Structural Damage Detection Research Articles

Research on Damage Detection of Dual-Rotor Synchronous Excitation Mine Screen Beams Based on Strain Mode Difference Vibration Mode Analysis

The frame beam structure of the mine screen, subjected to various excitations, is a critical component of mining machinery. Its stress is intricate, the operational environment is severe, and damage can lead to catastrophic failures resulting in machinery destruction and fatalities. Based on the characterization of the vibration response of mine screen frame beams with varying degrees of damage at the same location and with the same degree of damage but at different locations, this paper develops a method of strain modal difference vibration pattern analysis and damage feature extraction for the detection of structural damage in beams. This method is based on the sensitivity of the sudden change in vibration strain modal difference to small deformations. This method solves the problem of using the conventional structural finite element analysis or experimental modal analysis methods to obtain the displacement mode, intrinsic frequency, and other characteristics, which make it difficult to effectively identify the actual engineering, with the damage conditions of the damage state and damage location of the mine screen frame beam problems. The feasibility and validity of the engineering application of the concept are demonstrated through instances.

Local wavenumber imaging in anisotropic structures

Local wavenumber imaging has been proven effective for damage detection in thin-walled structures. Such structures are, however, often inherently anisotropic, posing serious challenges to the known algorithms of local wavenumber mapping. The use of local wavenumber mapping algorithm designed for isotropic, rather than anisotropic, structures results in processing artifacts that may prohibit correct detection and characterization of defects. Therefore, in this work, we propose an improved implementation of the local wavenumber estimation algorithm for anisotropic media (ALWE). The working principle and efficiency of the proposed algorithm are illustrated on datasets coming from numerical simulations and experimental testing on anisotropic carbon fiber reinforced polymer (CFRP) laminates. We demonstrate that the proposed algorithm successfully compensates for the processing artifacts and enhances the overall quality of the wavenumber maps facilitating unequivocal damage detection.

A hybrid wavelet-deep learning approach for vibration-based damage detection in monopile offshore structures considering soil interaction

A hybrid wavelet-deep learning approach for vibration-based damage detection in monopile offshore structures considering soil interaction

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.

Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage

Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively without human intervention, and are customized for each mechanical structure without the need for complex baseline models. We present a novel “deploy-and-forget” approach for automated detection and localization of damage in structures. It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder. Once deployed, the model continuously learns and adapts a bespoke baseline model for each structure, learning from its undamaged state’s response characteristics. After learning from just 3 hours of data, it can autonomously detect and localize different types of unforeseen damage. Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the autoencoder allows for up to a 35% improvement in the detection and localization of minor damage over a standard autoencoder. Our approach holds significant promise for reducing human intervention and inspection costs while enabling proactive and preventive maintenance strategies. This will extend the lifespan, reliability, and sustainability of civil infrastructures.

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.

Multiple damage detection in sandwich composite structures with lattice core using regression-based machine learning techniques

The abstract discusses the importance of Structural Health Monitoring (SHM) in ensuring the reliability and health of structures. It introduces a novel approach for recognizing cracks and delamination in sandwich composite structures using advanced methods for data analysis and machine learning algorithms. The research leverages artificial intelligence and machine learning to accurately identify and locate damage such as delamination and cracks in composite sandwich layers, which can significantly enhance troubleshooting performance, improve detection accuracy, and reduce the time and cost associated with repairs. The article presents a damage detection technique utilizing regression analysis for sandwich composite structures with a lattice core, capable of identifying and locating multiple cracks and delamination, as well as simultaneous defects in the structure. The analysis is conducted based on the sandwich composite structure’s healthy and damaged conditions in Abaqus software, and acceleration responses under random forces obtained through the finite element method were used to train various machine learning models, including regression algorithms like k-Nearest Neighborhood Regression (KNN), Light Gradient Boost Machine (LGBM), and Decision Tree Regression (DTR) to detect the damage location. The results indicate that the regression (LGBM), k-Nearest neighborhood, and decision tree regression (DTR) were the most successful functions, respectively, and the damage classification models accurately identified the damage and its location in the composite structure, achieving an accuracy rate of approximately 98.8%.

