Nondestructive Damage Detection of Bridges: A Status Review

2001 ◽  
Vol 4 (2) ◽  
pp. 75-91 ◽  
Author(s):  
Xiaotong Wang ◽  
Chih-Chen Chang ◽  
Lichu Fan
Keyword(s):  
Damage Detection ◽  
Rapid Development ◽  
Low Frequency ◽  
Basic Research ◽  
Digital Signal ◽  
Point Of View ◽  
Detection Methods ◽  
Measured Signal ◽  
Destructive Testing ◽  
Bridge Damage

The recent advances in detecting and locating damage in bridges by different kinds of non-destructive testing and evaluation (NDT&E) methods are reviewed. From the application point of view, classifications for general bridge components and their damage types are presented. The relationships between damage, bridge components, and NDT&E techniques are summarized. Many useful WEB sources of NDT&E techniques in bridge damage detection are given. It is concluded that: (1) vibration-based damage detection methods are successful to a certain extent, especially when the overall damage is significant and, low frequency vibration can identify those areas where more detailed local inspection should be concentrated; (2) robust identification techniques that are able to locate damage based on realistic measured data sets still seem a long way from reality, and, basic research is still necessary in the mean time; (3) the rapid development of computer technology and digital signal processing (DSP) techniques greatly impacts upon the conventional NDT techniques, especially in control data processing and data displaying, as well as in simulation and modeling; (4) most of the NDT&E techniques introduced in this paper have their own practical commercial systems, but the effort required for combining the theoretical, experimental and engineering achievements, is still a challenging task when establishing the relationship between the unknown quantities and the measured signal parameters and specialised instruments have shown great advantages for doing some things more effectively than general ones; (5) in bridge damage detection, a problem usually requires the application of different NDT&E techniques; two or more independent techniques are needed to enable confidence in the results.

Detection of Damages in Mooring Lines of Spar Type Floating Offshore Wind Turbines Using Fuzzy Classification and Arma Parametric Modeling

2021 ◽  
pp. 2150111
Author(s):  
Mousa Rezaee ◽  
Reza Fathi ◽  
Vahid Jahangiri ◽  
Mir Mohammad Ettefagh ◽  
Aysan Jamalkia ◽  
...  
Keyword(s):  
Damage Detection ◽  
Success Rate ◽  
Wind Turbines ◽  
Parametric Modeling ◽  
Degree Of Freedom ◽  
Fuzzy Classification ◽  
Detection Methods ◽  
Measured Signal ◽  
Mooring Lines ◽  
Floating Offshore Wind Turbines

Floating wind turbines may encounter severe situations because of harsh environments. Higher cost of repair and maintenance of floating wind turbines have led researches to focus on damage detection methods that can prevent sudden failures. This paper presents an applicable method of damage detection and structural health monitoring for floating wind turbines based on the autoregressive moving average (ARMA) model and fuzzy classification. First, the dynamic model of a spar type floating wind turbine is constructed, by which the time responses of each degree of freedom of the system are acquired. With the system’s nonlinearity included, the intrinsic mode functions are obtained for the response signal. The Hilbert–Huang transform is applied and the appropriate measured signal for each degree of freedom is chosen for the ARMA modeling. In order to evaluate the proposed method, the ARMA parameters are first estimated for the undamaged condition then assumed damages are injected to the model and the ARMA parameters are once again estimated for the damaged condition. These parameters are considered as inputs for the fuzzy classification method. After training the system using the assumed damaged and undamaged conditions, the proposed method is simulated. Furthermore, the effect of measurement noise on the success rate is investigated. The results show that, in the presence of noise, the proposed method is able to identify the damage location and severity of mooring lines with acceptable success rate.

