Damage Detection Method Research Articles

Damage Detection of Gantry Crane with a Moving Mass Using Artificial Neural Network

Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced using random changes in the stiffness of different parts of the structure. Contrary to the assumption of inherent reliability, undetected defects in crucial structural elements can lead to catastrophic failures. Then, the vibration equations of healthy and damaged models are analyzed to find the displacement, velocity, and acceleration of the different crane parts. The learning vector quantization neural network is used to train and detect the defects. The output is the location of the damage and the damage severity. Noisy data are then used to evaluate the network performance robustness. This research also addresses the limitations of traditional inspection methods, providing early detection and classification of defects in gantry cranes. The study’s relevance lies in the need for a comprehensive and efficient damage detection method, especially for components not easily accessible during routine inspections.

A Pattern Recognition Method for Filter Bags in Bag Dust Collectors Based on Φ-Optical Time-Domain Reflectometry

The use of phase-sensitive optical time-domain reflectometry (Φ-OTDR)-distributed fiber vibration sensors to detect and identify damaged bags in bag dust collectors has the potential to overcome the inadequacy of traditional damaged bag detection methods. In our previous study, we verified the feasibility of applying this technique in the field of damaged bag detection in bag filters. However, many problems still occur in engineering applications when using this technology to detect and identify damaged filter bags in pulse-jet dust-cleaning bag dust collectors. Further studies are needed to characterize the fiber vibration signals inside different types of rectangular damaged filter bags. A filter bag damage identification and detection method based on empirical mode decomposition (EMD) and a backpropagation (BP) neural network is proposed. The signal feature differences between intact filter bags and damaged filter bags with different rectangular hole sizes and positions are comparatively analyzed, and optimal feature difference parameters are proposed. Support vector machine (SVM) and a BP neural network are used to recognize different types of filter bag signals, and the comparison results show that the BP neural network algorithm is better at recognizing different types of filter bags, obtaining the highest recognition rate of 97.3%.

Vibration-Based SHM in the Synthetic Mooring Lines of the Semisubmersible OO-Star Wind Floater under Varying Environmental and Operational Conditions.

As the industry transitions toward Floating Offshore Wind Turbines (FOWT) in greater depths, conventional chain mooring lines become impractical, prompting the adoption of synthetic fiber ropes. Despite their advantages, these mooring lines present challenges in inspection due to their exterior jacket, which prevents visual assessment. The current study focuses on vibration-based Structural Health Monitoring (SHM) in FOWT synthetic mooring lines under uncertainty arising from varying Environmental and Operational Conditions (EOCs). Six damage detection methods are assessed, utilizing either multiple models or a single functional model. The methods are based on Vector Autoregressive (VAR) or Transmittance Function Autoregressive with exogenous input (TF-ARX) models. All methods are evaluated through a Monte Carlo study involving 1100 simulations, utilizing acceleration signals generated from a finite element model of the OO-Star Wind Floater Semi 10 MW wind turbine. With signals from only two measuring positions, the methods demonstrate excellent results, detecting the stiffness reduction of a mooring line at levels 10% through 50%. The methods are also tested for healthy cases, with those utilizing TF-ARX models achieving zero false alarms, even for EOCs not encountered in the training data.

A novel data-driven sensor placement optimization method for unsupervised damage detection using noise-assisted neural networks with attention mechanism

Optimization of sensor placement (OSP) is one of the important steps in structural health monitoring to reduce the instrumentation cost, and improve damage detection. Although modal analysis is an optional intermediate process in terms of damage detection, conventional OSP methods for damage detection are mostly dependent on mode shapes. In this case, the sensor placement and damage detection are highly relying on the accuracy of modal analysis, and the results may not be adapted to different type of excitations. In this article, a novel noise-assisted neural network with attention mechanism is proposed based on the above challenges. This method which can be used to optimize sensor placement in an unsupervised and data-driven manner, is verified using a dataset simulated from the ASCE benchmark and an experimental dataset obtained from shake table tests. The results from the simulated dataset show that the percentage of the sensors that could be removed are higher than the conventional effective independence (EFI) method, with a highest of 62.5% for cases with low noise levels. In the meantime, the occurrence and the level of damage can still be well detected with a reduced number of sensors. Most importantly, in the proposed approach the optimal sensor placement configurations can be determined adaptively to account for different forms of excitations and noise levels, which is impossible for conventional model-driven methods. The results obtained from the experimental dataset show that the proposed method is also effective for real-world applications. As a result, the proposed data-driven OSP method skips the conventional model analysis process and directly focuses on the sensor arrangement that enables accurate detection of damage. It also has the potential for application in related fields, such as the monitoring of aerospace and mechanical infrastructures.

