Structural Damage Localization Research Articles

Impact Damage Localization in Composite Structures Using Data-Driven Machine Learning Methods.

Due to the uncertainty of material properties of plate-like structures, many traditional methods are unable to locate the impact source on their surface in real time. It is important to study the impact source-localization problem for plate structures. In this paper, a data-driven machine learning method is proposed to detect impact sources in plate-like structures and its effectiveness is tested on three plate-like structures with different material properties. In order to collect data on the localization of the impact source, four piezoelectric transducers and an oscilloscope were utilized to construct an experimental platform for impulse response testing. Meanwhile, the position of the impact source on the surface of the test plate is generated by manually releasing the steel ball. The eigenvalue of arrival time in the time domain signal is extracted to build data sets for machine learning. This paper uses the Back Propagation (BP) neural network to learn the difference in the arrival time of each sensor and predict the location of the impact source. The results demonstrate that the machine learning method proposed in this paper can predict the location of the impact source in the plate-like structure without relying on the material properties, with high test accuracy and robustness. The research work in this paper can provide experimental methods and testing techniques for locating impact damage in composite material structures.

Damage location in mechanical structures by multi-objective pattern search

Damage location in mechanical structures by multi-objective pattern search

Baseline-free localization and quantification of structural damage using spectral response

Baseline-free localization and quantification of structural damage using spectral response

Physics-guided neural network for structural health monitoring with lamb waves through boundary reflection elimination

Lamb-wave-based structural health monitoring (SHM) technology for damage location in plate-like structures relies on the postprocessing of captured signals after interacting with damage. Traditional methods typically leverage the time of flight (ToF) of scattered waves from damage. However, these methods are prone to reflected waves from structural boundaries which mix with scattered waves from damage. This is a vital problem faced by most ToF-based detection methods, which seriously narrows the inspection area. To tackle this problem, a machine learning framework, consisting of a multiscale spatiotemporal (MSST) fusion network, is proposed to facilitate the accurate extraction of the ToF of scattered waves through eliminating the influence of boundary reflections. Experiments are conducted with the time-domain Lamb wave signals recorded by a tactically designed piezoelectric sensor array on a 2-mm-thick Al-6061 plate. A pair of circle magnets is attached onto the plate as the wave reflectors. Through step-by-step moving of the magnets in the predefined grids, the corresponding Lamb wave signals are measured to construct a database. An MSST is subsequently designed to minimize the error between estimated and theoretical ToFs, with wavelet coefficients of the signals and transducer position as inputs. The model is trained with the Adam algorithm where 80% of samples in the database are used for training and the rest for evaluation. The final validations are conducted with the scatters off the predefined grids. Results demonstrate that the designed neural network architecture can effectively eliminate boundary reflections and enable precise ToF extraction of the scattered waves from damage. This allows the enlargement of the detection area and presents a promising and useful tool for enhancing the detection performance of existing SHM methods in complex structures.

Wavelet packet transformation-based improved acoustic emission method for structural damage identification

Abstract Acoustic Emission (AE) technique has emerged as a sophisticated nondestructive testing technique that plays a crucial role in detecting and localizing damage in structures. This paper proposes a damage visualization approach by leveraging the signal decomposition capabilities of Wavelet Packet Transformation (WPT) and the classification abilities of the Gaussian Mixture Model (GMM). First, WPT decomposes AE signals acquired from the instrumented structure at different loading stages. The coordinates (e.g., x and y) of AE events identified by the localization model using de-noised AE components obtained from WPT are then determined. The extracted coordinates are used in the GMM model to visualize the location of the damage during the intermediate and final loading stages. The proposed method is validated using a suite of lab-scale experimental studies of concrete beams. The study compares the outcomes of the proposed method with those obtained from a traditional digital image correlation (DIC) system for both intermediate and final stages of damage. The results indicate that the proposed framework effectively visualizes the locations of various types of damage, such as flexural and shear cracks, at an early stage compared to the DIC. This demonstrates the proposed method's capability to be a reliable tool for early damage localization and visualization in concrete structures.

Damage Localization and Severity Assessment in Composite Structures Using Deep Learning Based on Lamb Waves.

In composite structures, the precise identification and localization of damage is necessary to preserve structural integrity in applications across such fields as aeronautical, civil, and mechanical engineering. This study presents a deep learning (DL)-assisted framework for simultaneous damage localization and severity assessment in composite structures using Lamb waves (LWs). Previous studies have often focused on either damage detection or localization in composite structures. In contrast, this study aims to perform damage detection, severity assessment, and localization using independent DL models. Three DL models, namely the artificial neural network (ANN), convolutional neural network (CNN), and gated recurrent unit (GRU), are compared. To assess their damage detection and localization capabilities. Moreover, zero-mean Gaussian noise is introduced as a data augmentation technique to address the variability and noise inherent in LW signals, improving the generalization capability of the DL models. The proposed framework is validated on a composite plate with four piezoelectric transducers, one at each corner, and achieves high accuracy in both damage localization and severity assessment, offering an effective solution for real-time structural health monitoring. This dual-function approach provides a scalable data-driven method to evaluate composite structures, with applications in predictive maintenance and reliability assurance in critical engineering systems.

