On using residual voltage to estimate electrode model parameters for damage detection

This paper describes a new global damage identification framework for the continuous/periodic monitoring of civil structures. In order to localize and estimate the severity of damage regions, a one-stage model-based Bayesian probabilistic damage detection approach is proposed. This method, which is based on the response power spectral density of the structure, enjoys the advantage of broadband frequency information and can be implemented on input-output as well as output-only damage identification studies. A parallel genetic algorithm is subsequently used to evolve the optimal model parameters introduced for different damage conditions. Given the complex search space and the need to perform multiple time-consuming objective function evaluations, a parallel meta-heuristic provides a robust optimization tool in this domain. It is shown that this approach is capable of detecting structural damage in both noisy and noise-free environments.

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Vibration Based Bayesian Inference for Finite Element Model Parameters Estimation and Damage Detection

Lecture Notes in Mechanical Engineering - Proceedings of XXIV AIMETA Conference 2019 ◽

10.1007/978-3-030-41057-5_129 ◽

2020 ◽

pp. 1591-1607

Author(s):

Chiara Pepi ◽

Massimiliano Gioffré

Keyword(s):

Finite Element ◽

Bayesian Inference ◽

Finite Element Model ◽

Damage Detection ◽

Element Model ◽

Parameters Estimation ◽

Model Parameters

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Use of Measured Vibration of In-Situ Sleeper for Detecting Underlying Railway Ballast Damage

International Journal of Structural Stability and Dynamics ◽

10.1142/s021945541540026x ◽

2015 ◽

Vol 15 (08) ◽

pp. 1540026 ◽

Cited By ~ 6

Author(s):

Q. Hu ◽

H. F. Lam ◽

S. A. Alabi

Keyword(s):

Damage Detection ◽

Bayesian Approach ◽

Model Updating ◽

Probabilistic Approach ◽

Modeling Method ◽

Detection Methods ◽

Model Parameters ◽

Railway Ballast ◽

Continuous Modeling ◽

The Bayesian Approach

The identification of railway ballast damage under a concrete sleeper is investigated by following the Bayesian approach. The use of a discrete modeling method to capture the distribution of ballast stiffness under the sleeper introduces artificial stiffness discontinuities between different ballast regions. This increases the effects of modeling errors and reduces the accuracy of the ballast damage detection results. In this paper, a continuous modeling method was developed to overcome this difficulty. The uncertainties induced by modeling error and measurement noise are the major difficulties of vibration-based damage detection methods. In the proposed methodology, Bayesian probabilistic approach is adopted to explicitly address the uncertainties associated with the identified model parameters. In the model updating process, the stiffness of the ballast foundation is assumed to be continuous along the sleeper by using a polynomial of order N. One of the contributions of this paper is to calculate the order N conditional on a given set of measurement utilizing the Bayesian model class selection method. The proposed ballast damage detection methodology was verified with vibration data obtained from a segment of full-scale ballasted track under laboratory conditions, and the experimental verification results are very encouraging showing that it is possible to use the Bayesian approach along with the newly developed continuous modeling method for the purpose of ballast damage detection.

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Novel Damage Detection Techniques for Structural Health Monitoring Using a Hybrid Sensor

Mathematical Problems in Engineering ◽

10.1155/2016/3734258 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Dengjiang Wang ◽

Jingjing He ◽

Banglin Dong ◽

Xiaopeng Liu ◽

Weifang Zhang

Keyword(s):

Damage Detection ◽

Fatigue Testing ◽

Fatigue Cracks ◽

Probability Of Detection ◽

Model Parameters ◽

The Novel ◽

Plate Structures ◽

Experimental Procedure ◽

Detection Techniques ◽

Sensor Monitoring

This study presents a technique for detecting fatigue cracks based on a hybrid sensor monitoring system consisting of a combination of intelligent coating monitoring (ICM) and piezoelectric transducer (PZT) sensors. An experimental procedure using this hybrid sensor system was designed to monitor the cracks generated by fatigue testing in plate structures. A probability of detection (POD) model that quantifies the reliability of damage detection for a specific sensor or the nondestructive testing (NDT) method was used to evaluate the weight factor for the ICM and PZT sensors. To estimate the uncertainty of model parameters in this study, the Bayesian method was employed. Realistic data from fatigue testing was used to validate the overall method, and the results show that the novel damage detection technique using a hybrid sensor can quantify fatigue cracks more accurately than results obtained by conventional sensor methods.