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis.

In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of bolts. To solve the structural nonlinear damage diagnosis problem more effectively, this study combines the autoregressive (AR) model and amplitude-aware permutation entropy (AAPE) to propose a data-driven damage detection method. First, an AR model is built for the acceleration data from each structure sensor in the baseline state, including determining the model order using a modified iterative method based on the Bayesian information criterion (BIC) and calculating the model coefficients. Subsequently, in the testing phase, the residuals of the AR model are extracted as damage-sensitive features (DSFs), and the AAPE is calculated as a damage classifier to diagnose the nonlinear damage. Numerical simulation of a six-story building model and experimental data from a three-story frame structure at the Los Alamos Laboratory are utilized to illustrate the effectiveness of the proposed methodology. In addition, to demonstrate the advantages of the present method, we analyzed AAPE in comparison with other advanced univariate damage classifiers. The numerical and experimental results demonstrate the proposed method's advantages in detecting and localizing minor damage. Moreover, this method is applicable to distributed sensor monitoring systems.

Abstract B033: Direct and sensitive in-situ detection of DNA breaks by STRIDE and its potential as a new biomarker

Abstract DNA damage is usually detected using indirect methods which require active signaling and triggered DNA repair mechanisms. These methods, although relatively sensitive, are prone to false (positive or negative) results. Methods for direct DNA damage detection, on the other hand, suffer from low sensitivity or require DNA isolation, what can result in misleading artefactual readouts of the analysis. Here we present a method called STRIDE (SensiTive Recognition of Individual DNA Ends), which allows for specific, very sensitive in-situ detection of DNA breaks. This fluorescence-based method, in its two variants (dSTRIDE and sSTRIDE), allows selective labelling of double-or single-strand breaks, respectively. STRIDE enables highly sensitive and specific detection and quantification of single and double-strand DNA breaks in cultured cells and FFPE human tissues, both PBMCs as well as solid tumors. STRIDE is superior to classic methods used to measure DNA damage, as it combines a direct detection that is independent of DNA repair mechanisms with an unprecedented sensitivity. With its versatility it can be used in any type of biological material, including liquid biopsies as well as different types of tumors. STRIDE has the potential to become a very useful biomarker reporting about the efficiency of therapies based on DNA damage induction, such as DNA repair inhibitors or radiotherapy. Citation Format: Kamil Solarczyk, Brian Patterson. Direct and sensitive in-situ detection of DNA breaks by STRIDE and its potential as a new biomarker [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: DNA Damage Repair: From Basic Science to Future Clinical Application; 2024 Jan 9-11; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2024;84(1 Suppl):Abstract nr B033.

An Investigation into the Application of Acceleration Responses' Trendline for Bridge Damage Detection Using Quadratic Regression.

It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky-Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method.

Multiscale Feature Fusion Convolutional Neural Network for Surface Damage Detection in Retired Steel Shafts

Abstract The detection of surface damage is an important part of the process before remanufacturing a retired steel shaft (RSS). Traditional damage detection is mainly done manually, which is time-consuming and error-prone. In recent years, computer vision methods have been introduced into the community of surface damage detection. However, some advanced typical object detection methods perform poorly in the detection of surface damage on RSS due to the complex surface background and rich diversity of damage patterns and scales. To address these issues, we propose a Faster R-CNN–based surface damage detection method for RSS. To improve the adaptability of the network, we endow it with a feature pyramid network (FPN) as well as adaptable multiscale information modifications to the region proposal network (RPN). In this paper, a detailed study of an FPN-based feature extraction network and the multiscale object detection network is conducted. Experimental results show that our method improves the mean average precision (mAP) score by 8.9% compared with the original Faster R-CNN for surface damage detection of RSS, and the average detection accuracy for small objects is improved by 18.2%. Compared with the current advanced object detection methods, our method is more advantageous for the detection of multiscale objects.

Partial‐Model‐Based Damage Identification of Long‐Span Steel Truss Bridge Based on Stiffness Separation Method

Damage detection in bridge structures has always been challenging, particularly for long‐span bridges with complex structural forms. In this study, a partial‐model‐based damage detection method was proposed for the damage identification of long‐span steel truss bridges. The proposed method employs partial models to estimate the parameters using the stiffness separation method. This approach obviates the need to construct complete stiffness information for the structure. In contrast, it depends solely on the arrangement of the structural members and material information in the recognized area. This technique can effectively circumvent the construction of an overall structural model and reduce the complexity of damage identification in large structures. A full‐scale long‐span steel truss bridge in service was used to illustrate the feasibility of the proposed method. The locations of the three partial models were considered in the model analysis, and the parameter estimation efficiency of the Nelder–Mead simplex and quasi‐Newton algorithms were compared.

