Active Detection of Structural Damage in Aluminum Alloy Using Magneto-Elastic Active Sensors (MEAS)

2011 ◽  
Author(s):  
David Conrad ◽  
Andrei Zagrai
Keyword(s):  
Aluminum Alloy ◽  
Damage Detection ◽  
Elastic Waves ◽  
Structural Damage ◽  
Fatigue Cracks ◽  
Plate Thickness ◽  
Detection Methods ◽  
Thin Plates ◽  
Active Sensors ◽  
Host Structure

Many structural damage detection methods utilize piezoelectric sensors. While these sensors are efficient in supporting many structural health monitoring (SHM) methodologies, there are a few key disadvantages limiting their use. The disadvantages include the brittle nature of piezoceramics and their dependence of diagnostic results on the quality of the adhesive used in bonding the sensors. One viable alternative is the utilization of Magneto-Elastic Active Sensors (MEAS). Instead of mechanically creating elastic waves, MEAS induce eddy currents in the host structure which, along with an applied magnetic field, generate mechanical waves via the Lorentz force interaction. Since elastic waves are generated electromagnetically, MEAS do not require direct bonding to the host structure and its elements are not as fragile as PWAS. This work explores the capability of MEAS to detect damage in aluminum alloy. In particular, methodologies of detecting fatigue cracks in thin plates were explored. Specimens consisted of two identical aluminum plates featuring a machined slot to create a stress riser for crack formation. One specimen was subjected to cyclic fatigue load. MEAS were used to transmit elastic waves of different characteristics in order to explore several SHM methodologies. Experiments have shown that the introduction of fatigue cracks created measurable amplitude changes in the waves passing through the fatigued region of the aluminum plate. The phase indicated sensitivity to load conditions, but manifestation in the cracked region lacked stability. Nonlinear effects were studied using plate thickness resonance, which revealed birefringence due to local stresses at the site of the fatigue crack. The resonance spectrum has also shown a frequency decrease apparently due to stiffness loss. Preliminary results suggest opportunities for fatigue damage detection using MEAS. Application of MEAS for the diagnosis of complex structures is currently being investigated.

scholarly journals UAV-BASED STRUCTURAL DAMAGE MAPPING – RESULTS FROM 6 YEARS OF RESEARCH IN TWO EUROPEAN PROJECTS

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 187-194 ◽  
Author(s):  
N. Kerle ◽  
F. Nex ◽  
D. Duarte ◽  
A. Vetrivel
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Point Cloud ◽  
Feature Fusion ◽  
Texture Features ◽  
Mobile App ◽  
Detection Methods ◽  
Support Vector ◽  
3D Point Cloud ◽  
Damage Mapping

<p><strong>Abstract.</strong> Structural disaster damage detection and characterisation is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of UAV in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.</p>

Damage Size Estimation of the Aircraft Structure with Use of Embedded Sensor Network Generating Elastic Waves

2014 ◽  
Vol 598 ◽  
pp. 57-62 ◽  
Author(s):  
Michal Dziendzikowski ◽  
Krzysztof Dragan ◽  
Artur Kurnyta ◽  
Sylwester Klysz ◽  
Andrzej Leski
Keyword(s):  
Damage Detection ◽  
Elastic Waves ◽  
Guided Waves ◽  
Fatigue Cracks ◽  
Model Description ◽  
Growth Monitoring ◽  
Size Estimation ◽  
Metallic Structures ◽  
Damage Indices ◽  
Signal Characteristics

One of the approach to develop a system of continues, automated monitoring of the health of the structures is to use elastic waves excited in a given medium by piezoelectric transducers network. Elastic waves depending on their source and the geometry of the structure under consideration can propagate over significant distance. They are also sensitive to local structure discontinuities and deformations providing a tool to detect local damage of large aerospace structures. In the paper the issue of fatigue crack growth monitoring by means of elastic guided waves actuated by a sparse array of sensors will be presented. In particular we propose signal characteristics, robust enough to detect different kinds of damages: Barely Visible Impact Damages (BVIDs) in composite materials and fatigue cracks of metallic structures. The model description and the results of specimen tests verifying damage detection capabilities of the proposed signal characteristics are delivered in the paper. Some issues concerning the proposed damage indices and its application to damage detection and its monitoring are also discussed.

