Fusion of structural damage identification results from different test scenarios and evaluation indices in structural health monitoring

2020 ◽  
pp. 147592172096216
Author(s):  
XY Li ◽  
SJ Lin ◽  
SS Law ◽  
YZ Lin ◽  
JF Lin
Keyword(s):  
Structural Health Monitoring ◽  
Impulse Response ◽  
Response Function ◽  
Health Monitoring ◽  
Structural Damage ◽  
Impulse Response Function ◽  
Identification Algorithm ◽  
Structural Health ◽  
Damage Indices ◽  
Test Parameters

There are many existing algorithms and damage indices that can effectively meet the engineering need in damage diagnosis. However, all of them (except those with an exact formulation) are based on certain assumptions with approximations, and their performances depend on the combination of test parameters, for example, type, number, and location of excitations and sensors; identification algorithm adopted; and environmental noise effect. Results obtained from different combinations of these test parameters are different and they may be mathematically biased or contradicting. Existing practice adopts the general indication of result for further maintenance decisions or by subjective screening the results with engineering sense. This article demonstrates the possibility to have a better design of the structural health monitoring system where the benefits of different damage indices and evaluation methodologies can be reaped via the fusion of the identified results. The scenario for discussion is the vibration-based damage localization problem. The change in mode shape in the modal domain and change in the covariance of impulse response function change in the time domain are selected as damage indices for the illustration. An exact form of the covariance of impulse response function is also proposed for this purpose. The demonstration is conducted via a simulated truss structure, an experimental beam, and 280 days valid recorded data from an in-service suspension bridge deck. Different measurements are analyzed to produce large number of identified results for reducing traffic excitation effect, the temperature effect, and so on, and to enhance the structural damage information in the final set of results. Experiences in handling the structural health monitoring data for monitoring the movement of a structural joint before and after a typhoon are also presented. The proposed strategy does not need a mathematical model of the structure, but this leads to heavy sensor requirement for a fine spatial resolution of the local damage.

scholarly journals Damage Indexing Method for Shear Critical Tubular Reinforced Concrete Structures based on Crack Image Analysis

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4304 ◽  
Author(s):  
Yang ◽  
Chang ◽  
Wu
Keyword(s):  
Image Analysis ◽  
Reinforced Concrete ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Measurement Method ◽  
Damage Index ◽  
Structural Health ◽  
Damage Indices ◽  
Indexing Method

Image analysis techniques have been applied to measure the displacements, strain field, and crack distribution of structures in the laboratory environment, and present strong potential for use in structural health monitoring applications. Compared with accelerometers, image analysis is good at monitoring area-based responses, such as crack patterns at critical regions of reinforced concrete (RC) structures. While the quantitative relationship between cracks and structural damage depends on many factors, cracks need to be detected and quantified in an automatic manner for further investigation into structural health monitoring. This work proposes a damage-indexing method by integrating an image-based crack measurement method and a crack quantification method. The image-based crack measurement method identifies cracks locations, opening widths, and orientations. Fractal dimension analysis gives the flexural cracks and shear cracks an overall damage index ranging between 0 and 1. According to the orientations of the cracks analyzed by image analysis, the cracks can be classified as either shear or flexural, and the overall damage index can be separated into shear and flexural damage indices. These damage indices not only quantify the damage of an RC structure, but also the contents of shear and flexural failures. While the engineering significance of the damage indices is structure dependent, when the damage indexing method is used for structural health monitoring, the damage indices safety thresholds can further be defined based on the structure type under consideration. Finally, this paper demonstrates this method by using the results of two experiments on RC tubular containment vessel structures.

scholarly journals Impedance-based damage detection under noise and vibration effects

2017 ◽  
Vol 17 (3) ◽  
pp. 654-667 ◽  
Author(s):  
Leandro M Campeiro ◽  
Ricardo ZM da Silveira ◽  
Fabricio G Baptista
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Low Cost ◽  
Coherence Function ◽  
Mechanical Impedance ◽  
Noise And Vibration ◽  
Structural Health ◽  
Damage Indices

The electro-mechanical impedance technique has been extensively studied in recent decades as a non-destructive method for detecting structural damage in structural health monitoring applications using low-cost piezoelectric transducers. Although many studies have reported the effectiveness of this detection method, numerous practical problems, such as the effects of noise and vibration, need to be addressed to enable this method’s effective use in real applications. Therefore, this article presents an experimental analysis of noise and vibration effects on structural damage detection in impedance-based structural health monitoring systems. The experiments were performed on an aluminum bar using two piezoelectric diaphragms, where one diaphragm was used to measure the electrical impedance signatures and the other diaphragm was used as an actuator to generate noise and controlled vibration. The effects of noise and vibration on impedance signatures were evaluated by computing the coherence function and basic damage indices. The results indicate that vibration and noise significantly affect the threshold of the lowest detectable damage, which can be compensated by increasing the excitation signal of the piezoelectric transducer.

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>

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.

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 A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shao-Fei Jiang ◽  
Si-Yao Wu ◽  
Li-Qiang Dong
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Damage Threshold ◽  
Optimization Techniques ◽  
Local Optimum ◽  
Structural Health ◽  
Civil Infrastructures ◽  
Standard Particle Swarm Optimization

Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.

Structural health–monitoring system for roof structure of the Dalian gymnasium

2018 ◽  
Vol 22 (7) ◽  
pp. 1579-1590 ◽  
Author(s):  
Hongnan Li ◽  
Chaolin Yuan ◽  
Liang Ren ◽  
Tao Jiang
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Structural Damage ◽  
Structural Information ◽  
Normal Operation ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System ◽  
Roof Structure

The roof of Dalian gymnasium was designed in the form of suspen-dome structure. A structural health–monitoring system has been developed for the roof structure to guarantee the safety condition during construction process as well as in future service. In this article, a monitoring scheme was proposed in detail according to the mechanical characteristics of the roof structure. Fiber Bragg grating sensors, inclinometers, and accelerometers were applied to measure necessary structural information. In order to interrogate different types of sensors, a novel data acquisition device of the structural health–monitoring system was also introduced and has achieved multitudinous physical variable synchronization acquisition. By analyzing the data obtained during the construction and normal operation of the gymnasium, the structural health condition was evaluated and the structural damage could subsequently be located.

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