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.

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.

scholarly journals Structural Health Monitoring and Interface Damage Detection for Infill Reinforced Concrete Walls in Seismic Retrofit of Reinforced Concrete Frames Using Piezoceramic-Based Transducers Under the Cyclic Loading

2019 ◽  
Vol 9 (2) ◽  
pp. 312 ◽  
Author(s):  
Wen-I Liao ◽  
Fu-Pei Hsiao ◽  
Chien-Kuo Chiu ◽  
Chin-En Ho
Keyword(s):  
Reinforced Concrete ◽  
Cyclic Loading ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
The Other ◽  
Structural Members ◽  
Interface Failure ◽  
Loading Test ◽  
Structural Health ◽  
Interface Damage

In this work, the piezoceramic-based transducers are used to perform the structural health monitoring (SHM) and interface damage detecting of non-ductile reinforced concrete (RC) frames retrofitted by post-installed RC walls. In order to develop the post-embedded piezoceramic-based transducers that can be used to identify interface failure or cracks between two structural members in retrofit construction, this work adopts the cyclic loading to test two specimens with post-embedded piezoceramic-based transducers (PPT). Since the failure of an interface between the post-installed wall and beam occurs, one of the specimens has damage in the foundation and existing boundary column and the other has damage in the top ends of column and wall. During the cyclic loading test, one transducer was used as an actuator to generate the stress waves and the other transducers were used as the sensors to detect the waves. In damaged specimens, the existence and locations of cracks and the interface damage can be detected by analyzing the wave response. Moreover, the severity of damage to the specimens can also be estimated. The experimental results indicate the effectiveness of the piezoceramic-based approach in the SHM and locating damage in shear-critical RC structural members under the seismic loading.

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.

Federal Aviation Administration’s Probability of Detection Testing Results for Structural Health Monitoring (SHM)

2021 ◽  
Author(s):  
Paul Swindell ◽  
Danielle Stephens
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Federal Aviation Administration ◽  
Steering Committee ◽  
Probability Of Detection ◽  
The Other ◽  
Atlantic City ◽  
Commercial Aviation ◽  
Structural Health ◽  
Set Up

Abstract The Federal Aviation Administration (FAA) has been participating with the Society of Automotive Engineers (SAE) Aerospace Industry Steering Committee (AISC) to develop a methodology for calculating the Probability of Detection (POD) for Structural Health Monitoring (SHM) for damage detection on commercial aviation. Two POD methodologies were developed: one by Dr. William Meeker, Iowa State University, and the other by Dennis Roach, Sandia National Laboratories (SNL). With Dr. Seth Kessler, Metis Design Corp, a test program of 24 samples of aluminum strips to be fatigued on MTS machines was developed. The samples were designed to meet the ASTM E647. Twelve samples had two SHM modalities on the front and back from Metis (PZT and carbon nanotubes), and the other twelve had SHM sensors from Structural Monitoring Systems (SMS) (comparative vacuum monitoring – CVM) and Acellent Technologies (PZT). The tests were performed at the FAA William J Hughes Technical Center in Atlantic City, NJ. The samples were cycled every 1500 cycles and then stopped for SHM data collection. Once the crack exceeded 0.125 inches and provided for a minimum of 15 inspections, a new sample was tested until all 12 samples were completed. The data was provided to each company to be set up in the format needed to run through the POD methodologies. Then the data was provided to Dr. Meeker and Dr. Roach for analysis. This paper will provide the results of those tests.

Feature Engineering for Structural Health Monitoring (SHM)

2021 ◽  
pp. 138-163
Author(s):  
Esraa Elhariri ◽  
Nashwa El-Bendary ◽  
Shereen A. Taie
Keyword(s):  
Computer Vision ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Emerging Technology ◽  
The Other ◽  
Feature Engineering ◽  
Future Directions ◽  
Structural Health ◽  
Computational Perspective

Feature engineering is a key component contributing to the performance of the computer vision pipeline. It is fundamental to several computer vision tasks such as object recognition, image retrieval, and image segmentation. On the other hand, the emerging technology of structural health monitoring (SHM) paved the way for spotting continuous tracking of structural damage. Damage detection and severity recognition in the structural buildings and constructions are issues of great importance as the various types of damages represent an essential indicator of building and construction durability. In this chapter, the authors connect the feature engineering with SHM processes through illustrating the concept of SHM from a computational perspective, with a focus on various types of data and feature engineering methods as well as applications and open venues for further research. Challenges to be addressed and future directions of research are presented and an extensive survey of state-of-the-art studies is also included.

scholarly journals Structural Health Monitoring of RC structures using optic fiber strain measurements: a deep learning approach

2019 ◽  
Author(s):  
Dimitrios F. Karypidis ◽  
Carlos G. Berrocal ◽  
Rasmus Rempling ◽  
Mats Granath ◽  
Peter Simonsson
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Reinforced Concrete Beam ◽  
Transport Infrastructure ◽  
Optic Fiber ◽  
Transverse Cracks ◽  
Fiber System ◽  
Rc Structures ◽  
Structural Health ◽  
New Methods

<p>This paper reports the early findings of an ongoing project aimed at developing new methods to upgrade the current maintenance strategies of the civil and transport infrastructure. As part of these new methods, the use of Machine Learning (ML) algorithms is being investigated to constitute the core of a new generation of more accurate and robust structural health monitoring (SHM) systems for concrete structures. Unlike most of the existing SHM systems, relying on the analysis of the natural frequencies of the structure based on data obtained from accelerometers, the present study uses a distributed optic fiber system to monitor the strain distribution along steel reinforcing bars. The preliminary results of the study indicate that a semi-supervised Deep Autoencoder algorithm (DAE) can successfully quantify the damage attributable to transverse cracks in a reinforced concrete beam subjected to three-point loading. Future applications will feature the determination of crack locations, early detection of reinforcement corrosion as well as other types of damage such as splitting cracks or surface spalling.</p>

Health Monitoring of RC Frame Structure to Shake Table Excitations

2011 ◽  
Vol 287-290 ◽  
pp. 2776-2780
Author(s):  
Wen I Liao ◽  
Jenn Shin Hwang ◽  
Yu Chi Sung ◽  
Shiang Jung Wang
Keyword(s):  
Earthquake Engineering ◽  
Health Monitoring ◽  
Health Condition ◽  
Frame Structure ◽  
Shake Table ◽  
Rc Frame ◽  
Moment Frame ◽  
Rc Structures ◽  
Engineering Research ◽  
Rc Frame Structures

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

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