Damage Assessment of a Building Subjected to a Terrorist Attack

2014 ◽  
Vol 17 (11) ◽  
pp. 1693-1704 ◽  
Author(s):  
E.L. Eskew ◽  
S. Jang
Keyword(s):  
Damage Detection ◽  
Damage Assessment ◽  
Structural Damage ◽  
Numerical Models ◽  
Terrorist Attack ◽  
Structural Response ◽  
Structural Condition ◽  
Condition Assessment ◽  
Detection Methods ◽  
Specific Element

An increasing threat of global terrorism has led to concerns about bombings of buildings, which could cause minor to severe structural damage. After such an event, it is important to rapidly assess the damage to the building to ensure safe and efficient emergency response. Current methods of visual inspection and non-destructive testing are expensive, subjective, and time consuming for emergency responders' usage immediately after an attack. On the other hand, vibration-based damage detection methods with wireless smart sensors could provide rapid assessment of structural characteristics with low cost. For blast analysis, structural response is usually determined using a simplified SDOF version of the undamaged structure, such as used in a Pressure-Impulse (P-I) Diagram, or using more complex FEM (finite element method) models. However, the simplified models cannot take into account damage caused by blast focus at a specific location or on a specific element, which may induce local failure leading to potential progressive collapse, and the more complex FEM models take too long to derive applicable results to be effective for a rapid structural assessment. In this paper, a new method to incorporate vibration-based damage detection methods to calculate the multi degree of freedom structural stiffness for determining structural condition is provided to create a framework for the rapid structural condition assessment of buildings after a terrorist attack. The stiffness parameters are generated from the modal analysis of the measured vibration on the building, which are then used in a numerical simulation to determine its structural response from the blast. The calculated structural response is then compared to limit conditions that have been developed from ASCE blast design codes to determine the damage assessment. A laboratory-scale building frame has been employed to validate the developed use of experimentally determined stiffness by comparing the P-I diagram using the experimental stiffness with that from numerical models. The reasonable match between the P-I diagrams from the numerical models and the experiments shows the positive potential of the method. The framework and examples of how to develop a rapid condition assessment are presented.

Structural Nonlinear Damage Detection Method Using AR/ARCH Model

2017 ◽  
Vol 17 (08) ◽  
pp. 1750083 ◽  
Author(s):  
J. J. Cheng ◽  
H. Y. Guo ◽  
Y. S. Wang
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Fatigue Cracks ◽  
Arch Model ◽  
Structure Model ◽  
Relevant Research ◽  
Engineering Structures ◽  
The Past ◽  
Damage Indicator ◽  
Shear Building

Structural health monitoring (SHM) has received increasing attention in the research community over the past two decades. Most of the relevant research focuses on linear structural damage detection. However, the majority of the damage in civil engineering structures is nonlinear, such as fatigue cracks that open and close under dynamic loading. In this study, a new hybrid AR/ARCH model in the field of economics and a proposed damage indicator (DI) which is the second-order variance indicator (SOVI) based on the model have been used for detecting structural nonlinear damage. The data from an experimental three-storey structure and a simulated eight-storey shear building structure model have been used to verify the effectiveness of the algorithm and SOVI. In addition, a traditional linear DI: cepstral metric indicator (CMI) has also been used to diagnose the nonlinear damage. The results of the CMI and SOVI were compared and it is found that there are advantages in using the SOVI in the field of nonlinear structural damage.

scholarly journals A Universal Fast Algorithm for Sensitivity-Based Structural Damage Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Q. W. Yang ◽  
J. K. Liu ◽  
C.H. Li ◽  
C.F. Liang
Keyword(s):  
Damage Detection ◽  
Fast Algorithm ◽  
Structural Damage ◽  
Research Area ◽  
Detection Methods ◽  
Structural Damage Detection ◽  
Response Data ◽  
Flexibility Matrix ◽  
New Research ◽  
Measured Response

Structural damage detection using measured response data has emerged as a new research area in civil, mechanical, and aerospace engineering communities in recent years. In this paper, a universal fast algorithm is presented for sensitivity-based structural damage detection, which can quickly improve the calculation accuracy of the existing sensitivity-based technique without any high-order sensitivity analysis or multi-iterations. The key formula of the universal fast algorithm is derived from the stiffness and flexibility matrix spectral decomposition theory. With the introduction of the key formula, the proposed method is able to quickly achieve more accurate results than that obtained by the original sensitivity-based methods, regardless of whether the damage is small or large. Three examples are used to demonstrate the feasibility and superiority of the proposed method. It has been shown that the universal fast algorithm is simple to implement and quickly gains higher accuracy over the existing sensitivity-based damage detection methods.