Structural damage detection via combining weighted strategy with trace Lasso

2018 ◽  
Vol 22 (3) ◽  
pp. 597-612 ◽  
Author(s):  
Chengbin Chen ◽  
Chudong Pan ◽  
Zepeng Chen ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Swarm Intelligence ◽  
Structural Damage ◽  
Optimization Problem ◽  
Detection Method ◽  
Rapid Development ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Structural Damage Detection ◽  
Selection Operator

With the rapid development of computation technologies, swarm intelligence–based algorithms become an innovative technique used for addressing structural damage detection issues, but traditional swarm intelligence–based structural damage detection methods often face with insufficient detection accuracy and lower robustness to noise. As an exploring attempt, a novel structural damage detection method is proposed to tackle the above deficiency via combining weighted strategy with trace least absolute shrinkage and selection operator (Lasso). First, an objective function is defined for the structural damage detection optimization problem by using structural modal parameters; a weighted strategy and the trace Lasso are also involved into the objection function. A novel antlion optimizer algorithm is then employed as a solution solver to the structural damage detection optimization problem. To assess the capability of the proposed structural damage detection method, two numerical simulations and a series of laboratory experiments are performed, and a comparative study on effects of different parameters, such as weighted coefficients, regularization parameters and damage patterns, on the proposed structural damage detection methods are also carried out. Illustrated results show that the proposed structural damage detection method via combining weighted strategy with trace Lasso is able to accurately locate structural damages and quantify damage severities of structures.

scholarly journals The Auto-Correlation of Ultrasonic Lamb Wave Phased Array Data for Damage Detection

Metals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 666 ◽  
Author(s):  
Haiyan Zhang ◽  
Jiayan Zhang ◽  
Guopeng Fan ◽  
Hui Zhang ◽  
Wenfa Zhu ◽  
...  
Keyword(s):  
Damage Detection ◽  
Phased Array ◽  
Time Synchronization ◽  
Signal To Noise Ratio ◽  
Correlation Method ◽  
High Sensitivity ◽  
Measured Signal ◽  
Correlation Technique ◽  
Destructive Testing ◽  
Auto Correlation

Ultrasonic phased array is widely used for damage detection recently because of its high sensitivity and rapid scanning without sensor movements. However, the measured signal is always influenced by the remnants of the initial excitation and the nonlinear signals from the instrumentation, which limits its application in thin-plate structures. To address this issue, an approach called auto-correlation subtraction is proposed to extract the scattering information of defects in this paper. In order to testify the feasibility of this method for damage detection, the experiments were carried out on three thin aluminum plates combined with the total focusing method (TFM) for imaging. By auto-correlating the full matrix data received by sensors and then subtracting the average auto-correlation of noise recorded by all receivers, the coherent scattered signal containing defect information is recovered. The experimental results indicate that the coherent travel time is in agreement with the theoretical value and the signal-to-noise ratio are improved. Additionally, compared with the cross-correlation technique, the time synchronization between different receivers is not necessary with the auto-correlation method. Results indicate that the presented method can improve the imaging resolution and has a great potential in the field of non-destructive testing.

Rationale and Design Considerations for a Semantic Mediator in Health Information Systems

1998 ◽  
Vol 37 (04/05) ◽  
pp. 518-526 ◽  
Author(s):  
D. Sauquet ◽  
M.-C. Jaulent ◽  
E. Zapletal ◽  
M. Lavril ◽  
P. Degoulet
Keyword(s):  
Information Systems ◽  
Software Engineering ◽  
Health Information ◽  
Rapid Development ◽  
Heterogeneous Systems ◽  
Health Information Systems ◽  
Semantic Interoperability ◽  
Point Of View ◽  
Current State ◽  
Engineering Environment

AbstractRapid development of community health information networks raises the issue of semantic interoperability between distributed and heterogeneous systems. Indeed, operational health information systems originate from heterogeneous teams of independent developers and have to cooperate in order to exchange data and services. A good cooperation is based on a good understanding of the messages exchanged between the systems. The main issue of semantic interoperability is to ensure that the exchange is not only possible but also meaningful. The main objective of this paper is to analyze semantic interoperability from a software engineering point of view. It describes the principles for the design of a semantic mediator (SM) in the framework of a distributed object manager (DOM). The mediator is itself a component that should allow the exchange of messages independently of languages and platforms. The functional architecture of such a SM is detailed. These principles have been partly applied in the context of the HEllOS object-oriented software engineering environment. The resulting service components are presented with their current state of achievement.

