On the use of symbolic vibration data for robust structural health monitoring

2016 ◽  
Vol 169 (9) ◽  
pp. 715-723 ◽  
Author(s):  
Vinicius Alves ◽  
Alexandre Cury ◽  
Christian Cremona
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Vibration Data

scholarly journals Assessment of low-cost wireless sensors for structural health monitoring applications

2021 ◽  
Vol 14 (2) ◽  
Author(s):  
Emerson Toledo Júnior ◽  
Alexandre Cury ◽  
Jánes Landre Júnior
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Low Cost ◽  
Tall Buildings ◽  
Raspberry Pi ◽  
Structural Health ◽  
Vibration Data ◽  
Structural Problems ◽  
Implementation Costs ◽  
Wireless Sensing System

Abstract Structural Health Monitoring (SHM) programs play an essential task in the field of civil engineering, especially for assessing safety conditions involving large structures such as viaducts, bridges, stadiums, and tall buildings. In fact, some of these structures are monitored 24 hours a day, 7 days a week, to supply dynamic measurements that can be used for the identification of structural problems, e.g., presence of cracks, excessive vibration, damage, among others. SHM programs may provide automated assessment of structural health by processing vibration data obtained from sensors attached to the structure. Frequently, SHM uses wired systems, which are usually expensive due to the necessity of continuous maintenance and are not always suitable for sensing remote structures. Conversely, commercial wireless systems often demand high implementation costs. Hence, this paper proposes the use of a low-cost wireless sensing system based on the single board computer Raspberry Pi, which significantly reduces implementation expenses while keeping data’s integrity. The wireless communication is performed in real-time through a local wireless network, responsible for sending and receiving vibration data. The proposed system is validated by comparing its results with a commercial wired system through a series of controlled experimental applications. The results suggest that the proposed system is suitable for civil SHM applications.

A Simplified Methodology for Condition Assessment of Bridge Bearings Using Vibration Based Structural Health Monitoring Techniques

2021 ◽  
pp. 2150133
Author(s):  
Babar Nasim Khan Raja ◽  
Saeed Miramini ◽  
Colin Duffield ◽  
Shilun Chen ◽  
Lihai Zhang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Condition ◽  
Condition Assessment ◽  
Concrete Bridges ◽  
Traffic Loading ◽  
Structural Health ◽  
Vibration Data ◽  
Bridge Bearing ◽  
Bridge Bearings

The mechanical properties of bridge bearings gradually deteriorate over time resulting from daily traffic loading and harsh environmental conditions. However, structural health monitoring of in-service bridge bearings is rather challenging. This study presents a bridge bearing condition assessment framework which integrates the vibration data from a non-contact interferometric radar (i.e. IBIS-S) and a simplified analytical model. Using two existing concrete bridges in Australia as a case study, it demonstrates that the developed framework has the capability of detecting the structural condition of the bridge bearings in real-time. In addition, the results from a series of parametric studies show that the effectiveness of the developed framework is largely determined by the stiffness ratio between bridge bearing and girder ([Formula: see text], i.e. the structural condition of the bearings can only be effectively captured when the value of [Formula: see text] ranges from 1/100 and 100.

Application of Compressive Sampling for Accelerometer Signals Used in Structural Health Monitoring

2009 ◽  
Vol 413-414 ◽  
pp. 455-462 ◽  
Author(s):  
Yue Quan Bao ◽  
James L. Beck ◽  
Hui Li
Keyword(s):  
Structural Health Monitoring ◽  
Data Compression ◽  
Health Monitoring ◽  
Yellow River ◽  
Compressive Sampling ◽  
Sensor Data ◽  
Huffman Coding ◽  
Civil Structures ◽  
Structural Health ◽  
Vibration Data

In structural health monitoring (SHM) of civil structures, data compression is often needed for saving the cost of data transfer and storage because of the large volumes of sensor data generated from the monitoring system. The traditional framework for data compression is to first sample the full signal, then to compress it. Recently, a new data compression method named compressive sampling (CS) has been presented, that can acquire the data directly in compressed form by using special sensors. In this paper, the potential of CS for data compression of vibration data is investigated using simulation of the CS sensor algorithm. The acceleration data collected from the SHM system of Shandong Binzhou Yellow River Highway Bridge is used to analyse the data compression ability of CS. For comparison, the wavelet transform based and Huffman coding methods are also employed to compress the data. The results show that CS is useful for compression of vibration data in SHM of civil structures.

scholarly journals Structural Health Monitoring for Condition Assessment Using Efficient Supervised Learning Techniques

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 17 ◽  
Author(s):  
Alireza Entezami ◽  
Hashem Shariatmadar ◽  
Stefano Mariani
Keyword(s):  
Structural Health Monitoring ◽  
Supervised Learning ◽  
Health Monitoring ◽  
Learning Strategy ◽  
Structural Condition ◽  
Condition Assessment ◽  
Statistical Characteristics ◽  
Classification Methods ◽  
Structural Health ◽  
Vibration Data

Pattern recognition can be adopted for structural health monitoring (SHM) based on statistical characteristics extracted from raw vibration data. Structural condition assessment is an important step of SHM, since changes in the relevant properties may adversely affect the behavior of any structure. It looks therefore necessary to adopt efficient and robust approaches for the classification of different structural conditions using features extracted from the said raw data. To achieve this goal, it is essential to correctly distinguish the undamaged and damage states of the structure; the aim of this work is to present and compare classification methods using feature selection techniques to classify the structural conditions. All of the utilized classifiers need a training set pertinent to the undamaged/damaged conditions of the structure, as well as relevant class labels to be adopted in a supervised learning strategy. The performance and accuracy of the considered classification methods are assessed through a numerical benchmark concrete beam.

Structural Health Monitoring of a Vertical Lift Bridge Using Vibration Data

2018 ◽  
Author(s):  
Vahid Shahsavari ◽  
◽  
Milad Milad Mehrkash ◽  
Erin Bell
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Vibration Data ◽  
Vertical Lift

Cyberinfrastructure Middleware and Analytical Tool Sets for Automated Mining of Massive Structural Health Monitoring Data Sets

2013 ◽  
Author(s):  
Y. Zhang ◽  
S. M. O'Connor ◽  
J. P. Lynch ◽  
G. van der Linden ◽  
A. Prakash
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Analytical Tool ◽  
Monitoring Data ◽  
Data Sets ◽  
Structural Health
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