Structural damage detection via combining weighted strategy with trace Lasso

2018 ◽  
Vol 22 (3) ◽  
pp. 597-612 ◽  
Author(s):  
Chengbin Chen ◽  
Chudong Pan ◽  
Zepeng Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Swarm Intelligence ◽  
Structural Damage ◽  
Optimization Problem ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Structural Damage Detection ◽  
Selection Operator

With the rapid development of computation technologies, swarm intelligence–based algorithms become an innovative technique used for addressing structural damage detection issues, but traditional swarm intelligence–based structural damage detection methods often face with insufficient detection accuracy and lower robustness to noise. As an exploring attempt, a novel structural damage detection method is proposed to tackle the above deficiency via combining weighted strategy with trace least absolute shrinkage and selection operator (Lasso). First, an objective function is defined for the structural damage detection optimization problem by using structural modal parameters; a weighted strategy and the trace Lasso are also involved into the objection function. A novel antlion optimizer algorithm is then employed as a solution solver to the structural damage detection optimization problem. To assess the capability of the proposed structural damage detection method, two numerical simulations and a series of laboratory experiments are performed, and a comparative study on effects of different parameters, such as weighted coefficients, regularization parameters and damage patterns, on the proposed structural damage detection methods are also carried out. Illustrated results show that the proposed structural damage detection method via combining weighted strategy with trace Lasso is able to accurately locate structural damages and quantify damage severities of structures.

scholarly journals Disassembling-Based Structural Damage Detection Using Static Measurement Data

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Eun-Taik Lee ◽  
Hee-Chang Eun
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Degrees Of Freedom ◽  
Measurement Data ◽  
Truss Structures ◽  
Detection Methods ◽  
Internal Stresses ◽  
Structural Damage Detection ◽  
Constraint Forces ◽  
Entire Structure

Damage detection methods can be classified into global and local approaches depending on the division of measurement locations in a structure. The former utilizes measurement data at all degrees of freedom (DOFs) for structural damage detection, while the latter utilizes data of members and substructures at a few DOFs. This paper presents a local method to detect damages by disassembling an entire structure into members. The constraint forces acting at the measured DOFs of the disassembled elements at the damaged state, and their internal stresses, are predicted. The proposed method detects locally damaged members of the entire structure by comparing the stress variations before and after damage. The static local damage can be explicitly detected when it is positioned along the constraint load paths. The validity of the proposed method is illustrated through the damage detection of two truss structures, and the disassembling (i.e., local) and global approaches are compared using numerical examples. The numerical applications consider the noise effect and single and multiple damage cases, including vertical, diagonal, and chord members of truss structures.

scholarly journals Decision Fusion for Structural Damage Detection: Numerical and Experimental Studies

2010 ◽  
Vol 2010 ◽  
pp. 1-12
Author(s):  
Yong Chen ◽  
Senyuan Tian ◽  
Bingnan Sun
Keyword(s):  
Damage Detection ◽  
System Performance ◽  
Structural Damage ◽  
Model Updating ◽  
Experimental Studies ◽  
Decision Fusion ◽  
Experimental Results ◽  
Detection Methods ◽  
Correct Selection ◽  
Fusion Strategy

This paper describes a decision fusion strategy that can integrate multiple individual damage detection measures to form a new measure, and the new measure has higher probability of correct detection than any individual measure. The method to compute the probability of correct selection is presented to measure the system performance of the fusion system that includes the presented fusion strategy. And parametric sensitive studies on system performance are also conducted. The superiority of the fusion strategy herein is that it can be extended to deal with the multiresolution subdecision or blind adaptive detection, and corresponding methodologies are also provided. Finally, an experimental setup was fabricated, whereby the vibration properties of damaged and undamaged structures were measured. The experimental results with the undamaged structural model provide information for producing an improved theoretical and numerical model via model updating techniques. Three existing vibration-based damage detection methods with varied resolutions were utilized to identify the damage that occurred in the structure, based on the experimental results. Then the decision fusion strategy was implemented to join the subdecisions from these three methods. The fused results are shown to be superior to those from single method.

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