Protein Solvent Interaction: Transition of Protein-solvent Interaction Concept from Basic Research into Solvent Manipulation of Chromatography

2018 ◽  
Vol 20 (1) ◽  
pp. 34-39 ◽  
Author(s):  
Tsutomu Arakawa ◽  
Yoshiko Kita
Keyword(s):  
Column Chromatography ◽  
Rapid Development ◽  
Academic Research ◽  
Basic Research ◽  
Protein Stabilization ◽  
Special Issue ◽  
Solvent Interaction ◽  
Protein Formulations ◽  
Solvent Additive ◽  
Downstream Processes

Previously, we have reviewed in this journal (Arakawa, T., Kita, Y., Curr. Protein Pept. Sci., 15, 608-620, 2014) the interaction of arginine with proteins and various applications of this solvent additive in the area of protein formulations and downstream processes. In this special issue, we expand the concept of protein-solvent interaction into the analysis of the effects of solvent additives on various column chromatography, including mixed-mode chromatography. Earlier in our research, we have studied the interactions of such a variety of solvent additives as sugars, salts, amino acids, polymers and organic solvents with a variety of proteins, which resulted in mechanistic understanding on their protein stabilization and precipitation effects, the latter known as Hofmeister series. While such a study was then a pure academic research, rapid development of genetic engineering technologies and resultant biotechnologies made it a valuable knowledge in fully utilizing solvent additives in manipulation of protein solution, including column chromatography.

scholarly journals A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models

2020 ◽  
Vol 12 (17) ◽  
pp. 2809
Author(s):  
Meirman Syzdykbayev ◽  
Bobak Karimi ◽  
Hassan A. Karimi
Keyword(s):  
Land Surface ◽  
Basic Research ◽  
Probabilistic Methods ◽  
Detection Methods ◽  
Computational Techniques ◽  
Surface Evolution ◽  
Dem Analysis ◽  
High Degree ◽  
Terrain Features ◽  
Shaded Relief

Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods.

Detection by real-time quaking-induced conversion (RT-QuIC), ELISA, and IHC of chronic wasting disease prion in lymph nodes from Pennsylvania white-tailed deer with specific PRNP genotypes

2021 ◽  
pp. 104063872110214
Author(s):  
Deepanker Tewari ◽  
David Steward ◽  
Melinda Fasnacht ◽  
Julia Livengood
Keyword(s):  
Real Time ◽  
Disease Susceptibility ◽  
Chronic Wasting Disease ◽  
Low Frequency ◽  
The United States ◽  
Detection Methods ◽  
Spongiform Encephalopathy ◽  
Wasting Disease ◽  
Diagnostic Laboratory ◽  
Highly Sensitive

Chronic wasting disease (CWD) is a prion-mediated, transmissible disease of cervids, including deer ( Odocoileus spp.), which is characterized by spongiform encephalopathy and death of the prion-infected animals. Official surveillance in the United States using immunohistochemistry (IHC) and ELISA entails the laborious collection of lymphoid and/or brainstem tissue after death. New, highly sensitive prion detection methods, such as real-time quaking-induced conversion (RT-QuIC), have shown promise in detecting abnormal prions from both antemortem and postmortem specimens. We compared RT-QuIC with ELISA and IHC for CWD detection utilizing deer retropharyngeal lymph node (RLN) tissues in a diagnostic laboratory setting. The RLNs were collected postmortem from hunter-harvested animals. RT-QuIC showed 100% sensitivity and specificity for 50 deer RLN (35 positive by both IHC and ELISA, 15 negative) included in our study. All deer were also genotyped for PRNP polymorphism. Most deer were homozygous at codons 95, 96, 116, and 226 (QQ/GG/AA/QQ genotype, with frequency 0.86), which are the codons implicated in disease susceptibility. Heterozygosity was noticed in Pennsylvania deer, albeit at a very low frequency, for codons 95GS (0.06) and 96QH (0.08), but deer with these genotypes were still found to be CWD prion-infected.

