Damage Identification Research Articles

Achieving wheat seedling freezing injury assessment during the seedling stage using Unmanned Ground Vehicle (UGV) and hyperspectral imaging technology

Freezing injury may cause irreversible damage to wheat (Triticum aestivum L) tissues and can significantly reduce yield and quality. Therefore, quick and non-destructively estimating the degree of frost damage for formulating anti-freezing protection strategies and preventing frost damage is very crucial. In this study, we obtained hyperspectral images of wheat leaves for accurate identification of frost damage. A remote-controlled Unmanned Ground Vehicle (UGV) equipped with an imaging spectral camera was used to capture the hyperspectral images of frost-damaged wheat leaves. We compared the efficiency of two methods (the one without removal of weeds, and the other is to remove the corresponding area of weeds from the hyperspectral image by Deeplab V3+) for estimation of wheat freezing damage degree by using four different algorithms; Support Vector Machine Classification (SVM), Mahalanobis Distance Classification (MaD), Minimum Distance Classification (MiD), and Maximum Likelihood Classification (ML). We found that, Deeplab V3+ can efficiently identify the weeds from hyperspectral images, as the overall accuracy (OA) values of different algorithms were high in images with weeds removal as compared to the values in weeds containing images. Further, applying ML model after weeds removal have high OA (93.26 %) as compared to the other models. Thus, using Deeplab V3+ and ML can be a potential approach to identify the freezing injury in wheat for sustainable agricultural productivity.

Multirate UKF Damage Identification Based on Computer Vision Monitoring of Ship–Bridge Collisions

Multirate UKF Damage Identification Based on Computer Vision Monitoring of Ship–Bridge Collisions

Effect of Non-Uniform Temperature Distribution on the Natural Frequency of Concrete Girder Bridges

Structural dynamic properties like frequencies and mode shapes have been widely adopted for bridge condition assessment and damage identification. One of the main challenges lies in environmental factors, particularly the varying temperature, which significantly influences the bridge’s natural frequencies. In some cases, the effect of temperature changes can be comparable to, or even exceed, the effect caused by damage, rendering the damage identification methods ineffective. This study investigates the influence of the cross-sectional non-uniform temperature distribution, as a result of surrounding environmental factors, on the frequency of concrete girder bridges. A theoretical analysis is conducted to derive the bridge’s frequencies considering the cross-sectional non-uniform temperature distribution. The upper and lower bounds of the frequencies are developed by using only the environmental temperature and the largest temperature gradient defined by codes. More importantly, the damage to the bridge can be detected if the frequencies exceed the bounds for a certain period. This approach is convenient and practical in real applications without embedding thermocouples in the main girder. Numerical simulations, laboratory experimental tests, and field measurements are carried out to validate the derived frequency considering the cross-sectional non-uniform temperature distribution as well as the upper and lower bounds of the frequency. In particular, the results of the Z24 bridge show that when the bridge is damaged, the measured frequency exceeds the bound continuously, verifying the capability of the developed upper and lower bounds to evaluate bridge conditions.

Impact of Vitamin C and Hydroxyurea on the Reproductive System Health in Male Rats

Hydroxyurea (HU) is a medication used to treat various diseases and is also being studied for its effects on spermatogenesis. Vitamin C (VC), an antioxidant, has been shown to protect sperm cells from oxidative stress, potentially improving fertility and sperm quality. In a study involving twenty-four adult male albino rats divided into four groups, Group 1 served as the control, Group 2 received 5 mg of vitamin C, Group 3 received 100 mg of hydroxyurea per body weight, and Group 4 received a combination of vitamin C and hydroxyurea to assess essential functions of the reproductive system, including hormone levels, antioxidant markers, oxidative stress indicators, histopathology, and identification of DNA damage. The study found that hydroxyurea significantly reduced testicular weight, SOD, CAT, and GSH while increasing FSH and MDA levels and causing abnormalities in sperm morphology. Hydroxyurea also caused apparent alterations in the histological structure of the testes and comet parameters. Rats treated with vitamin C showed a significant increase in absolute and relative epididymis weight compared to the control group. Moreover, vitamin C intervention reversed the adverse effects observed in rats fed hydroxyurea, indicating that low doses of vitamin C can protect against testicular damage.

Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties

In the field of structural damage detection through vibration measurements, most existing methods demand extensive data collection, including vibration readings at multiple levels, strain data, temperature measurements, and numerous vibration modes. These requirements result in high costs and complex instrumentation processes. Additionally, many approaches fail to account for model uncertainties, leading to significant discrepancies between the actual structure and its numerical reference model, thus compromising the accuracy of damage identification. This study introduces an innovative computational method aimed at minimizing data requirements, reducing instrumentation costs, and functioning with fewer vibration modes. By utilizing information from a single vibration sensor and at least three vibration modes, the method avoids the need for higher-mode excitation, which typically demands specialized equipment. The approach also incorporates model uncertainties related to geometry and mass distribution, improving the accuracy of damage detection. The computational method was validated on a steel frame structure under various damage conditions, categorized as single or multiple damage. The results indicate up to 100% accuracy in locating damage and up to 80% accuracy in estimating its severity. These findings demonstrate the method’s potential for detecting structural damage with limited data and at a significantly lower cost compared to conventional techniques.

Lightweight and conformal acousto‐ultrasonic sensing network for multi‐scale structural health monitoring: A review

AbstractStructural health monitoring (SHM) has been increasingly investigated for decades. Different physical principles have been developed for damage identification, such as electronics, mechanics, magnetics, etc., with different coverage (i.e., global, large‐area, and local monitoring) and sensitivity. Mechanical acousto‐ultrasonic‐based methods have formed a big family in SHM technologies. Multiple wave/resonance modes have been utilized for versatile SHM tasks. The permanently integrated sensing networks play a significant role in achieving a cost‐effective and reliable SHM system, with major concerns including weight increase for large‐scale deployment and conformity for complex geometry structures. In this review, typical acousto‐ultrasonic sensors made of different material systems are discussed, along with advantages and limitations. Moreover, advanced network installation methods have been introduced, including surface‐mounting with pre‐integrated networks on substrates and in situ printing, and embedding with composite layup and metal additive manufacturing. Sensor versatility and usage in multi‐scale SHM techniques are then highlighted. Different wave/resonance modes are transmitted and received with corresponding elements and network designs. In conclusion, this systematic review mainly covers a collection of acousto‐ultrasonic sensors, two modalities of network installation, and their employment with various SHM methods, hopefully providing a useful guide to building lightweight and conformal networks with passive or active‐passive sensors, and developing complete and reliable SHM strategies by integrating different damage identification methods on multiple scales.

Wireless vibration testing and bridge deck damage identification using underneath maintenance walkway

Bridges will inevitably experience structural damage during long-term operation, which will decrease their structural load bearing capacity and durability. The vibration characteristics of bridge structures can be used as evaluation indicators for accurately diagnosing their deterioration. This study aimed at developing a vibration testing method for identifying bridge deck damage that does not affect traffic and is easy to implement. The vibration tests on the in-service bridge revealed it was mainly subjected to vertical vibrations, with the accelerations in the vertical direction about twice those in the horizontal direction. Based on fast Fourier transform analysis, the dominant vertical vibration frequencies were 2.34 Hz, 4.10 Hz, 10.30 Hz, 14.45 Hz and 18.75 Hz. A series of 0.1–0.3 Hz bandpass filters were used with those frequencies at the centers for modal analysis. The experimental results were then compared to the finite element analysis results. This study proposed a vibration-based damage identification method based on the differences between the bridge deck and both main steel girders. The study confirmed the convenience and effectiveness of using underneath maintenance walkways for wireless vibration testing.

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

Robustness of wavelet features for damage detection with neural network and digital twin

Recent studies demonstrate that the wavelet energy–based features are highly sensitive to local damages, and accurate damage identification could be achieved by integrating such features with neural networks. However, the robustness of the features in practical applications and the feasibility of generating damage information with a digital twin to facilitate training of neural networks have not been systematically investigated. This paper aims to provide new insight into the practicality of using wavelet energy–based features for accurate damage detection of structures. A systematic investigation into the tolerance of the normalised wavelet packet node energy features with neural network (NWPNE-NN) approach against measurement noises, excitations uncertainties and limited frequency range in the measured responses is carried out, with consideration of data fusion from multiple measurement points. The feasibility of a digital twin to simulate damage scenarios and generate training data for neural networks is explored, and the use of relative WPNE as a new feature is proposed. The results show that the NWPNE-NN approach is capable of detecting and quantifying the main damage in a structure. The neural networks trained by data generated from a well-calibrated digital twin is capable of identifying damage in the actual structure.

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.