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Damage Detection of Steel-Concrete Composite Beam

Civil and Environmental Engineering Reports ◽

10.2478/ceer-2018-0033 ◽

2018 ◽

Vol 28 (3) ◽

pp. 30-49

Author(s):

Maciej Szumigała ◽

Agnieszka Pełka-Sawenko ◽

Tomasz Wróblewski ◽

Małgorzata Abramowicz

Keyword(s):

Experimental Data ◽

Damage Detection ◽

Discrete Model ◽

Composite Beams ◽

Model Parameters ◽

Discrete Wavelet ◽

Identification Procedure ◽

Original Algorithm ◽

Automated Optimization ◽

Made In

Abstract The paper presents analysis results of steel-concrete composite beams, identification and attempts to detect damage introduced in a discrete model. Analysis of damage detection was conducted using DDL (Damage, Detection, Localization), our own original algorithm. Changes of dynamic and static parameters of the model were analysed in damage detection. Discrete wavelet transform was used for damage localization in the model. Prior to ultimate analysis, two-tier identification of discrete model parameters based on experimental data was made. In identification procedure, computational software (Python, Abaqus, Matlab) was connected in automated optimization loops. Results positively verified the original DDL algorithm for damage detection in steel-concrete composite beams, which enables further analysis using experimental data.

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Time-domain Markov chain Monte Carlo–based Bayesian damage detection of ballasted tracks using nonlinear ballast stiffness model

Structural Health Monitoring ◽

10.1177/1475921720966950 ◽

2020 ◽

pp. 147592172096695

Author(s):

Heung-Fai Lam ◽

Mujib Olamide Adeagbo ◽

Yeong-Bin Yang

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Damage Detection ◽

Probability Density ◽

Time Domain ◽

Model Updating ◽

Model Parameters ◽

Uncertain Model ◽

Nonlinear Stress

This article reports the development of a methodology for detecting ballast damage under a sleeper based on measured sleeper vibration following the Bayesian statistical system identification framework. To ensure the methodology is applicable under large amplitude vibration of the sleeper (e.g. under trainload), the nonlinear stress–strain behavior of railway ballast is considered. This, on one hand, significantly reduces the problem of modeling error, but, on the other hand, increases the number of uncertain model parameters. The uncertainty associated with the identified model parameters of the rail–sleeper–ballast system may be very high. To overcome this difficulty, the Markov chain Monte Carlo–based Bayesian model updating is adopted in the proposed methodology for the approximation of the posterior probability density function of uncertain model parameters. Owing to the nonlinear behavior of the system, the model updating is performed in the time domain instead of the modal domain. The applicability of the proposed damage detection methodology was first verified numerically using simulated impact hammer test data in two damaged cases perturbed with Gaussian white noise. Second, impact hammer tests of in situ sleepers in the full-scale in-door ballasted track test panel were carried out to collect data for the experimental verification of the proposed methodology. Artificial ballast damage was simulated under the target concrete sleeper by replacing normal-sized ballast particles (∼60 mm) by small-sized ballast particles (∼15 mm). The proposed methodology successfully identified the location and severity of ballast damage under the sleeper. From the calculated posterior marginal probability density functions of model parameters, one can quantify the uncertainties associated with the damage detection results. The proposed methodology is an essential step in the development of a long-term railway track health monitoring system utilizing train-induced vibration.