Elimination of outlier measurements for damage detection of structures under changing environmental conditions

2021 ◽  
pp. 147592172199847
Author(s):  
William Soo Lon Wah ◽  
Yining Xia
Keyword(s):  
Damage Detection ◽  
Multiple Regression ◽  
Natural Frequencies ◽  
Detection Method ◽  
Detection Methods ◽  
Structure Model ◽  
Bridge Structure ◽  
Beam Structure ◽  
Dependent Variables ◽  
The Difference

Damage detection methods developed in the literature are affected by the presence of outlier measurements. These measurements can prevent small levels of damage to be detected. Therefore, a method to eliminate the effects of outlier measurements is proposed in this article. The method uses the difference in fits to examine how deleting an observation affects the predicted value of a model. This allows the observations that have a large influence on the model created, to be identified. These observations are the outlier measurements and they are eliminated from the database before the application of damage detection methods. Eliminating the outliers before the application of damage detection methods allows the normal procedures to detect damage, to be implemented. A multiple-regression-based damage detection method, which uses the natural frequencies as both the independent and dependent variables, is also developed in this article. A beam structure model and an experimental wooden bridge structure are analysed using the multiple-regression-based damage detection method with and without the application of the method proposed to eliminate the effects of outliers. The results obtained demonstrate that smaller levels of damage can be detected when the effects of outlier measurements are eliminated using the method proposed in this article.

scholarly journals Robust and fast post-processing of single-shot spin qubit detection events with a neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tom Struck ◽  
Javed Lindner ◽  
Arne Hollmann ◽  
Floyd Schauer ◽  
Andreas Schmidbauer ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bayesian Inference ◽  
Rabi Oscillation ◽  
Detection Methods ◽  
Measured Signal ◽  
Single Shot ◽  
Post Processing ◽  
Readout Signal ◽  
Spin Qubit

AbstractEstablishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal of our qubit measurements. This readout signal contains a stochastic peak, for which a Bayesian inference filter including Gaussian noise is theoretically optimal. Hence, we benchmark our neural networks trained by various strategies versus this latter algorithm. Training of the network with 106 experimentally recorded single-shot readout traces does not improve the post-processing performance. A network trained by synthetically generated measurement traces performs similar in terms of the detection error and the post-processing speed compared to the Bayesian inference filter. This neural network turns out to be more robust to fluctuations in the signal offset, length and delay as well as in the signal-to-noise ratio. Notably, we find an increase of 7% in the visibility of the Rabi oscillation when we employ a network trained by synthetic readout traces combined with measured signal noise of our setup. Our contribution thus represents an example of the beneficial role which software and hardware implementation of neural networks may play in scalable spin qubit processor architectures.

scholarly journals Research progress in the effects of terahertz waves on biomacromolecules

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Liu Sun ◽  
Li Zhao ◽  
Rui-Yun Peng
Keyword(s):  
Vibrational Energy ◽  
Rapid Development ◽  
Energy Levels ◽  
Basic Research ◽  
Research Progress ◽  
Biological Macromolecules ◽  
Terahertz Waves ◽  
Terahertz Spectrum ◽  
Biomedical Field ◽  
Vibrational Energy Levels

AbstractWith the rapid development of terahertz technologies, basic research and applications of terahertz waves in biomedicine have attracted increasing attention. The rotation and vibrational energy levels of biomacromolecules fall in the energy range of terahertz waves; thus, terahertz waves might interact with biomacromolecules. Therefore, terahertz waves have been widely applied to explore features of the terahertz spectrum of biomacromolecules. However, the effects of terahertz waves on biomacromolecules are largely unexplored. Although some progress has been reported, there are still numerous technical barriers to clarifying the relation between terahertz waves and biomacromolecules and to realizing the accurate regulation of biological macromolecules by terahertz waves. Therefore, further investigations should be conducted in the future. In this paper, we reviewed terahertz waves and their biomedical research advantages, applications of terahertz waves on biomacromolecules and the effects of terahertz waves on biomacromolecules. These findings will provide novel ideas and methods for the research and application of terahertz waves in the biomedical field.

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