Dynamics-based model-independent local inspection method for damage detection of large structures

2005 ◽  
Author(s):  
P. Frank Pai ◽  
Lu Huang
Keyword(s):  
Damage Detection ◽  
Inspection Method ◽  
Large Structures ◽  
Model Independent

Ultrasonic/Guided Waves for Structural Health Monitoring

2005 ◽  
Vol 293-294 ◽  
pp. 49-62 ◽  
Author(s):  
W.J. Staszewski
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Guided Waves ◽  
Ultrasonic Waves ◽  
Detection Methods ◽  
Smart Sensor ◽  
Large Structures ◽  
Guided Ultrasonic Waves ◽  
Damage Monitoring ◽  
Recent Developments

Structural damage detection and monitoring is one of the major maintenance activities in transportation, processing and civil engineering. Current procedures are based on scheduled inspections which are often time/labour consuming and expensive. Guided ultrasonic waves offer the ability of inspecting large structures with a small number of transducers. Recent developments in smart sensor technologies allow for integration of these transducers with monitored structures. This is associated with a new design philosophy leading to more efficient and economically attractive structures. The paper briefly discusses various damage detection methods based on structural, ultrasonic and guided ultrasonic waves. The focus is on recent research advances in damage monitoring techniques, smart sensor technologies and signal processing.

scholarly journals Migration Learning-Based Bridge Structure Damage Detection Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ruling Chen
Keyword(s):  
Damage Detection ◽  
Data Augmentation ◽  
Detection Algorithm ◽  
Urban Traffic ◽  
Training Data ◽  
Detection Methods ◽  
Inspection Method ◽  
Structure Damage ◽  
The Road ◽  
The World

Most of the bridge structures in the world are built of reinforced concrete. With the growth of service life and the increase of urban traffic and other factors, most bridges put into service have more or less damage. Traditional bridge damage detection methods include the manual inspection method and bridge inspection vehicle method, which have many shortcomings. Moreover, the detection of cracks in bridges is critical to the safety of transportation due to the extremely large number of bridges built in the road networks across the world. To this end, this paper uses the most widely used CNN in deep learning to identify and classify crack images and proposes a migration learning technique to solve the problem of the large amount of training data required for training CNN. The data augmentation and sliding window techniques are introduced to divide the collected crack data into training establish and test set. The experiments show that the method in this paper can classify the crack images better, extract and locate the cracks of bridge crack units, and finally extract the crack coordinates of boxing. Compared with the customary image recognition methods, the method used in this paper is easier to operate in practical engineering, and the accuracy of the obtained results is higher.

Structural Health Monitoring (SHM) of Offshore Jacket Platforms

2011 ◽  
Author(s):  
Mohammad Ali Lotfollahi-Yaghin ◽  
Sajad Shahverdi ◽  
Reza Tarinejad ◽  
Behrouz Asgarian
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Damage Assessment ◽  
Structural Damage ◽  
Wavelet Packet ◽  
Structural Health ◽  
Finite Element Software ◽  
Modal Properties ◽  
Large Structures

In the present paper, Structural health monitoring has become an evolving area of research in last few decades with increasing need of online monitoring the health of large structures. The damage detection by visual inspection of the structure can prove impractical, expensive and ineffective in case of large structures like offshore platforms, multistoried buildings and bridges. Structural health monitoring is defined as the process of detecting damage in a structural system. Damage in the system causes a change in dynamic properties of a system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require the modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such a good sensitive indication of structural damage. Structural damage detection and damage localization of jacket platforms, based on wavelet packet transforms is presented in this paper. Dynamic signals measured from the structure by the finite element software package ANSYS are first decomposed into wavelet packet components. Component energies are then calculated and used for damage assessment. The results show that the WPT-based component energies are good candidate indices that are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and location.

scholarly journals Analysis of Piezoelectric Diaphragms in Impedance-Based Damage Detection in Large Structures

Proceedings ◽  
2017 ◽  
Vol 2 (3) ◽  
pp. 131 ◽  
Author(s):  
Danilo Budoya ◽  
Bruno de Castro ◽  
Leandro Campeiro ◽  
Ricardo da Silveira ◽  
Everaldo de Freitas ◽  
...  
Keyword(s):  
Damage Detection ◽  
Large Structures

scholarly journals Damage Detection and Localization from Dense Network of Strain Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Simon Laflamme ◽  
Liang Cao ◽  
Eleni Chatzi ◽  
Filippo Ubertini
Keyword(s):  
Damage Detection ◽  
Damage Identification ◽  
Low Cost ◽  
Strain Sensors ◽  
Dense Network ◽  
Virtual Sensors ◽  
Large Structures ◽  
Complex Engineering ◽  
Detection And Localization

Structural health monitoring of large systems is a complex engineering task due to important practical issues. When dealing with large structures, damage diagnosis, localization, and prognosis necessitate a large number of sensors, which is a nontrivial task due to the lack of scalability of traditional sensing technologies. In order to address this challenge, the authors have recently proposed a novel sensing solution consisting of a low-cost soft elastomeric capacitor that transduces surface strains into measurable changes in capacitance. This paper demonstrates the potential of this technology for damage detection, localization, and prognosis when utilized in dense network configurations over large surfaces. A wind turbine blade is adopted as a case study, and numerical simulations demonstrate the effectiveness of a data-driven algorithm relying on distributed strain data in evidencing the presence and location of damage, and sequentially ranking its severity. Numerical results further show that the soft elastomeric capacitor may outperform traditional strain sensors in damage identification as it provides additive strain measurements without any preferential direction. Finally, simulation with reconstruction of measurements from missing or malfunctioning sensors using the concepts of virtual sensors and Kriging demonstrates the robustness of the proposed condition assessment methodology for sparser or malfunctioning grids.

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