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The Damage Detection of Pressure Pipe Based on the Statistical Pattern Recognition Theory

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.955-959.3432 ◽

2014 ◽

Vol 955-959 ◽

pp. 3432-3436

Author(s):

Su Min Zhao ◽

We She He ◽

Shuang Mei Chang ◽

Yu Qiang Cheng

Keyword(s):

Pattern Recognition ◽

Damage Detection ◽

Structural Damage ◽

Test Method ◽

Statistical Pattern Recognition ◽

Model Parameters ◽

Pressure Pipe ◽

Pattern Recognition Theory ◽

Statistical Pattern ◽

Recognition Theory

Based on the statistical pattern recognition theory, the AMRA timing analysis methods are used in the article, through the combination of long autoregressive model residuals method and the least squares method the model parameters are estimated, and a system model is established. By using mean control chart method the vibration information and feature of the pressure pipe are extracted and selected, so whether the pressure pipes is damaged can be judged effectively. The simulation results show that structural abnormalities test method of the mean value ,which is Based on the recognition theory of statistical pattern, can accurately diagnose structural damage detection state ,the injury degree and damage location, it has a very strong sensitivity

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A Novel Probability of Detection Assessment Considering Model Uncertainty for Lamb Wave Detection

10.1115/qnde2021-74014 ◽

2021 ◽

Author(s):

Chenjun Gao ◽

Jingjing He ◽

Xuefei Guan

Keyword(s):

Model Selection ◽

Damage Detection ◽

Lamb Wave ◽

Bayes Factor ◽

Choice Model ◽

Assessment Method ◽

Uncertain Variable ◽

Probability Of Detection ◽

Model Parameters ◽

Model Selection Uncertainty

Abstract Uncertainty in Non-Destructive Evaluation (NDE) arises from many sources, e.g., manufacturing variability, environmental noise, and inadequate measurement devices. The reliability of the NDE measurements is typically quantified by the probability of detection (POD). With the advent and technical developments of the simulation method and computer science, efforts have been devoted to generating and estimating the POD curve for Lamb wave damage detection. However, few studies have been reported on the POD evaluation considering model selection uncertainty. This paper presents a novel POD assessment method incorporating model selection uncertainty for Lamb wave damage detection. By treating the flaw quantification model as a discrete uncertain variable, a hierarchical probabilistic model for Lamb wave POD is formulated in the Bayesian framework. Uncertainties from the model choice, model parameters, and other variables can be explicitly incorporated using the proposed method. The Bayes factor is used to evaluate the performance of models. The posterior distributions of model parameters and the model fusion results are calculated through the Bayesian update using the reversible jump Markov chain Monte Carlo method. A fatigue problem with naturally developed cracks is used to demonstrate the proposed method.

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Instrumental and Ideological Union Commitment: Longitudinal Assessment of Construct Validity

European Journal of Psychological Assessment ◽

10.1027//1015-5759.17.2.98 ◽

2001 ◽

Vol 17 (2) ◽

pp. 98-111 ◽

Cited By ~ 1

Author(s):

Anders Sjöberg ◽

Magnus Sverke

Keyword(s):

Construct Validity ◽

Temporal Stability ◽

Strong Support ◽

Internal Consistency Reliability ◽

Measurement Model ◽

Two Dimensions ◽

Model Parameters ◽

Longitudinal Assessment ◽

Union Commitment ◽

Union Participation

Summary: Previous research has identified instrumentality and ideology as important aspects of member attachment to labor unions. The present study evaluated the construct validity of a scale designed to reflect the two dimensions of instrumental and ideological union commitment using a sample of 1170 Swedish blue-collar union members. Longitudinal data were used to test seven propositions referring to the dimensionality, internal consistency reliability, and temporal stability of the scale as well as postulated group differences in union participation to which the scale should be sensitive. Support for the hypothesized factor structure of the scale and for adequate reliabilities of the dimensions was obtained and was also replicated 18 months later. Tests for equality of measurement model parameters and test-retest correlations indicated support for the temporal stability of the scale. In addition, the results were consistent with most of the predicted differences between groups characterized by different patterns of change/stability in union participation status. The study provides strong support for the construct validity of the scale and indicates that it can be used in future theory testing on instrumental and ideological union commitment.

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