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.

A Hybrid Approach of Long Short-Term Memory and Machine Learning With Acoustic Emission Sensors for Structural Damage Localization

A Hybrid Approach of Long Short-Term Memory and Machine Learning With Acoustic Emission Sensors for Structural Damage Localization

Robust data-driven online learning algorithm for precise structural damage localization using stochastic subspace identification

Robust data-driven online learning algorithm for precise structural damage localization using stochastic subspace identification

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

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

Bridge Damage Localization Through Response Reconstruction with Multiple BP-ANNs Under Vehicular Loading

Damage detection is a critical aspect of bridge health monitoring. While data reconstruction has been posited as a promising method for damage detection, its effectiveness in this context has rarely been empirically validated. In this study, we introduce a novel approach to pinpoint potential bridge damage by reconstructing bridge inclination data. For an intact bridge, we selected reference cross-sections and trained multiple Backpropagation Artificial Neural Networks (BP-ANNs) to simulate transfer matrices for inclination between these base sections and other sections of the bridge. These BP-ANNs were then employed to reconstruct inclination data at the same cross-sections on a bridge with artificial damage. We demonstrated that damage localization is feasible through a comparison of the reconstructed and actual measured responses. The theoretical underpinnings of the transfer matrix and the damage localization method were initially elucidated through an analysis of the dynamics of a simplified vehicle–bridge interaction (VBI) system. A series of finite element models were constructed to substantiate the theoretical basis of the damage localization method. Additionally, a large-scale laboratory experiment was carried out to assess the practical effectiveness of the proposed method. The proposed method has been demonstrated to effectively pinpoint the location of potential structural damage. It successfully differentiates between areas in close proximity to the damage and those that are more distant. Compared to existing research, our method does not necessitate prior knowledge of factors such as mode shape functions, traffic conditions, or the constraint of inspecting with a single vehicle. This approach is anticipated to be more convenient for engineering applications, particularly in the development of online monitoring systems, due to its streamlined requirements and robust performance in identifying damage localization.

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.

Relationship Between Ultrasound and Physical Examination in the Assessment of Enthesitis in Patients With Spondyloarthritis: Results From the DEUS Multicenter Study.

The study objectives were (i) to explore the agreement between the Outcome Measures in Rheumatology (OMERACT) ultrasound lesions of enthesitis and physical examination in assessing enthesitis in patients with spondyloarthritis (SpA) and (ii) to investigate the prevalence and clinical relevance of subclinical enthesitis in this population. Twenty rheumatology centers participated in this cross-sectional study. Patients with SpA, including axial spondyloarthritis (axSpA) and psoriatic arthritis (PsA), underwent both ultrasound scan and physical examination of large lower limb entheses. The OMERACT ultrasound lesions of enthesitis were considered, along with a recently proposed definition for "active enthesitis" by our group. Subclinical enthesitis was defined as the presence of "active enthesitis" in ≥1 enthesis in patients with SpA without clinical enthesitis (ie, number of positive entheses on physical examination and Leeds Enthesitis Index score = 0). A total of 4,130 entheses in 413 patients with SpA (224 with axSpA and 189 with PsA) were evaluated through ultrasound and physical examination. Agreement between ultrasound and physical examination ranged from moderate (ie, enthesophytes) to almost perfect (ie, power Doppler and "active enthesitis"). Patellar tendon entheses demonstrated the highest agreement, whereas Achilles tendon insertion showed the lowest. Among 158 (38.3%) of 413 patients with SpA with clinical enthesitis, 108 (68.4%) exhibited no "active enthesitis" on ultrasound. Conversely, of those 255 without clinical enthesitis, 39 (15.3%) showed subclinical enthesitis. Subclinical enthesitis was strongly associated with local structural damage. However, no differences were observed regarding the demographic and clinical profiles of patients with SpA with and without subclinical enthesitis. Our study underscores the need for a comprehensive tool integrating ultrasound and physical examination for assessing enthesitis in patients with SpA.

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.

Mechanisms of erectile dysfunction induced by aging: A comprehensive review.

With the increasing trend ofpopulation aging, erectile dysfunction (ED) among elderly men has emerged as apressing health concern. Despite extensive research on the relationship betweenED and aging, ongoing discoveries and evidence continue to arise. Through this comprehensiveanalysis, we aim to provide a more nuanced theoretical framework for thedevelopment of preventive and therapeutic strategies for senile ED, ultimatelyenhancing the quality of life for elderly men. This review delves deeper into thecore mechanisms underlying ED in the context of aging and offers acomprehensive overview of published meta-analyses and systematic reviewspertinent to these conditions. Our findings revealthat local structural damage to the penis, vascular dysfunction, neuronalinjury, hormonal alterations, other physiological changes, and psychologicalbarriers all play pivotal roles in the pathogenesis of aging-related ED.Furthermore, more than 20 diseases closely associated with aging have beenimplicated in the occurrence of ED, further compounding the complexity of thisissue.