In-situ acoustic emission based technique for damage detection and identification and failure mode prediction in scarf repaired composite structures

One of the great challenges facing adhesively bonded repair procedures in the industry is the absence of a reliable structural health monitoring technique to ensure that the repaired region is intact during service. To this end, this paper proposes a novel in-situ acoustic emission-based methodology to detect and identify damages as well as to predict failure modes at early stages. In this essence, two tapered-scarf repaired plates are produced with different patch materials. The first patch consists of neat carbon fiber prepregs whereas in the second thermally exfoliated graphene oxide integrated carbon fiber prepregs are utilized. Testing the constituents of the repair system individually while monitoring their acoustic activity makes it possible to separate each damage type precisely. Hence, when the specimens of the repaired panel are tested, very detailed information is revealed regarding the current structural status of the specimen and probable failure scenarios. The effectiveness and robustness of the proposed methodology in detecting and identifying damages of varying severity in addition to predicting the failure scenario are verified in the composite panels repaired with pristine and graphene integrated patches.

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.

Damage identification method for jacket platform based on dual-channel model

To address the challenges posed by noisy vibration signals and underutilized time-series data in jacket platforms, this paper proposes a dual-channel damage detection method that integrates Temporal Convolutional Networks (TCN) and Gated Recurrent Units (GRU) in parallel. A multi-head attention mechanism (MA) is employed to reassign feature weights, improving detection accuracy. The optimized features are fused using the Concatenate function for the final output. Two experimental scenarios—isolated noise and ocean noise—were designed to evaluate the method. The results demonstrate that the combination of TCN, GRU and MA effectively detects damage in offshore platforms, surpassing other deep learning models. Although the method shows strong potential for real-world applications, further testing in more complex ocean environments is required to address potential limitations in handling highly variable noise patterns.

Smart structural health monitoring (SHM) system for on-board localization of defects in pipes using torsional ultrasonic guided waves

Most reported research for monitoring health of pipelines using ultrasonic guided waves (GW) typically utilize bulky piezoelectric transducer rings and laboratory-grade ultrasonic non-destructive testing (NDT) equipment. Consequently, the translation of these approaches from laboratory settings to field-deployable systems for real-time structural health monitoring (SHM) becomes challenging. In this work, we present an innovative algorithm for damage identification and localization in pipes, implemented on a compact FPGA-based smart GW-SHM system. The custom-designed board, featuring a Xilinx Artix-7 FPGA and front-end electronics, is capable of actuating the PZT thickness shear mode transducers, data acquisition and recording from PZT sensors and generating a damage index (DI) map for localizing the damage on the structure. The algorithm is a variation of the common source method adapted for cylindrical geometry. The utility of the algorithm is demonstrated for detection and localization of defects such as notch and mass loading on a steel pipe, through extensive finite element (FE) method simulations. Experimental results obtained using a C-clamp for applying mass loading on the pipe show good agreement with the FE simulations. The localization error values for experimental data analysed using C code on a processor implemented on the FPGA are consistent with algorithm results generated on a computer running Python code. The system presented in this study is suitable for a wide range of GW-SHM applications, especially in cost-sensitive scenarios that benefit from on-node signal processing over cloud-based solutions.

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.

Precision detection and identification method for apparent damage in timber components of historic buildings based on portable LiDAR equipment

For the purpose of preservation and restoration of historic timber buildings, it is critical that damage to timber components must be detected and identified accurately since proper detection and identification lay the foundation for scientific utilization and preservation of cultural heritage. Portable smart devices with integrated LiDAR sensor technology have significantly advanced, enabling them to be major pedestals for damage detection. An iPhone Pro based technique is therefore suggested for the precise detection and diagnosis of apparent damage to the timber components. The study consists of three major procedures. First, an apparent data collection of the timber components was conducted. A high-resolution triangular mesh model was utilized for precision detection and identification. Point cloud and texture data were the main constituents of this data collection. Second, a technique for image processing and apparent damage analysis was proposed for crack detection in historic timber buildings. This approach can be used to identify, recognize,and assess damage to timber components such as cracks, rotting, and knots. Finally, actual measurements of the timber components in Datong, Shanxi Province, at the Great Temple Corner, proved the effectiveness of the iPhone 12 Pro as a substitute approach to the conventional inspection equipment. The findings indicate that the iPhone 12 Pro, which has a built-in LiDAR sensor, performs exceptionally well in terms of effectiveness, accuracy, and graphical interpretation when identifying apparent damage to historic timber components. Moreover, it has the potential to substitute traditional measurement, 3D scanning, and other detection and identification techniques. Even though damage detection and identification of historical timber buildings has reached a mature stage in terms of portable, refined, and digital transformation, such technique can serve as a compatible addition as a safe, affordable, and paradigm shifting technical solution for detection and identification of historic building components.

