scholarly journals Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

2018 ◽  
Vol 7 (3) ◽  
pp. 30 ◽  
Author(s):  
Chiara Bedon ◽  
Enrico Bergamo ◽  
Matteo Izzi ◽  
Salvatore Noè
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Full Scale ◽  
Shaking Table ◽  
Efficient Design ◽  
Dynamic Identification ◽  
Structural Health ◽  
Cable Stayed Bridge ◽  
Mems Accelerometers

In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.

Non-contact structural health monitoring of a cable-stayed bridge: case study

2019 ◽  
Vol 15 (8) ◽  
pp. 1119-1136 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Linlin Ge ◽  
Kamyar Kildashti ◽  
Yincai Zhou ◽  
Bruce Harvey ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Cable Stayed Bridge

Foundation structural health monitoring of an offshore wind turbine—a full-scale case study

2016 ◽  
Vol 15 (4) ◽  
pp. 389-402 ◽  
Author(s):  
Wout Weijtjens ◽  
Tim Verbelen ◽  
Gert De Sitter ◽  
Christof Devriendt
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Health Monitoring ◽  
Full Scale ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Structural Health

The 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020): A summary and benchmark problem

2021 ◽  
pp. 147592172110064
Author(s):  
Yuequan Bao ◽  
Jian Li ◽  
Tomonori Nagayama ◽  
Yang Xu ◽  
Billie F Spencer ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Undergraduate Students ◽  
Health Monitoring ◽  
Fatigue Cracks ◽  
Full Scale ◽  
Asia Pacific ◽  
International Project ◽  
Full Time ◽  
Structural Health ◽  
Institute Of Technology

To promote the development of structural health monitoring around the world, the 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020) was initiated and organized in 2020 by the Asia-Pacific Network of Centers for Research in Smart Structures Technology, Harbin Institute of Technology, the University of Illinois at Urbana-Champaign, and four leading companies in the application of structural health monitoring technology. The goal of this competition was to attract more young scholars to engage in the study of structural health monitoring, encouraging them to provide creative and effective solutions for full-scale applications. Recognizing the recent advent and importance of artificial intelligence in structural health monitoring, three competition projects were set up with the data from full-scale bridges: (1) image-based identification of fatigue cracks in bridge girders, (2) data anomaly detection for structural health monitoring, and (3) condition assessment of stay cables using cable tension data. Three corresponding data sets were released at http://www.schm.org.cn and http://sstl.cee.illinois.edu/ipc-shm2020 . Participants were required to be full-time undergraduate students, M.S. students, Ph.D. students, or young scholars within 3 years after obtaining their Ph.D. Both individual and teams (each team had no more than five individuals) could compete. Submissions for the competition included a 10- to 15-page technical paper, a 10-min presentation video with PowerPoint slides, and commented code. The organizing committee then conducted the validation, review, and evaluation. A total of 330 participants in 112 teams from 70 universities and institutions in 12 countries registered for the competition, resulting in 75 papers from 56 teams from 57 different affiliations finally being submitted. Of those submitted, 31, 30, and 14 papers were for Projects 1, 2, and 3, respectively. After completion of the review by the organization committee and awards committee, the top 10, 10, and 5 teams were selected as the prize winners for the three competition projects.

Wireless structural health monitoring of cable-stayed bridge using Imote2-platformed smart sensors

2012 ◽  
Author(s):  
Duc-Duy Ho ◽  
Khac-Duy Nguyen ◽  
Po-Young Lee ◽  
Dong-Soo Hong ◽  
So-Young Lee ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Smart Sensors ◽  
Structural Health ◽  
Cable Stayed Bridge

scholarly journals Structural reliability assessment based on optical monitoring system: case study

2016 ◽  
Vol 9 (2) ◽  
pp. 297-305 ◽  
Author(s):  
E. Mesquita ◽  
P. Antunes ◽  
A. A. Henriques ◽  
A. Arêde ◽  
P. S. André ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Reliability ◽  
Extreme Values ◽  
Low Cost ◽  
Reliability Assessment ◽  
Water Reservoir ◽  
Optical Systems ◽  
Structural Health

ABSTRACT Optical systems are recognized to be an important tool for structural health monitoring, especially for real time safety assessment, due to simplified system configuration and low cost when compared to regular systems, namely electrical systems. This work aims to present a case study on structural health monitoring focused on reliability assessment and applying data collected by a simplified optical sensing system. This way, an elevated reinforced concrete water reservoir was instrumented with a bi-axial optical accelerometer and monitored since January 2014. Taking into account acceleration data, the natural frequencies and relative displacements were estimated. The reliability analysis was performed based on generalized extreme values distribution (GEV) and the results were employed to build a forecast of the reliability of the water elevated reservoir for the next 100 years. The results showed that the optical system combined with GEV analysis, implemented in this experimental work, can provide adequate data for structural reliability assessment.

scholarly journals A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

2018 ◽  
Vol 18 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Nguyen Lu Dang Khoa ◽  
Yang Wang ◽  
Bijan Samali ◽  
Xinqun Zhu
Keyword(s):  
Data Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Damage Identification ◽  
Tensor Analysis ◽  
Support Vector ◽  
Damage Scenarios ◽  
Structural Health ◽  
Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

scholarly journals A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2955 ◽  
Author(s):  
Mario de Oliveira ◽  
Andre Monteiro ◽  
Jozue Vieira Filho
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Lead Zirconate ◽  
Industrial Applications ◽  
Electromechanical Impedance ◽  
Structural Health

Preliminaries convolutional neural network (CNN) applications have recently emerged in structural health monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT-(lead zirconate titanate) based method and CNN. Likewise, applications using CNN along with the electromechanical impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of four types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

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