Structural Damage Identification Research Articles

Simultaneous identification of structural joint and member damage in semi-rigid frames using expanded FRF data

The simultaneous identification of damage in both structural joints and members is indeed a crucial and challenging task due to the complex interactions between these components and the varied forms of damage that can affect overall structural integrity. In this regard, the present research introduces an efficient method for this simultaneous identification in semi-rigid frames using a model updating procedure based on expanded FRF data. Initially, a high-fidelity model of the monitored frame structures is created, simulating all beam-column joints as semi-rigid connections with zero-length rotational springs. The model updating process is then framed as an optimization scheme, where both structural joint and stiffness parameters are continuously adjusted. To deal with incomplete measured FRF data, an iterative order reduction method is used to expand the data. To enhance the efficiency and effectiveness of the optimization process, we adopt a novel meta-heuristic algorithm, Chaos Game Optimization (CGO) algorithm. We validate the efficiency and accuracy of the damage identification procedure through two numerical examples of semi-rigid frame structures and compare its performance with several newly developed algorithms. With the proven capability, the proposed procedure offers a promising avenue for accurately localizing and quantifying damage in joints and members using spatially incomplete FRF data contaminated by noise.

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

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.

Post‐earthquake structural damage identification and safety evaluation using point clouds

AbstractEfficient and accurate post‐earthquake damage assessment of building structures is critical for ensuring the human safety and structural integrity of affected buildings. However, manual inspections and traditional visual damage identification methods are often constrained by the inaccessibility of hard‐to‐reach regions and the subjectivity of human inspectors. To overcome these issues, this paper proposes a novel approach for rapid and precise post‐earthquake damage identification and evaluation using unmanned aerial vehicle (UAV) and point cloud techniques, significantly reducing the time, labor, and errors associated with traditional methods. A comprehensive testing on full‐scale reinforced concrete shear walls was conducted to validate the precision and feasibility of this method. The point cloud models of the shear wall were generated leveraging UAV imagery and laser scanning technology with millimeter‐scale accuracy. The proposed algorithm effectively segmented each target plane of the shear wall, achieving a relatively satisfactory overall Intersection over Union of 99.25%. The relative errors of deformation between the algorithm's identification and gauges measurements were within 5%. This study successfully segmented and quantified structural surface damage, including cracks and spalling. Finally, the structural safety of the shear wall was evaluated according to ATC‐20 guidelines, using indicators such as inclination, story drift ratio, crack width, and damage area. Furthermore, proposed method was also verified in real cases.

Research and Evaluation of a New Structural Damage Identification Method Based on a Refined Genetic Algorithm

In order to solve the problem of structural damage location and degree identification, the weighted mean of vectors algorithm (INFO), a high-performance optimization algorithm, was first introduced to identify structural damage. By comparison with the refined genetic algorithm (RGA), the accuracy and advantages of INFO are analyzed and evaluated. An objective function is constructed by combining the dynamic response transfer ratio without modal analysis. The INFO and RGA algorithms are used to optimize the objective function for damage identification. The simulation results verify the effectiveness of the three damage identification methods. The results show that the identification effect of the INFO algorithm can reach nearly 100% without noise influence, and the anti-noise ability is the strongest. Among the three algorithms, the damage identification accuracy of the INFO algorithm is the highest, followed by the RGA algorithm and the GA algorithm.

Bayesian structural damage identification using fraction function regularisation model based on natural frequency and mode shape curvature

ABSTRACT The existing models based on Bayesian inference and L 1 regularisation provide a feasible scheme to the uncertainty problem in structural damage identification. However, L 1 regularisation cannot accurately describe the sparsity of damage due to excessive punishment of large damage parameters, and the mode shape data used by such models are insensitive to the damage. This compromises the accuracy of damage identification. Compared with L 1 regularisation, the fraction function regularisation avoids excessive punishment for larger elements in the damage parameters, resulting in a more accurate sparse solution. The mode shape curvature is more sensitive to damage because it can be viewed as the differential form of the mode shape. Inspired by this, a fraction function regularisation model based on natural frequency and mode shape curvature data is proposed in this paper to further improve the accuracy of Bayesian damage identification. In addition, an improved cuckoo search algorithm is proposed to solve the model by introducing a ranking-based mutation strategy. The results obtained from both the numerical investigation and the experimental study consistently indicate that the proposed method can provide accurate and reliable identification results.

