Structural Damage Detection Research Articles

Standard deviation as a suitable criterion for localizing damages in Nano- composite structures

Wavelet transform is a proper method for detecting damages in signals such as vibration mode shapes. This study introduces the standard deviation as a suitable criterion for selecting and analyzing wavelet function in damage detection of nano-laminated composite structures. In this way, a Fe2O3-carbon epoxy fiber laminated composite structure is studied. Experimental modal analysis is conducted to obtain the natural frequencies and their corresponding mode shapes. Findings show that the standard deviation can effectively find the best analyzing wavelet function for structural damage detection when the damages are led to local and subtle changes in vibration mode shapes.

Assessment of foundation damage during earthquakes using acoustic emission monitoring: An experimental study on the shaking table test

Assessing structural foundation damage following an earthquake is critical for safety evaluation. However, assessing the damage to pile foundations with traditional visual inspections and non-destructive testing methods is challenging. This study evaluated the use of acoustic emission (AE) monitoring for damage detection and location in foundation structures during earthquake simulations by conducting shaking table experiments. Scaled models of foundation structures with and without surrounding soil were used in the experiments, and the performance of the AE monitoring system was evaluated by comparing the AE parameters via visual inspection. The experimental results showed that the AE monitoring system could effectively predict the initiation of cracks in foundation structures that experienced an earthquake. In addition, an appropriate filtering criterion for the shaking table experiments was established based on the AE characteristics of the foundation structures during the earthquake simulation, thereby improving the performance of the AE monitoring system for damage location. Consequently, this study contributed to a better understanding of the applicability of AE monitoring systems to foundation structures during earthquakes.

Advanced structural damage detection in steel beams using discrete wavelet transform and fast Fourier transform

Advanced structural damage detection in steel beams using discrete wavelet transform and fast Fourier transform

A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms.

Quick and accurate structural damage detection is essential for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage. For training purposes, a diverse dataset of images depicting both structural damage cases and undamaged cases was used. To further enhance the robustness, data augmentation techniques were also employed. In this research, precision, recall, F1-score, and accuracy were employed to evaluate the effectiveness of the introduced hybrid deep learning model. Our findings indicate that the hybrid deep neural network introduced in this study significantly outperformed standalone architectures such as ResNet50 and GoogLeNet, making it a highly effective solution for applications in disaster response and infrastructure maintenance.

Structural damage detection using a sensitivity-based model updating approach with autocorrelation function data

ABSTRACT This study presents a novel sensitivity equation for estimating structural parameters using autocorrelation function (ACF) data of dynamic responses. The proposed method reduces the need for extensive excitation frequencies, making structural parameter identification more practical and cost-effective, especially for large-scale structures. Furthermore, due to the quadratic relationship between ACF and the structural dynamic response, this approach exhibited greater sensitivity to damage than time-history-based methods. The method’s effectiveness was evaluated using simulated data from truss and offshore jacket platform models. Incomplete measurements were addressed by using partially measured structural characteristics without model reduction and data expansion processes. Measurement and modelling errors were simulated by adding uniformly distributed random error into the damaged structure data. The low values of the coefficient of variation indicated the method’s robustness against these errors. The accuracy of the method was confirmed through low root mean square error (RMSE) values and high variance accounted for (VAF) values.

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

Multi-Source Transfer Learning for zero-shot Structural Damage Detection

Developing generalizable Structural Health Monitoring (SHM) tools for downstream tasks, such as Structural Damage Detection (SDD), is one way to tackle large-scale SHM applications. Such tools become particularly important when dealing with a heterogeneous population of structures lacking prior (damaged and non-damaged) data, a common scenario in large-scale SHM applications. Devising Transfer Learning (TL) mechanisms using representative SHM datasets is a favorable method to address this challenge. Multiple SHM datasets are publicly available but only partially cover the objective representative SHM dataset. The missing parts limit the ability to extract or embed high-level features, resulting in non-generalizable solutions, such as one-to-one (source-to-target) TL models. In the absence of a representative SHM dataset, this study tackles the challenge of accumulating SDD knowledge from sparse, non-similar datasets and integrating them to enable damage detection in unseen target structures with no prior damaged data. The innovation lies in two areas: (1) defining a feature and Domain Adaptation (DA) method that can transfer features between dissimilar structures, and (2) designing an SDD model to learn from these features and a mechanism to combine the learned SDD knowledge from various sources in a zero-shot setting, a necessity in online SHM applications without prior damaged data. To that end, this study proposes a DA-based TL approach and employs ensemble techniques to combine knowledge from parametric models trained on each source dataset, thereby establishing a common ground for fusing data from different structures. The proposed zero-shot Multi-Source TL-SDD technique achieves mean F1 scores of 0.971, 0.959, and 0.922 in three well-known SHM benchmark datasets, without requiring fine-tuning of models pre-trained on source data for the target features. This performance is achieved by integrating signal processing domain knowledge into the DA procedure, outperforming other competitive deep zero-shot SDD techniques trained with the target non-damaged data. Computational complexity analysis is also examined, demonstrating the method satisfying online SHM requirements, a critical feature for real-world applications.

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%.

Acoustic emission source location in complex structures based on artificial potential field-guided rapidly-exploring random tree* and genetic algorithm

Towards more accurate and easy-to-implement damage detection in large-scale complex structures, a novel acoustic emission (AE) source location method is developed based on artificial potential field-guided rapidly-exploring random tree* (APF-RRT*) and genetic algorithm (GA). APF-RRT*, which combines the excellent obstacle avoidance ability of RRT* with the path planning efficiency of APF, is introduced to adaptively estimate the shortest distances from the damage source to AE sensors. The shortest distances are obtained as the actual propagation distances of waves and then embedded into the modified error function, where GA is employed as an optimization scheme to evaluate the source location via iterations. Through the experiment on a full-scale high-strength bolt joint plate with a series of bolt holes, the effectiveness and superiority of the proposed method were validated. It achieved a better source location performance with lower mean absolute error and standard deviation than the time-of-arrival (TOA) method, delta-T mapping method, and machine learning-improved methods based on Gaussian process (GP) and artificial neural network (ANN), respectively. The primary contributions of the proposed method lay in abandoning the straight-wave-propagation assumption of the traditional TOA method by adaptively taking into account the geometric obstacles in complex structures, and removing the need for a large amount of training data and burdensome pencil lead break (PLB) tests required by data-driven location methods.

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.