Earthquake engineering applications of structural health monitoring

2013 ◽  
Vol 42 (9) ◽  
pp. 1413-1414 ◽  
Keyword(s):  
Structural Health Monitoring ◽  
Earthquake Engineering ◽  
Health Monitoring ◽  
Engineering Applications ◽  
Structural Health

scholarly journals Sensors for Structural Health Monitoring and Condition Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1558
Author(s):  
Francesc Pozo ◽  
Diego A. Tibaduiza ◽  
Yolanda Vidal
Keyword(s):  
Structural Health Monitoring ◽  
Condition Monitoring ◽  
Health Monitoring ◽  
Structural Control ◽  
Engineering Applications ◽  
System Parameters ◽  
Structural Health ◽  
Agricultural Engineering ◽  
Physical Variables

Structural control and health monitoring as condition monitoring are some essential areas that allow for different system parameters to be designed, supervised, controlled, and evaluated during the system’s operation in different processes, such as those used in machinery, structures, and different physical variables in mechanical, chemical, electrical, aeronautical, civil, electronics, mechatronics, and agricultural engineering applications, among others [...]

Structural Health Monitoring of a Concrete Frame Subjected to Shake Table Excitations Using Smart Aggregates

2014 ◽  
Author(s):  
Wei Chang ◽  
Juin-Fu Chai ◽  
Wen-I Liao
Keyword(s):  
Structural Health Monitoring ◽  
Earthquake Engineering ◽  
Health Monitoring ◽  
Health Condition ◽  
Frame Structure ◽  
Shake Table ◽  
Rc Structures ◽  
Engineering Research ◽  
Structural Health ◽  
Concrete Frame

Structural health monitoring of RC structures under seismic loads has recently attracted dramatic attention in the earthquake engineering research community. In this paper, a piezoceramic-based device called “smart aggregate” was used for the health monitoring of a two stories one bay RC frame structure under earthquake excitations. The RC moment frame instrumented with smart aggregates was tested using a shake table with different ground excitation intensities. The distributed piezoceramic-based smart aggregates embedded in the RC structure were used to monitor the health condition of the structure during the tests. The sensitiveness and effectiveness of the proposed piezoceramic-based approach were investigated and evaluated by analyzing the measured responses.

Restoration and structural health monitoring of Manitoba's Golden Boy

2003 ◽  
Vol 30 (6) ◽  
pp. 1123-1132 ◽  
Author(s):  
Aftab A Mufti
Keyword(s):  
Structural Health Monitoring ◽  
Knowledge Base ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Paper Briefly ◽  
Engineering Applications ◽  
Engineering Structures ◽  
Structural Health ◽  
Existing Structures ◽  
History Of

Although bridges were among the first civil engineering structures to use structural health monitoring (SHM) technologies, research is now expanding to explore other types of applications, including Manitoba's famous Golden Boy statue. Global research is identifying the value of using SHM technologies for civil engineering applications. Structural health monitoring uses a variety of sensors to gather information about the behaviour of a structure. The information creates a valuable knowledge base that can be analyzed to help identify potential structural risks, develop safer and more efficient new structures, and determine more effective ways to rehabilitate existing structures. This paper briefly describes the history of the Manitoba Legislative Building and the Golden Boy and also the use of SHM technologies to help preserve the Golden Boy statue, an icon of provincial heritage.Key words: history, Golden Boy, statue, sculptors, architects, engineers, shaft, corrosion, sensors, monitoring.

scholarly journals Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3567 ◽  
Author(s):  
Xu ◽  
Yuan ◽  
Chen ◽  
Ren
Keyword(s):  
Neural Network ◽  
Fatigue Crack ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Health Monitoring ◽  
Guided Wave ◽  
Engineering Applications ◽  
Monitoring Method ◽  
Structural Health ◽  
High Level

Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investigated for FCD. However, reliable FCD still meets challenges, because uncertainties in real engineering applications usually cause serious change both to the crack propagation itself and GW monitoring signals. As one of deep learning methods, convolutional neural network (CNN) owns the ability of fusing a large amount of data, extracting high-level feature expressions related to classification, which provides a potential new technology to be applied in the GW-structural health monitoring method for crack evaluation. To address the influence of dispersion on reliable FCD, in this paper, a GW-CNN based FCD method is proposed. In this method, multiple damage indexes (DIs) from multiple GW exciting-acquisition channels are extracted. A CNN is designed and trained to further extract high-level features from the multiple DIs and implement feature fusion for crack evaluation. Fatigue tests on a typical kind of aircraft structure are performed to validate the proposed method. The results show that the proposed method can effectively reduce the influence of uncertainties on FCD, which is promising for real engineering applications.