Strain concentration applied on crack inspection based on marker-based and homography matrix algorithm

ABSTRACT The detection and identification of ship damages have become a significant area of research. This study proposes an image-based structural crack monitoring method that marks the crack locations in the form of strain concentrations by analyzing the overall strain of the structure. Most existing studies focus on using fixed cameras for structural crack monitoring. However, during the actual operation of structures, continuous monitoring around these structures is challenging. To overcome this limitation, this paper implements the transformation from non-fixed camera images to fixed images using a marker-based approach and homography matrix algorithm. In laboratory-scale structural experiments, the algorithm is capable of locating structural crack positions by observing the behavior of strain concentration. Additionally, this study explores the application of markers in structures and verifies the capability of marker attachment methods in crack inspection.

Dynamic Response Characteristics and Pile Damage Identification of High-Piled Wharves under Dynamic Loading

In port dock engineering, high-piled wharves represent one of the primary structural forms. Damage to the foundation piles is a common issue, influenced by external loads such as impact forces during vessel berthing, slope deformations, and operational loads. This study focuses on the Jungong Road Wharf in Shanghai, utilizing FLAC 3D version 6.0 to conduct dynamic calculations under ship impact loading. The dynamic responses of the structure were analyzed, and various internal forces were extracted during the impact event. By combining concrete cracking failure criteria and fatigue damage theories, the effects of ship collisions on the cracking damage of high-piled wharf structures under different scenarios were assessed. Additionally, the applicability of modal flexibility in high-piled wharf scenarios was evaluated through finite element simulations. The results indicate that the dynamic amplification factor caused by dynamic loading is approximately 1.5, underscoring the necessity of considering this effect in the design and impact analysis of high-piled wharves. The impact loading significantly influences the bending moments of the piles, with inclined piles showing the greatest sensitivity. When a designed ship model collides with the high-piled wharf structure at a speed of 0.2 m/s, the tensile stress in the inclined piles reaches 87% of the ultimate tensile strength of the reinforcement. The impact loading has a relatively minor effect on the axial forces of the piles, a limited influence on the bending moments of the beams, but a considerable impact on the axial forces of the beams. Berthing by oversized vessels and unexpected incidents can lead to more severe damage to high-piled wharf structures. In the finite element simulations, modal flexibility effectively identified the locations of damage, with greater changes in modal flexibility correlating with increased damage severity.

Lamb Wave Probabilistic Damage Identification Based on the Exchanging-Element Time-Reversal Method.

The commonly used baseline-free Lamb wave damage identification methods often require a large amount of sensor data to eliminate the dependence on baseline signals. To improve the efficiency of damage localization, this paper proposes a new Lamb wave damage location method, namely the probabilistic exchanging-element time-reversal method (PEX-TRM), which is based on the exchanging-element time-reversal method (EX-TRM) and the probabilistic damage identification method. In this method, the influence of the damage wave packet migration on the correlation coefficient between the reconstructed signals of each sensing path and the initial excitation signal is analyzed, and the structure is divided into multiple regional units corresponding to the damage to locate damage. In addition, the influence of the number of sensing paths on the location accuracy is also analyzed. A method of damage probability imaging based on structural symmetry is proposed to enhance location accuracy in the case of sparse sensing paths. The experimental and simulation results verify that the method can achieve damage location with fewer excitation times. Moreover, this method can avoid the problem that the damage wave packet is difficult to extract, improve the efficiency of damage location, and promote the engineering application of the Lamb wave damage location method.

A deep learning-based spatial gradient reconstruction method for efficient damage identification in composite with high-sparsity Lamb wavefield

The structural integrity and safety of carbon fiber reinforced plastics (CFRP) are vulnerable to delamination, which is often imperceptible to the naked eye. Although the Scanning Laser Doppler Vibrometer (SLDV) has shown promise in damage quantification of CFRP, its time-consuming measurement process limits its application in engineering scenarios. To address this, we introduce a novel damage index, the spatial gradient, which captures the interaction between delamination and the wavefield. We have also developed a neural network capable of reconstructing the spatial gradient directly from high-sparsity Lamb wavefield data obtained at an extremely low spatial sampling rate, thereby significantly reducing measurement time. To enhance the network’s capability to detect wavefield anomalies, we employ the cross-attention technique, allowing for the direct injection of shallow features representing local wavefield distortions caused by damage into the decoder. Additionally, we integrate multiple reconstruction layers to guide the wavefield reconstruction process, ensuring meaningful information is captured at each stage. Our method achieves substantial improvements in reconstruction accuracy, increasing from 70 % to 92 % in single-damage scenario and from 14 % to 72 % in multi-damage scenario compared to the previous state-of-the-art techniques. By using the reconstructed spatial gradient field for damage imaging through spatial covariance analysis, our approach demonstrates its feasibility and generalizability across various damage locations. This suggests its potential as a reliable solution for fast and accurate damage characterization, reducing the measurement burden and enhancing practical applicability.