Unsupervised Vision‐Based Structural Anomaly Detection and Localization with Reverse Knowledge Distillation

Most of vision‐based methods for structural damage detection rely on supervised learning, requiring a substantial number of labeled images for model training, which is labor‐intensive and time‐consuming. To address these challenges, this study introduces a vision‐based structural anomaly detection and localization approach using unsupervised learning and reverse knowledge distillation. The proposed model incorporates a teacher model, a student model, and a trainable one‐class bottleneck embedding module. The asymmetrical architecture of the teacher and student models forms an encoder‐decoder structure for parameter transfer and feature extraction. The student network receives a specific embedding from the teacher network as input and target, facilitating the recovery of multiscale information from the teacher. Training images only contain the undamaged structures, and the teacher model, a pretrained model, instructs the student model to remember their undamaged features to detect and localize damages in unseen testing images. Through experiments, including a comparison among five candidate backbones for pretrained teacher models based on the residual network and testing across various structural damage types, the optimal model is identified, demonstrating good performance in both anomaly detection and localization. Furthermore, the model’s generalization performance is thoroughly validated, confirming its efficacy across diverse scenarios.

Design of Multi-objective Optimization Clustering Method for Hypervelocity Impact Complex Damage Detection

Design of Multi-objective Optimization Clustering Method for Hypervelocity Impact Complex Damage Detection

GES: A New Building Damage Data Augmentation and Detection Method Based on Extremely Imbalanced Data and Unique Spatial Distribution of Satellite Images

GES: A New Building Damage Data Augmentation and Detection Method Based on Extremely Imbalanced Data and Unique Spatial Distribution of Satellite Images

Fatigue damage detection method of nano ceramic coating rail based on acoustic signal acquisition

Fatigue damage detection method of nano ceramic coating rail based on acoustic signal acquisition

Study on the structural damage detection method using the flexibility matrix

Study on the structural damage detection method using the flexibility matrix

Fatigue damage detection method of nano ceramic coating rail based on acoustic signal acquisition

Fatigue damage detection method of nano ceramic coating rail based on acoustic signal acquisition

UAV-deployed deep learning network for real-time multi-class damage detection using model quantization techniques

Real-time damage detection algorithms deployed on Unmanned Aerial Vehicles (UAVs) can support flight control in real time, enabling the capture of higher quality inspection data. However, three challenges have hindered their wider application: 1) Existing anchor-based damage detectors cannot generalize well to real-world scenarios and degrade the detection speed; 2) Prior studies exhibit a low detection accuracy; 3) No previous study considers the energy consumption issue of the damage detector, limiting the UAVs' flight time. To meet these challenges, this paper presented the YOLOv6s-GRE-quantized method, which is an energy-efficient anchor free and real-time damage detection method built on top of the YOLOv6s algorithm. Firstly, the YOLOv6s-GRE method was presented, where a generalized feature pyramid network (GFPN), a reparameterization efficient layer aggregation network (RepELAN) and an efficient detection head were introduced into the YOLOv6s. Comparison experiments showed that the YOLOv6s-GRE method, in contrast to YOLOv6s, advanced 2.3 percentage points in the metric of mAP50, while maintaining comparable detection speed and without requiring an increase in model size. The YOLOv6s-GRE model was then reconstructed by the RepOptimizer (RepOpt) to equivalently transform the YOLOv6s-GRE into a quantization-friendly model for addressing the quantization difficulty of the reparameterization model. Finally, the YOLOv6s-GRE model with RepOpt was quantized by the partial quantization-aware training technique, expediting the detection speed by 83.5% and saving energy by 79.7% while still maintaining a comparable level of detection accuracy. Implementing of this proposed method can significantly boost bridge inspection productivity.

Damage inference and residual capacity assessment for an E-Defense 2018 ten-story RC test structure

This paper presents an enhanced vision-based, post-earthquake performance assessment framework for RC building structures. This framework incorporates a damage inference method that can estimate the stiffness and strength reduction factors of components in stories without damage inspection based on the inter-story drift. The framework was validated by the application to the full-scale shaking table test of an E-Defense 2018 ten-story RC structure. The vision-based damage detection method effectively detected multicategory seismic damage on the component surfaces, and the damage states estimated by the vision-based method were consistent with the results estimated by the measured plastic hinge rotation. The proposed inter-story drift-based damage inference method reasonably estimated the reduction factors of components in the stories without damage photos. The numerical model updated by the reduction factors of damaged components provided accurate estimates of the fundamental vibrational frequencies after different levels of seismic shaking, and the drift-based inference method significantly improved the results compared with the trivial linear interpolation method. The residual capacity curves provided by pushover analysis of the updated model using the reduction factors as per FEMA 306 & Chiu et al. reasonably captured the hysteretic responses of the structure.