Structural Health Monitoring for Local Damages of RC Walls Using Piezoceramic-Based Sensors Under Seismic Loading

2017 ◽  
Author(s):  
Wen-I Liao ◽  
Wen-Yu Jean
Keyword(s):  
Structural Health Monitoring ◽  
Earthquake Engineering ◽  
Health Monitoring ◽  
Shear Walls ◽  
Seismic Loading ◽  
The Other ◽  
Loading Test ◽  
Rc Structures ◽  
Engineering Research ◽  
Structural Health

Structural health monitoring of reinforced concrete (RC) structures under seismic loads have recently attracted dramatic attention in the earthquake engineering research community. In this study, reversed cyclic loading test of structural health monitoring of RC shear walls using piezoceramic (PZT)-based sensors are presented. The piezoceramic-based sensors called “smart aggregate (SA)”, was pre-embedded before casting of concrete and adopted for the structural health monitoring of the RC shear wall under seismic loading. Two RC walls were adopted in this test, one is the wall having damages in the boundary columns and foundation of the specimen, and the other is the wall having damages in the upper part of the wall panel. During the test, SAs embedded in the foundation were used as actuators to generate propagating waves, and the other selected SAs were used to detect the waves. By analyzing the wave response, the existence and locations of cracks and damages can be detected and the severity can be estimated. The experimental results demonstrate the sensitiveness and the effectiveness of the piezoceramic-based approach in the structural health monitoring and the identification of damage locations of shear governed concrete structures under seismic loading.

scholarly journals Digital image correlation for structural health monitoring – a review

2021 ◽  
Vol 309 ◽  
pp. 01176
Author(s):  
K Tharun Kumar Reddy ◽  
Srikanth Koniki
Keyword(s):  
Structural Health Monitoring ◽  
Digital Image Correlation ◽  
Digital Image ◽  
Health Monitoring ◽  
Vital Role ◽  
Image Correlation ◽  
Detailed Knowledge ◽  
Engineering Applications ◽  
Detailed Review ◽  
Structural Health

This paper presents a detailed review of the use of digital image correlation on structural health monitoring. The durability of structures like bridges and buildings etc., need to monitor continuously. Structural health monitoring plays a vital role in assets and planning. Detailed knowledge on digital image correlation concerning structural health monitoring and civil engineering applications is presented in this paper.

NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING

2019 ◽  
Vol 2 (Special Issue on First SACEE'19) ◽  
pp. 77-112 ◽  
Author(s):  
Khalid Mosalam ◽  
Sifat Muin ◽  
Yuqing Gao
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Earthquake Engineering ◽  
Health Monitoring ◽  
Damage Identification ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Engineering Research ◽  
Structural Health ◽  
Cumulative Absolute Velocity

This paper presents two on-going efforts of the Pacific Earthquake Engineering Research (PEER) center in the area of structural health monitoring. The first is data-driven damage assessment, which focuses on using data from instrumented buildings to compute the values of damage features. Using machine learning algorithms, these damage features are used for rapid identification of the level and location of damage after earthquakes. One of the damage features identified to be highly efficient is the cumulative absolute velocity. The second is vision-based automated damage identification and assessment from images. Deep learning techniques are used to conduct several identification tasks from images, examples of which are the structural component type, and level and type of damage. The objective is to use crowdsourcing, allowing the general public to take photographs of damage and upload them to a server where damage is automatically identified using deep learning algorithms. The paper also introduces PEER.s effort and preliminary results in engaging the engineering and computer science communities in such developments through the PEER Hub Image-Net (F-Net) challenge.

Sign in / Sign up

Export Citation Format

Share Document