scholarly journals Implementation of a Probabilistic Structural Health Monitoring Method on a Highway Bridge

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Adam Scianna ◽  
Zhaoshuo Jiang ◽  
Richard Christenson ◽  
John DeWolf
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Probability Distributions ◽  
Highway Bridge ◽  
Monitoring Method ◽  
Operational Conditions ◽  
Structural Health ◽  
Bridge Superstructure ◽  
Girder Bridge

This paper describes the application of a probabilistic structural health monitoring (SHM) method to detect global damage in a highway bridge in Connecticut. The proposed method accounts for the variability associated with environmental and operational conditions. The bridge is a curved three-span steel dual-box girder bridge located in Hartford, Connecticut. The bridge, monitored since Fall 2001, experienced a period of settling in the Winter of 2002-2003. While this change was not associated with structural damage, it was observed in a permanent rotation of the bridge superstructure. Three damage measures are identified in this study: the value of fundamental natural frequency determined from peak picking of autospectral density functions of the bridge acceleration measurements; the magnitude of the peak acceleration measured during a truck crossing; the magnitude of the tilt measured at 10-minute intervals. These damage measures, including thermal effects, are shown to be random variables and associatedPvalues are calculated to determine if the current probability distributions are the same as the distributions of the baseline bridge data from 2001. Historical data measured during the settling of the bridge is used to verify the performance of the bridge, and the field implementation of the proposed method is described.

Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings

2013 ◽  
Vol 390 ◽  
pp. 192-197
Author(s):  
Giorgio Vallone ◽  
Claudio Sbarufatti ◽  
Andrea Manes ◽  
Marco Giglio
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Numerical Models ◽  
Damage State ◽  
Operational Conditions ◽  
Structural Health ◽  
Artificial Neural

The aim of the current paper is to explore fuselage monitoring possibilities trough the usage of Artificial Neural Networks (ANNs), trained by the use of numerical models, during harsh landing events. A harsh landing condition is delimited between the usual operational conditions and a crash event. Helicopter structural damage due to harsh landings is generally less severe than damage caused by a crash but may lead to unscheduled maintenance events, involving costs and idle times. Structural Health Monitoring technologies, currently used in many application fields, aim at the continuous detection of damage that may arise, thereby improving safety and reducing maintenance idle times by the disposal of a ready diagnosis. A landing damage database can be obtained with relatively little effort by the usage of a numerical model. Simulated data are used to train various ANNs considering the landing parameter values as input. The influence of both the input and output noise on the system performances were taken into account. Obtained outputs are a general classification between damaged and undamaged conditions, based on a critical damage threshold, and the reconstruction of the fuselage damage state.

Structural Health Monitoring of existing structures and infrastructures combining on-site and satellite data

2020 ◽  
Author(s):  
Felice Carlo Ponzo ◽  
Rocco Ditommaso
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Rapid Evolution ◽  
Mode Shapes ◽  
Operational Conditions ◽  
Structural Health ◽  
Existing Structures ◽  
Research Fields ◽  
Over Time

<p>In the last years, existing structures and transport infrastructures, especially those made using reinforced concrete, have experienced significant safety criticalities implying also a relevant social and economic impacts. Structural Health Monitoring techniques represent a reliable response to the problem available to scientists and engineers. A multidisciplinary approach combing knowledge from several research fields and using different kind of technologies would be preferable for this type of application. Most of developed methods for structural damage detection on civil structures and infrastructures is generally based on the evaluation of displacements, eigenfrequencies, damping factors, mode shapes, etc., and their variation over time, by means of on-site installed sensors. In recent years, thanks to the rapid evolution of interferometric SAR processing techniques, a large amount of “satellite measurements” are available for both geophysical phenomena and building monitoring in terms of displacement rate over time. This paper presents an overview on the 2019-2021 WP6 Reluis Project aiming to contribute to the discussion about the opportunity and the modalities to merge information retrieved both by on-site and remote sensing measurements and to define a shared strategy to detect damage on existing structures and infrastructures in operational conditions.</p>

scholarly journals An overview of 38 least squares–based frameworks for structural damage tomography

2019 ◽  
Vol 19 (1) ◽  
pp. 215-239 ◽  
Author(s):  
Danny Smyl ◽  
Sven Bossuyt ◽  
Waqas Ahmad ◽  
Anton Vavilov ◽  
Dong Liu
Keyword(s):  
Structural Health Monitoring ◽  
Least Squares ◽  
Health Monitoring ◽  
Structural Damage ◽  
Tomographic Imaging ◽  
Inverse Methods ◽  
One Dimension ◽  
Distributed Data ◽  
Structural Health ◽  
Static Elasticity