Structural Damage Identification of Large-Span Spatial Grid Structures Based on Genetic Algorithm

Structural Damage Identification of Large-Span Spatial Grid Structures Based on Genetic Algorithm

Computer vision-based substructure isolation method for localized damage identification

In recent years, vision-based structural damage identification techniques have garnered significant attention due to their simplicity and cost-effectiveness. Nevertheless, dynamic response-based vision methods face challenges related to the limitations of the field of view (FOV), i.e., the extent of the FOV is often sacrificed in order to ensure sufficient monitoring accuracy, which reduces the amount of data available for structural health monitoring (SHM). This study presents a solution to this problem by employing the substructure isolation method (SIM), which focuses on local vibrations of the structure and enables local damage identification by isolating the area of interest. Additionally, a method combining kernelized correlation filter (KCF) with sub-pixel template matching is used to extract vibration information recorded by visual sensor. This strategy balances both the efficiency and accuracy of displacement extraction. In numerical simulations, the performance of the SIM based on acceleration response and displacement response is compared, demonstrating the potential compatibility of visual sensors with the SIM. Finally, the effectiveness of the proposed vision-based local damage identification method is validated through vibration testing of a shear frame model.

Acoustic Emission and Digital Image Correlation-Based Study for Early Damage Identification in Sandwich Structures

Acoustic Emission and Digital Image Correlation-Based Study for Early Damage Identification in Sandwich Structures

A vine-copulas based multi-sensor fusion structural damage monitoring method and its application in dam engineering

As a civil engineer that is susceptible to the mechanism of slow and continuous deterioration, the dam must detect damage according to the static data of the arranged sensors, which is an important issue in structural health monitoring. Due to the characteristics of huge solid structures, damage with a slight degree and small range usually does not cause significant changes in the monitoring data of a single sensor. Therefore, it is difficult to effectively detect local damage by traditional monitoring methods that establish operating warning values for a single sensor. Currently, there have been some approaches to fuse sensor information that consider correlations between multiple sensors as well as the overall operational behavior of the dam. However, their objectives are focused on improving the accuracy of response variable prediction models without addressing the local damage identification aspect. In this study, we apply the Vine-Copula model for the first time in a multi-sensor information fusion dam damage detection method based on static monitoring data. In this way, it improves the sensitivity of local damage identification for huge solid structures. A prediction model is fitted with the measuring data of each sensor to obtain the residual series. Then the joint probability distribution of the multidimensional residuals is acquired by constructing the Vine-Copula model. Finally, the joint cumulative distribution function value is used as an alarm limit to determine whether the structure has an abnormal damage condition. The effectiveness and accuracy of the proposed method were demonstrated in both a numerical arch dam simulating local damage and an actual rockfill dam engineering with cracks at the crest.

Multiscale Damage Identification Method of Beam-Type Structures Based on Node Curvature

This paper proposes a multiscale damage identification method for beam-type structures based on node curvature. Firstly, based on the assumption that micro-damage has little effect on stress redistribution and the basic relationship between structural bending moment and curvature, combined with the denoising function of wavelet analysis, the linear matrix equation before and after node curvature damage is solved using the singular value decomposition (SVD) method. Then, the theoretical feasibility of this method is verified with laboratory tests of a simply supported beam. Finally, the damage sensitivity and noise resistance of this method are verified using field measurements of a beam bridge. The results show that the nodal curvature serves as an indicator parameter for damage identification in beam-type structures, enabling the precise localization of damage within these structures. When utilizing a multiscale finite element model for analysis, the nodal curvature enhances the ability to identify both the location and severity of damage within small-scale elements. Furthermore, this method can provide a reference for the damage identification and health monitoring of other types of bridges.

An improved generalized flexibility matrix-based damage identification technique for derrick steel structures

An improved generalized flexibility matrix-based method using matrix partitioning technique for structure damage identification is proposed. As an effective damage identification method, the generalized flexibility matrix requires fewer vibration frequencies and corresponding modal shapes compared with the traditional flexibility matrix method. By introducing a new partitioning technique of the matrices involved in the constructive process of the governing equation, the improved method simplifies the process of calculation with a dramatic reduction in computational cost while maintaining accuracy of identification results. In the case of incomplete measured modal data, an improved matrix partitioning technique based on the model expansion method is applied and the damaged indicators can be identified using partial measured modal data. A K-type derrick steel structure in service is considered as a numerical example for inspection the validity and feasibility of the proposed method. Numerical results show better accuracy and efficiency of this method for various damage scenarios, and can be used for damage diagnosis or health monitoring of other large structures.