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.

Impact of Model Knowledge on Acoustic Emission Source Localization Accuracy

Reliable and precise damage localization in mechanical structures is of high importance in the context of structural health monitoring (SHM). The acoustic emission (AE) method has already shown its excellent suitability for damage localization. However, low signal-to-noise ratios (SNRs) are often prevalent in SHM, thus an increase in localization accuracy and robustness is still in demand. The present study faces this task through the integration of various model knowledge for AE source localization. The basis of the presented algorithm is the consideration of the dispersive behavior of elastic waves in thin-walled structures. The continuous wavelet transform (CWT) is used to obtain time-frequency representations of the signals, where frequency-dependent values for time-of-arrival (TOA) are extracted. Furthermore, the algorithm incorporates the knowledge that all sensors receive signals with the same time and location of origin, as well as the slightly different sensitivity of the theoretically equal sensors. The final localization results are achieved by a two parameter grid search optimization. The algorithm is experimentally tested on a large aluminum plate with four piezoelectric wafer active sensors (PWASs) arranged in an array of 100 mm × 100 mm. The mean localization error of pencil lead breaks at ten different positions within the sensor array is used as an accuracy measure. It is shown that as more of the described model knowledge is incorporated into the localization, the accuracy increases. With the final algorithm, the mean localization error is more than halved compared to AE localization based on classical TOA estimation. Although the experiment described is conducted under laboratory conditions, the remarkable increase in accuracy suggests that AE source localization may be successful even at low SNR as typical for operational conditions.

Guided wave multi-frequency damage localization method in variable-thickness structures by one pair of sensors based on frequency-dependent velocity anisotropy

Variable thickness structures are prevalent in aircraft, ships, and other machines, necessitating numerous sensors for health monitoring to reduce safety hazards. This paper presents a guided wave multi-frequency localization method based on frequency-dependent velocity anisotropy. This method achieves damage localization in variable-thickness structures with a pair of sensors and can effectively reduce the number of sensors used for monitoring. Variations in structural thickness cause a gradient in guided wave velocity that bends the propagation path. Different thickness variations with different directions cause wave velocity anisotropy. As a result, variations in thickness cause possible damage loci determined by echo time to deviate from an elliptical shape. Because the velocity anisotropy is frequency-dependent, damage loci at different frequencies are close but do not overlap and intersect only at the damage location. So, the multi-frequency method can increase the damage information acquired by a single pair of sensors, enabling damage localization. Experimental validation was conducted on a steel plate with linearly varying thicknesses. The feasibility of the multi-frequency localization method was verified by successfully locating the damage at three different locations using a pair of receiver-excitation sensors. In addition, the experiments demonstrated the capability of this multi-frequency method in improving the localization accuracy of sensor networks. The method has potential applications in monitoring systems lightweight, phased arrays, and imaging enhancement.

Numerical study of steady-state dynamic characteristic of non-pneumatic tire with local structural damage

Non-pneumatic tires (NPTs) fundamentally avoid the risk of tire blowout of traditional pneumatic tires, but the overall and key component performance inevitably degrades due to factors such as high temperature, variable load, variable working conditions and impact in its service process. Once this leads to failure, it will significantly impact on vehicle safety. To this end, this paper studied the influence of local structural damage on the dynamic response of NPTs, which lays the foundation for realizing health monitoring of intelligent non-pneumatic tires (INPTs). Firstly, a three-dimensional nonlinear finite element model of NPTs was established, and the failure locations of NPTs were determined by fracture mechanics and maximum strain energy density; Secondly, the influence of structural damage on the static and dynamic performance of NPTs was analyzed; Finally, the sensitivity of the acceleration signal and sensor position of the tire inner liner to the local structural damage was studied. The research results show that structural damage will cause the stress of the spokes and shear layer to increase, and as the number of broken spokes increases, the shear layer will bear a larger load. Compared with the circumferential and lateral acceleration, the radial acceleration has the highest sensitivity to the damage of NPTs. The sensor closest to the damage location is the most sensitive to the damage. The research results provide a reference for the structural optimization design and health monitoring of INPTs.

Structural damage detection and localization via an unsupervised anomaly detection method

This study introduces an unsupervised machine learning framework for damage detection and localization in Structural Health Monitoring (SHM), leveraging dynamic graph convolutional neural networks and Transformer networks. This approach is specifically tailored to overcome the challenge of limited labeled data in SHM, enabling precise analysis and feature synthesis from sensor-derived time series data for accurate damage identification. Incorporating a novel ‘localization score’ enhances the framework’s precision in pinpointing structural damages by integrating data-driven insights with a physics-informed understanding of structural dynamics. Extensive validations on diverse structures, including a benchmark steel structure and a real-world cable-stayed bridge, underscore the framework’s effectiveness in anomaly detection and damage localization, showcasing its potential to safeguard critical infrastructure through advanced data-effective machine learning techniques.