The ability to reliably detect damage and intercept deleterious processes, such as cracking, corrosion, and plasticity are central themes in structural health monitoring. The importance of detecting such processes early on lies in the realization that delays may decrease safety, increase long-term repair/retrofit costs, and degrade the overall user experience of civil infrastructure. Since real structures exist in more than one dimension, the detection of distributed damage processes also generally requires input data from more than one dimension. Often, however, interpretation of distributed data—alone—offers insufficient information. For this reason, engineers and researchers have become interested in stationary inverse methods, for example, utilizing distributed data from stationary or quasi-stationary measurements for tomographic imaging structures. Presently, however, there are barriers in implementing stationary inverse methods at the scale of built civil structures. Of these barriers, a lack of available straightforward inverse algorithms is at the forefront. To address this, we provide 38 least-squares frameworks encompassing single-state, two-state, and joint tomographic imaging of structural damage. These regimes are then applied to two emerging structural health monitoring imaging modalities: Electrical Resistance Tomography and Quasi-Static Elasticity Imaging. The feasibility of the regimes are then demonstrated using simulated and experimental data.

Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

2021 ◽  
pp. 136943322110384
Author(s):  
Xingyu Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Low Frequency ◽  
Data Driven ◽  
Mode Shapes ◽  
Monitoring Methods ◽  
Destructive Testing ◽  
Electromechanical Impedance ◽  
Structural Health

Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

Sensor Network for Structural Health Monitoring of a Highway Bridge

2010 ◽  
Vol 24 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Michael Fraser ◽  
Ahmed Elgamal ◽  
Xianfei He ◽  
Joel P. Conte
Keyword(s):  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Highway Bridge ◽  
Structural Health

scholarly journals Structural Health Monitoring Method of Pantograph–Catenary System Based on Strain Response Inversion

2021 ◽  
Vol 9 ◽  
Author(s):  
Sheng Liu ◽  
Yibo Wei ◽  
Yongxin Yin ◽  
Tangzheng Feng ◽  
Jinbao Lin
Keyword(s):  
Structural Health Monitoring ◽  
Contact Force ◽  
Health Monitoring ◽  
Data Consistency ◽  
Inversion Method ◽  
Maximum Speed ◽  
Strain Response ◽  
Monitoring Method ◽  
Structural Health ◽  
Catenary System

Pantograph-catenary system provides electric energy for the subway lines; its health status is essential to the serviceability of the vehicle. In this study, a real-time structural health monitoring method based on strain response inversion is proposed to calculate the magnitude and position of the dynamic contact force between the catenary and pantograph. The measurement principle, calibration, and installation detail of the fiber Bragg grating (FBG) sensors are also presented in this article. Putting this monitoring system in use, an application example of a subway with a rigid overhead catenary is given to demonstrate its performance. The pantograph was monitored and analyzed, running underground at a maximum speed of 80 km/h. The results show that the strain response inversion method has high measurement accuracy, good data consistency, and flexibility on sensor installation. It can accurately calculate the magnitude and location of the contact force exerted on the pantograph.

scholarly journals Research on the Progress of Interdigital Transducer (IDT) for Structural Damage Monitoring

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wang Ziping ◽  
Xiong Xiqiang ◽  
Qian Lei ◽  
Wang Jiatao ◽  
Fei Yue ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Large Scale ◽  
Safety Information ◽  
Health And Safety ◽  
Guided Wave ◽  
Structural Safety ◽  
Structural Health ◽  
Damage Monitoring

In the application of Structural Health Monitoring (SHM) methods and related technologies, the transducer used for electroacoustic conversion has gradually become a key component of SHM systems because of its unique function of transmitting structural safety information. By comparing and analyzing the health and safety of large-scale structures, the related theories and methods of Structural Health Monitoring (SHM) based on ultrasonic guided waves are studied. The key technologies and research status of the interdigital guided wave transducer arrays which used for structural damage detection are introduced. The application fields of interdigital transducers are summarized. The key technical and scientific problems solved by IDT for Structural Damage Monitoring (SHM) are presented. Finally, the development of IDT technology and this research project are summarised.

Structural Health Monitoring With Piezoelectric Active Sensors

2000 ◽  
Author(s):  
Howard A. Winston ◽  
Fanping Sun ◽  
Balkrishna S. Annigeri
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Electromechanical Coupling ◽  
Low Cost ◽  
Piezoelectric Element ◽  
Impedance Spectrum ◽  
Active Sensors ◽  
Electromechanical Impedance ◽  
Structural Health

A technology for non-intrusive real-time structural health monitoring using piezoelectric active sensors is presented. The approach is based on monitoring variations of the coupled electromechanical impedance of piezoelectric patches bonded to metallic structures in high-frequency bands. In each of these applications, a single piezoelectric element is used as both an actuator and a sensor. The resulting electromechanical coupling makes the frequency-dependent electric impedance spectrum of the PZT sensor a good mapping of the underlying structure’s acoustic signature. Moreover, incipient structural damage can be indicated by deviations of this signature from its original baseline pattern. Unique features of this technology include its high sensitivity to structural damage, non-intrusiveness to the host structure, and low cost of implementation. These features have potential for enabling on-board damage monitoring of critical or inaccessible aerospace structures and components, such as aircraft wing joints, and both internal and external jet engine components. Several exploratory applications will be discussed.

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