage-sensitive feature. Through batch automatic calculation, a comprehensive dataset encompassing various damage scenarios—including different locations and severity levels—was efficiently generated to serve as training, validation, and testing samples for the network. The proposed CNN model was specifically designed and implemented in MATLAB to adapt to the characteristics of the structural damage detection data. This study systematically verifies the recognition performance of the CNN from three key aspects: identification of single damage location, quantification of damage severity at a single location, and detection of multiple damage locations. The results indicate that the CNN can not only accurately identify the location of structural damage with high precision but also achieve a considerable level of accuracy in quantifying the degree of damage, demonstrating its strong potential for comprehensive damage assessment in engineering structures.

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.

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

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.

Performance comparison of Kalman−based filters for nonlinear structural finite element model updating

Structural damage identification is the core of Structural health monitoring system. The paper points out existing damage identification methods and analysis the application status and disadvantages of various methods. The research hotspots are summarized on considering the statistical identification method under uncertainty and the environmental impact of damage identification methods. Aiming at the characters of bridge engineering, the paper points out that a four-in-one multi-system damage identification method is the develop direction for bridge structural damage identification, which is established based on construction, load testing, health monitoring and manual inspection.

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.

<p>An experimental implementation of a relative wavelet entropy (RWE)-based structural damage identification technique (DIT) is presented. The technique is capable of detecting and localizing structural damage, as well as estimating its severity, without the need for any data to be collected from undamaged (reference) state of structure. The bases of this reference-free DIT are: (1) structural damage changes the energy distribution of bridges’ vibrational signals; (2) these changes are detectable by means of discrete wavelet transforms (DWTs); and (3) the detected changes can be quantified using spectral entropy. The efficacy of the proposed RWE-based DIT in identification of structural damage has been verified through its application on a precast bridge truss girder system tested under fatigue loading. The girder consists of glass fibre-reinforced polymer (GFRP) tubes filled with concrete reinforced and connected to pretensioned top and bottom concrete chords by double-headed GFRP bars.‌</p>