Structural damage detection based on transmissibility functions with unsupervised domain adaptation

Deep learning (DL) has been used for structural damage detection by training neural network models with a large amount of data. These trained models perform well when the test samples are from the data with an identical distribution of the training data. The distributions are always different due to numerical modelling errors and operational environmental varieties, and the data for damage scenarios is difficult to be obtained in practice. A novel method based on the joint maximum discrepancy and adversarial discriminative domain adaptation (JMDAD) for structural damage detection without labelled measurement data has been developed to address the above issues. To reduce the influence of external excitations in practice, the transmissibility function of measured structural responses is used for structural damage detection. The proposed network includes a feature generator, two classifiers and one discriminator. Firstly, the feature generator and domain discriminator are to extract features and merge their distributions at the domain level to overcome the issue of insufficient data from the target real structure. Secondly, the generator and two classifiers are optimised to align their distributions at the class level using the classification discrepancy between the two classifiers. As a result, the damage-sensitive features are extracted and aligned to eliminate modelling errors and operational environmental varieties between the source and target structures. Three case studies have been conducted in this paper, e.g., one case between two building structures with different storeys, another case between the numerical and experimental structures subjected to random and earthquake excitations and the application of Canton Tower. The results show that the proposed method is much robust to the measurement noise and operational environment and the structural damage is identified accurately without labelled measurement data from the target structure in operational environments.

Domain generalization-based damage detection of composite structures powered by structural digital twin

This research addresses the challenge of generalizing deep learning models for different CFRP composite structures in the task of fatigue damage detection. To overcome this challenge, knowledge distillation is employed to enhance the generalizability of deep learning models. A teacher network processes continuous wavelet transform images using Fourier transform and neural networks, while a student network distills the teacher network. This framework improves the models' generalization performance by transferring knowledge from the teacher network to the student network. Additionally, soft gradient boosting is utilized to further enhance the generalizability. By constructing a main sub-network and multiple parallel auxiliary sub-networks within the teacher network, the student network mimics the main sub-network to achieve improved accuracy in the target domain and prevent overfitting. To augment limited datasets of real CFRP monitoring signals and help to learn domain-invariant features, structural digital twin technology is leveraged to generate simulated monitoring signals, which enables the models to capture domain invariant information, significantly enhancing its performance of fatigue damage detection across different structures. Damage detection based on the generalization results between multiple Layups demonstrates a test accuracy exceeding 80 % when the monitoring data of the target CFRP structure is unavailable during training. Therefore, the cross-structure damage detection ability of the proposed approach is well proved.

Whale Optimization Algorithm for structural damage detection, localization, and quantification

This paper introduces an approach for vibration-based damage detection based on matrix updating aided by the Whale Optimization Algorithm (WOA). The methodology uses the Data-driven Stochastic Subspace Identification (SSI-DATA) technique to determine the modal parameters, which are compared with those obtained from both healthy and damaged conditions of the structure. The methodology’s efficacy is assessed through three distinct steps: numerical simulations, experimental data, and real-world data from a bridge. Initially, numerical analyses are conducted on a cantilever beam, a 10-bar truss, and a Warren truss subjected to environmental vibrations with varying damage cases and noise levels. Subsequently, experimental validations are performed on a test system and in the Z24 Bridge. Results from the computational simulations demonstrate the method’s promise to identify, locate, and quantify single and multiple damage cases, even amidst signal noise, variations in the first vibration mode as minimal as 0.015%, and complex structures with 54 elements. Moreover, the matrix updating method utilizing WOA showcased superior accuracy compared to existing techniques in the literature. In addition, the Z24 Bridge example validated the capability of the presented damage detection method to localize structural damage solely based on natural frequencies.

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.

Damage detection and localization in plate-like structures using sideband peak count (SPC) technique

This paper addresses the critical issue of detecting and localizing damage in plate-like structures, which are commonly encountered in aerospace, marine and other engineering applications. To address this challenge, the current study introduces the sideband peak count (SPC) technique as the foundation for diagnostic imaging for damage detection in plate structures. The proposed damage detection algorithm requires only a limited number of sensor responses, streamlining the detection process. It does not rely on a reference baseline, thereby enhancing its efficiency and accuracy. This approach enables rapid and precise identification of damage and its location within the plate structure. To validate the effectiveness and applicability of the proposed method, finite element simulation results are utilized. These results demonstrate the capability of the proposed technique to accurately detect and localize damage, providing a promising solution for enhancing the structural health monitoring of plate-like structures in various engineering domains.