Structural Damage Detection Based on Static and Dynamic Flexibility: A Review and Comparative Study

Material damage in structures must be detected in a timely manner to prevent engineering accidents. Damage detection based on structural flexibility has attracted widespread attention in recent years due to its simplicity and practicality. This article provides a detailed overview of damage detection methods based on structural flexibility. Depending on the calculation method and data used, flexibility-based methods can be divided into the following categories: flexibility difference, flexibility derivative index, flexibility sensitivity, flexibility decomposition, static flexibility, and combinations of flexibility with other methods. The basic principles and main calculation formulas of various flexibility methods are explained, and their advantages and disadvantages are analyzed. For the method using flexibility difference, the advantage is that the calculation is very simple and does not require the construction of a finite element model of the structure. The disadvantage is that it requires the measurement of modal data of the intact structure, and this method cannot quantitatively assess the degree of damage. For the method using the flexibility derivative index, the advantage is that it only requires the modal data of the damaged structure to locate the damage, but this method is particularly sensitive to noise in the data and is prone to misjudgment. For methods based on flexibility sensitivity and flexibility decomposition, the advantage is that they can simultaneously obtain the location and degree of damage in the structure, but the disadvantage is that they require the establishment of accurate finite element models in advance. Static flexibility methods can compensate for the shortcomings of dynamic flexibility methods, but they usually affect the normal use of the structure during static testing. Combining flexibility-based methods with advanced intelligent algorithms and other methods can further improve their accuracy and efficiency in identifying structural damage. Finally, this article discusses the challenges that have not yet been solved among damage detection methods based on structural flexibility.

Development of a procedure for model selection in machine learning-based methods for structural damage detection

Output-only methods based on machine/deep-learning algorithms are highly practical approaches for timely detecting potential damages in civil structures as they directly employ measured vibration signals but do notrequire exact knowledge of input loading nor the service interruption for manual inspection. However, there isno one-size-fits-all model that is optimal for all problems in different perspectives; hence, it is necessary todiscover the advantages as well as drawbacks of different models, then leverage these understandings to selectthe most appropriate model for specific structures in reality. Therefore, this study develops a framework thatfacilitate the model selection by extensively comparing various machine learning-based methods ranging fromrelatively simple ones such as Na¨ıve Bayes to complex ones such as the extreme boosting tree-based ensemblemodel. The framework can provide comparison results include various aspects such as model complexity, training time, detection accuracy, and inference time.

Applicability of available NDT methods for damage detection in concrete elements reinforced or strengthened with FRP

The application of Fiber Reinforced Polymer (FRP) materials in concrete structures has been rising due to their several advantages, including lightweight, high tensile strength, ease of installation, and corrosion resistance. They have been mostly implemented for strengthening and repairing existing structures in the form of an externally bonded system, i.e., sheet, jacket, near surface mounted. Furthermore, they have been recently utilized as internal reinforcement of concrete elements in the form of strands, bars, tendons, etc. Although higher durability and performance are associated with the FRP material in some aspects compared to steel, concerns remain regarding damages and defects in this material, many of which are related to their unique features. Importantly, debonding of FRP materials from a concrete surface or within a concrete element has always been an issue resulting in the premature failure of the structure. To this end, concrete elements strengthened or reinforced with FRP materials has to be inspected periodically to detect potential issues and hence prevent any premature failures. This study first determines all possible or potential damages and anomalies attributed to FRP reinforced/strengthened concrete (FRP-RSC) elements. It then investigates Non-Destructive Testing (NDT) methods that can be applicable to the inspection of FRP-RSC elements from a literature survey of past studies, applications, and research projects. Furthermore, this study evaluates the ability of two of the most commonly used NDT methods, Ground Penetrating Radar (GPR) and Phased Array Ultrasonic (PAU), in detecting FRP bars/strands embedded in concrete elements. GPR and PAU tests were performed on two slab specimens reinforced with GFRP (Glass-FRP) bars, the most commonly used FRP bar, with variations in their depth, size and configuration, and a slab specimen with different types of available FRP reinforcements. The results of this study propose the most applicable methods for detecting FRP and their damage/defects in FRP-RSC elements. This study further investigates the feasibility of two new methods for improving the detectability of embedded FRP bars. By providing the inspection community with more clarity in the application of NDT to FRP, this study offers means for verifying the performance and, therefore, help the proliferation of FRP materials in concrete structures.