High-Efficiency Finite Element Model Updating of Bridge Structure Using a Novel Physics-Guided Neural Network

An accurate finite element model (FEM) plays a critical role in the structural damage identification. However, due to the existence of the uncertainties, such as material properties and modeling errors, it always exists some gaps between the analytical FEM and experimental structure. While an artificial neural network (ANN)-based model updating methods have been widely adopted to narrow the gap and obtain a baseline FEM, it still faces inaccurate results and fails to meet the physical law. In this regard, the study proposes a novel physics-based loss function inspired by modal sensitivity analysis and incorporates it into the residual neural network, thereby forming a novel physics-guided neural network (PGNN) method. The mapping relationship between the input of structural responses and output of model updating variables is constrained to retain its physical meaning by guiding the training process instead of pure data association, which aims to improve the accuracy of the ANN-based method and achieve accurate and high-efficiency model updating. An experimental example of a continuous rigid frame bridge is adopted to verify the feasibility of the proposed method. Additionally, other common model updating methods, including moth-flame optimization and regularization method, are used to make a comparison. The noise-robustness of the proposed method is investigated as well. Compared to the existing method, the results illustrate that the proposed PGNN method can achieve better model updating and good noise-robustness under high uncertainties, which means the introduction of the physics-based loss function significantly enhances the parameters updating ability of the neural network. The proposed method exhibits high efficiency and promising potential for large-scale bridge structure model updating.

Structural damage detection for spatial frame structures with semi-rigid joints using multiple set wireless measurements

Damage detection of a complex spatial frame structure with semi-rigid joints is a challenging task. Most existing damage detection methods consider the joint as rigid and the nonuniform cross section of the member is not considered. This assumption results to a large error in the structural damage identification. A novel generic element is proposed for the nonuniform cross-section member with semi-rigid joints at both ends in this study. The finite element model of spatial structures with semi-rigid joints is established using the proposed element. The modal strain energy-based damage index is defined and its sensitivity to the member and joint damage has been investigated using numerical and experimental study. An 8-m long bridge model with the bolted connection at joints is built in the laboratory. Dynamic responses of the bridge under random excitations are monitored using 13 wireless tri-axis accelerometers. The spatial mode shapes of the bridge are extracted using the reference-based stochastic subspace identification from multiple set wireless measurements. The numerical model is validated using experimental results. Dynamic responses of the bridge with different damage scenarios have been simulated using the validated model and the corresponding damage indices are obtained. The results show that the proposed method is reliable and accurate to analyze dynamic behavior of the spatial structure with semi-rigid joints, and structural damage can be identified accurately using the modal strain energy-based damage index.

Lorentz attractor excitation-based structural damage identification using state space curvature reconstruction- enhanced transformer

Abstract Vibration-based structural damage identification has been widely investigated. Different from previous studies that analyze vibrational responses in time and frequency domains, a new Lorentz attractor excitation-based damage identification is becoming a novel strategy with the advantage of capturing the structure’s nonlinear dynamic effects. In this study, Lorentz attractor-based chaotic signals were employed as excitation signals for the structural damage identification of a frame structure. Nonlinear responses were recorded and damages of bolt looseness at different locations were considered. The structural damages could be revealed in the state-space plot of the responses. A state space curvature reconstruction method was introduced to enhance the key features of the nonlinear responses. A small-sample damage identification is performed using a deep learning algorithm – a Transformer with an accuracy of 92.38%. The advantages of the proposed method over conventional deep learning algorithms were validated. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses.

Silicone Rubber-Packaged FBG Sensing Information and SSI-COV-Recognized Modal Parameters Motivated Damage Identification in Pipe Structures

Pipes are the main structures serving as the lifeline for oil and gas transportation. However, they are prone to cracks, holes and other damages due to harsh working environments, which can lead to leakage incidents and result in significant economic losses. Therefore, the development of structural health monitoring systems with advanced online diagnostic methods is of great importance for identifying local damages and assessing the safety state of pipe structures. These efforts can guide rapid repairs and ensure the continuous, efficient and cost-effective transportation of oil and gas resources. To address this problem, this paper proposes the development of a pipe monitoring system based on quasi-distributed fiber Bragg grating (FBG) sensing technology. The SSI-COV method is employed to process the sensor responses and extract the modal parameters of the structure. Based on this foundation, an enhanced damage identification index is proposed, which mitigates the effects of support and excitation positions on damage identification. The pipe structure can be regarded as a continuous super-statical beam, and based on its structural symmetry, a unit structure, specifically a stainless-steel pipe with fixed ends, is regarded as the experimental subject. Impact experiments have been conducted to analyze its behavior in both undamaged and damaged states. The research indicates that by using the proposed modal parameter identification method and the ASMDI damage index, ASMDI exhibits peak values at damage locations of the pipe structure. This allows for the identification of structural damage with high accuracy, fast processing efficiency and strong robustness. The study provides an effective and reliable damage diagnosis method, which can contribute to the refinement and visualization of pipe structural health monitoring systems.