Utilizing machine learning algorithm and Carrera unified formulation for vibration analysis of nanocomposite-enhanced bridge structures rested on an innovative elastic foundation: verification of the results via nondestructive testing

This paper presents an advanced vibration analysis of Al2O3 nanocomposite-reinforced concrete bridge structures resting on an innovative elastic foundation using the Carrera Unified Formulation (CUF). The primary objective is to investigate the dynamic response of these bridges under various loading conditions, accounting for the reinforcing effects of Al2O3 nanocomposites within the concrete matrix. The formulation incorporates a novel elastic foundation model designed to more accurately simulate realistic boundary conditions and soil-structure interaction. The accuracy and reliability of the CUF-based vibration analysis are further validated using nondestructive testing (NDT) techniques, which enable the detection of potential damage and anomalies in the bridge structures. Moreover, a machine learning (ML) algorithm is employed to predict the vibrational characteristics, facilitating an efficient comparison with the results obtained from CUF and NDT. The combination of theoretical modeling, experimental verification, and ML predictions highlights the robustness of the proposed method. The results demonstrate the effectiveness of using Al2O3 nanocomposites to enhance the mechanical properties of bridge structures, improving their vibrational performance, stability, and longevity. This study provides a comprehensive framework for future applications in bridge engineering, combining high-fidelity numerical methods with state-of-the-art testing and computational techniques.

A fine-segmentation algorithm for XCT images of multiphase composite building materials based on deep learning

In response to the challenges presented by the variable internal structures and complex components of multiphase composite building materials, this study introduces a novel image segmentation network named UT-FusionNet. Building on the U-Net model, UT-FusionNet integrates Transformer module and tensor concatenation and fusion mechanism to overcome the limitations of convolutional networks in relation to the receptive field. UT-FusionNet is employed to segment CT images of conventional concrete, grout consolidation bodies, and fiber-reinforced concrete. The results demonstrate that UT-FusionNet achieves superior segmentation accuracy and robustness, with Accuracy (ACC), Intersection over Union (IoU) and Dice scores exceeding 90 % across all subtasks. The mean accuracy metrics are 99.44 %, 96.70 %, and 98.31 %, respectively. This innovative end-to-end network offers robust support for detailed structural analysis, damage detection, and digital modeling of multiphase composite building materials through deep learning.

A novel damage index for crack identification of a drilling riser during deployment based on the modal vibration energy flow

Early detection of structural damage especially small cracks in marine deepwater drilling risers is of paramount importance. Small cracks are always hard to detect because of weak vibration signals if using traditional damage indices, such as natural frequencies and mode shapes including modal displacements, cross-section slopes, and curvatures. Especially when the deepwater riser is during deployment, the variable suspension riser length and strong sea environment noise make the small crack identification more difficult. To address this problem, this study formulates a new concept of damage detection by integrating the vibration energy flow theory into the vibration-based damage detection (VDD) technique. A novel damage-sensitive feature (DSF) named modal vibration energy flow (MVEF) is established for detecting and locating the small cracks in deepwater drilling risers. The capability of the approach is verified by using the same riser model and the numerical results in the literature. The sensitivities versus crack positions and depths, variable suspension riser length, and sea noise are studied in detail. Results show that the proposed index MVEF is more sensitive than the classic traditional DSFs, i.e., modal slope, modal strain (or curvature), and modal strain energy. In total, the MVEF approach can accurately identify the small cracks and is robust against noise interference, suitable for detecting and locating small cracks in the deepwater drilling riser during deployment.

Nonlinear Crack-Wave Modulations in Shear Horizontal Wave Propagation for Fatigue Crack Detection

The nonlinear interaction of shear horizontal waves with fatigue cracks is investigated for structural damage detection. The focus is on the non-classical modulation effect in the presence of damage. The major principle of the method is explained theoretically and supported by a semi-analytical solution. The method is tested experimentally in aluminium beams with fatigue cracks. A combination of high-frequency ultrasonic and low-frequency vibration shear excitations are introduced to monitored beam components using low-profile piezoceramic shear actuators. Nonlinear vibro-acoustic responses are gathered using laser vibrometry. Vibration excitation frequencies are selected using experimental and numerical modal analysis. A series of vibro-acoustic experiments is performed for fatigue crack detection. The results show that the non-classical nonlinear vibro-acoustic effect – based on shear horizontal wave propagation – can be used for crack detection. In addition, the method also allows for damage localisation.

