Fatigue Damage Identification in Precast Truss Girders Using Relative Wavelet Entropy

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.

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.

<p>The fatigue behaviour of a novel precast hybrid bridge truss girder system is experimentally investigated. The girders consist of pretensioned top and bottom concrete chords connected by vertical and diagonal truss members made of concrete-filled fibre reinforced polymer (FRP) tubes. The truss members are connected to the top and bottom chords by means of long double-headed glass FRP (GFRP) bars. The chords are also reinforced with GFRP longitudinal bars and transverse stirrups. Six large-scale truss girders were fabricated and tested. All girders had identical cross-section dimensions with 1.32 m overall depth. Three of the girders were 2.83 m in length. The remaining three were</p><p>9.82 m long. One short and one long girder were tested under static loading up to failure. The remaining four girders were tested under cyclic fatigue loading of different levels and amplitudes. The tests showed excellent performance of the truss girders in terms of strength, stiffness, and fatigue life.</p>

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

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.

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 damage and its extent can be detected by vibration-based techniques to avoid failure or to minimize maintenance. Among different damage identification techniques, modal curvature approaches are widely researched and applied one. On the contrary, wavelet transformation (WT) methods are gaining popularity in damage identification of real life buildings because of their suitability for non-stationary signals and non-dependency on baseline data. This paper presents a novel approach utilizing complex continuous wavelet to effectively locate change in physical properties of reinforced concrete (RC) buildings by virtue of variation in frequency and mode shapes due to small change in mass and stiffness. In this paper, the effect of variation of mass and stiffness of a building on the modal parameters is established analytically using theequation of motion for a multi-degree freedom system under forced vibration condition. A 3-D finite element model was developed for predicting the modal frequencies and mode shapes of the scaled down six storey RC building and the effect of addition of mass on a particular level of structure on the modal parameters was studied. Further, acceleration time histories were recorded with variation in mass on 3rd story of building using wireless tri-axial accelerometers and the time histories were processed to arrive at Curvature Damage Factor and wavelet coefficients for identification of the additional load on the particular floor. Vibration responses from all floors of RC building in ambient and loaded conditions were analyzed for frequency response spectra (FRS). Mode shapes were drawn for unloaded case and loaded cases. It was observed that the modal frequency of building decreases with the increase in mass at floors. It is observed that CDF approach could detect the change in mass in both numerical and experimental results. However, CDF algorithm could not detect the addition of load in case 1, 2 and 3, i.e. when load was less than 25 kg, i.e. only 2.6% of floor mass (960 kg). The acquired data for the above stated load cases were analyzed using complex Gaussian ‘cgau5’ wavelet in MATLAB toolbox to determine the singularity in the signal in terms of wavelet coefficient modulus. It is observed that the WT approach is able to precisely locate the change in physical parameters of the RC model building. However, it is seen that additional load could not be detected in case where only 9 kg, i.e. 0.93% of the total floor mass, was placed on 3rd floor. From the research work, it is observed that CDF technique is inefficient in damage detection and always demand prior baseline information, which is usually difficult to obtain in practice. However, the wavelet transform-based approach more accurately detects the location of change without relying on intact state vibration data.

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.

The structural monitoring of mechanical systems is an extremely important task for ensuring its performance and structural health. To overcome limitations of traditional non-destructive inspections (NDIs), damage identification techniques have been developed from global indicators, mainly those based on modal data. In this study, damages are identified by solving an inverse problem. A fuselage model of an E190 aircraft is considered and the firefly algorithm (FA) metaheuristic is applied to solve the inverse problem in order to identify structural damages (location and severity). The method is then solved in two main fronts: (1) the direct problem using finite element analysis and (2) the inverse problem by minimizing an objective function. Evaluating modal response at many points on a large-scale structure can become prohibitive. For this, a method of optimizing sensors is performed using the Fisher information matrix (FIM). Results are compared considering the sensor placement optimization problem. It is noticed that optimized sensors contribute to an improvement in the identification of damages, mainly for complex and large-scale structures. The proposed optimized damage identification process using FIM-FA has the potential to be extended to a wide range of SHM applications in complex structures. Hence, traditional NDIs have many shortcomings due to the complexity of large-scale structures as well as modern design structures and may not be practicable if the structure has restricted access. Accordingly, an enhanced damage identification method is developed in order to better handle measurement data to find structural changes (or damages) in complex aerospace 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.

The aim of this study is to understand the thermal phenomenon of unidirectional Glass Fibre Reinforced Polymer (GFRP) composite under static and fatigue (tensile) loads. This study used a rectangular shaped GFRP composite and consisted of specimens with and without a circular notch. Under static test, the constant displacement rate was applied. Under fatigue test, frequency and amplitude of stress were explored to study the fatigue properties and damage evolution of the specimen. Thermography was used in real-time observation to obtain the temperature profile on the external surface of the specimens. This experimental method showed that the thermal phenomenon gave a good detection of the damage appearance of GFRP material under static loading. Moreover, thermal phenomenon gave a good correlation with the energy dissipation under fatigue loading. Thermal phenomenon successfully determined the high cycle fatigue strength of GFRP composite. This study concluded that thermal phenomenon observed by Infra-Red (IR) camera has successfully demonstrated the damage propagation and the strength behaviour of GFRP composite due to tensile loading in both static and fatigue conditions. The IR camera can then be used to determine the damage evolution and the strength prediction based on the thermal phenomenon on the external surface of the GFRP composite.

In the past decade, we have seen growing interest in SHM based on wireless sensor networks and the reason for this is the poor installation and expenses of maintenance. WSNs allow a deep deployment of measurement points on an existing structure, facilitating exact and fault-tolerant damage identification techniques without installing a fixed wired infrastructure. We provide a hierarchical decentralized SHM system that executes flexibility-based damage identification and localization. Flexibility-based methods exactly identify and localize damage on a wider range of structures than the previous System, by externally correlating data across various sensors. Our hierarchical system organizes nodes into clusters using a novel multilevel search approach that incrementally activates sensors in the damaged regions, allowing much of the network to remain asleep.

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.

FEM modeling method of damage structures for structural damage detection