Research on damage identification of wind power tower structure based on deformation and vibration dual parameter analysis

Abstract During the operation of the wind turbine, the force on the wind turbine tower is mainly caused by the vibration source at the top. As a high-rise structure with a large span, traditional vibration mechanics is used to analyze the overall force on the tower body, but the analysis of vibration modes or modes is slightly insufficient. This article adopts the analysis method of elastic wave dynamics to identify damage to the structure of wind power towers based on deformation and vibration dual parameter analysis. This article analyzes the propagation characteristics of elastic waves in tower structures, concrete bases, and foundation parts when damaged. This article develops a remote online monitoring and early warning platform for wind power tower bodies based on the attenuation properties of elastic waves. This article monitors the vibration and inclination of the tower drums of 5 wind turbines (# 13, # 31, # 34, # 49, and # 52). The tower drums have not undergone irreversible tilting or torsional deformation. However, through vibration analysis, it is suggested to check whether there is crushing between the tower drums and the concrete base, as well as whether there are local cracks on the surface of the concrete base. There may be local damage to the foundation ring of tower #31. It is recommended to check for local cracks in the foundation ring. This ensures the safe operation of wind power towers and has certain practical application value.

Damage identification in composite structures using high-speed 3D digital image correlation and wavelet analysis of mode shapes

ABSTRACT In this study, the damage identification procedure combining the advantages of high-speed 3D digital image correlation (DIC) that is used for modal analysis and wavelet-based processing algorithm is presented. The proposed procedure has been successfully validated on sandwich composite structures with internal damage of a core and impact damage, and its performance was demonstrated by detection, localisation, and identification of shapes of damage in tested structures, comparable to the results from scanning laser Doppler vibrometry (SLDV). The effectiveness in damage identification was achieved by the development of processing algorithm of acquired mode shapes, which uses a 2D double-density directional wavelet transform with increased sensitivity with entropic weights allowing to automatically select those wavelet coefficients and mode shapes, which are most sensitive to damage. The procedure presented in this paper demonstrates the performance of 3D DIC for damage identification as a cost-effective alternative to the procedures based on SLDV measurements.

Experimental validation of an efficient strategy for FE model updating and damage identification in tubular structures

ABSTRACT Early identification of damages in tubular structures is crucial for their long-term safety and functionality, as they are essential in various modern life applications. Experimental and numerical modal data may slightly differ due to unknown structural characteristics and uncertainties, which are typically addressed using finite element (FE) model updating procedures. Instead of using the Euler-Bernoulli beam element, this paper utilises the semi-rigidly connected frame element (S-RCFE). By incorporating extra design parameters, such as the end fixity factor of all connections, the S-RCFE offers a unique opportunity to establish a strong agreement between experimental and numerical models through an optimisation-based FE model updating procedure. A well-calibrated FE model represents the actual behaviour of the structure and leads to achieving accurate results in the damage detection step. This paper employs the improved grey wolf optimiser (IGWO) and weIghted meaN oF vectOrs (INFO) to minimise 11 objective functions with adjustable coefficients. The statistical investigations reveal that the IGWO effectively minimised five out of six objective functions, which were defined based on the modified total modal assurance criterion (MTMAC). The rest of the objective functions based on the modal assurance criterion (MAC), natural frequency vector assurance criterion (NFVAC), differences in natural frequencies, and a combination of the MAC and NFVAC could not obtain accurate outcomes for the model updating problem. The statistical comparison indicates that the INFO algorithm is unreliable for the FE model updating despite achieving at least one successful result in ten independent runs. The INFO algorithm and the IGWO algorithm demonstrate comparable performance in damage detection. The analysis also shows that the coefficients of MTMAC, alpha and beta, should be adjusted to 0.65 and 1, respectively, to achieve the most accurate damage detection result.

Structural damage identification using improved dynamic time warping fast algorithm based on largest triangle three buckets dimensionality reduction

Structural damage identification using improved dynamic time warping fast algorithm based on largest triangle three buckets dimensionality reduction

Structural Damage Identification Based on Multi-head Convolutional Neural Network and Improved Beluga Whale Optimization Considering Ambient Temperature

Structural Damage Identification Based on Multi-head Convolutional Neural Network and Improved Beluga Whale Optimization Considering Ambient Temperature