A Comparative Study of Single-Chain and Multi-Chain MCMC Algorithms for Bayesian Model Updating-Based Structural Damage Detection

Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge.

Structural damage detection of floating offshore wind turbine blades based on Conv1d-GRU-MHA network

Due to the global commitment to mitigate climate warming by replacing fossil fuels, the global wind power installed capacity continues to increase. The reliability of wind turbines is a vital factor in ensuring their safety and profitability. Early fault detection can significantly reduce the maintenance costs of wind farms. This paper constructs a Conv1d-GRU-MHA model for detecting blade structural damage in floating offshore wind turbines (FOWTs) operating under complex conditions. This model combines a one-dimensional convolutional neural network (Conv1d), gated recurrent units (GRU), and multi-head attention mechanism (MHA) to provide accurate detection of blade structural damage. This amalgamation enables the model to comprehend diverse scales of sequence data, effectively capturing intricate patterns and abstract features. The NREL OpenFAST software was used to model a 5 MW wind turbine, where structural damage was introduced on the blades. Acceleration data was collected from blades and tower in different directions and under different wind turbulence intensities (WTI). The Conv1d-GRU-MHA network predicts blade health with an accuracy of up to 97.58 % under the 5 % turbulence level background, with both precision and recall being superior to the comparative models.

Uncertainty quantification for damage detection in 3D-printed auxetic structures using ultrasonic guided waves and a probabilistic neural network

Auxetic structures hold significant potential for applications due to their outstanding properties. Ultrasonic waves and neural networks are the popular technologies used for structural health monitoring (SHM). To increase the reliability of the neural network output for SHM, comprehensive uncertainty quantification is needed for damage detection in unknown areas of auxetic structures. This paper presents the first comprehensive framework for health diagnosis and uncertainty quantification based on ultrasonic guided waves in two 3D-printed star hourglass honeycomb auxetic structures. The proposed framework integrates in-plane compression with simultaneous ultrasonic testing to receive ultrasonic signals across various deformation states. Additionally, fully elastic and elasto-plastic finite element simulations are conducted to analyze wave energy variations and mechanical responses in auxetic structure. Critical damage deformation is identified based on observed deformation patterns and variations in signal energy. The Hilbert transform is used to extract two damage-sensitive features, namely envelope and phase. These features serve as input data for the Flipout probabilistic convolutional neural network (FPCNN) model, which integrates pseudo-independent weight perturbations and a Gaussian probabilistic layer within the visual geometry group 13 architecture to predict structural deformations and associated uncertainties. The UQ framework effectively separates and quantifies the predictive variance of the FPCNN model into aleatoric and epistemic uncertainty. The framework’s effectiveness is demonstrated through the comprehensive approach, combining compression and ultrasonic tests, finite element simulation, and the FPCNN technique.

Overlapping Group Sparse Regularization Technique for Vibration-Based Regional Damage Detection of Structures

In structural damage detection (SDD) studies, regularization techniques have shown potential for improving ill-posedness. However, existing methods based on regularization techniques cannot yield satisfactory results for the SDD problem involving regional damages. Based on the sparse distribution characteristics of regional damages in the finite element (FE) model, this study proposes a novel SDD method that integrates sensitivity analysis of modal parameters with overlapping group sparse regularization. First, the relationship between modal parameters and structural damages is established based on sensitivity analysis. Then, the overlapping group sparsity of regional damages is analyzed and the objective function for SDD can be defined. Finally, a grouping matrix is introduced to transform the objective function from overlapping to non-overlapping group sparse regularization. To evaluate the effectiveness of the proposed method, both numerical simulations and experimental studies are employed, and comparative studies are conducted with the SDD method based on the [Formula: see text]-norm regularization. The identification results indicate that the proposed method can handle regional damages in the FE model and identify both single and multiple damages well.