scholarly journals Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771878688 ◽  
Author(s):  
Songtao Xue ◽  
Bo Wen ◽  
Rui Huang ◽  
Liyuan Huang ◽  
Tadanobu Sato ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Structural Health Monitoring ◽  
Parameter Identification ◽  
Health Monitoring ◽  
Gaussian Noise ◽  
Damage Identification ◽  
Structural Parameters ◽  
Likelihood Estimate ◽  
Structural Health

Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

scholarly journals Parameter Identification for Structural Health Monitoring with Extended Kalman Filter Considering Integration and Noise Effect

2018 ◽  
Vol 8 (12) ◽  
pp. 2480 ◽  
Author(s):  
Liyu Xie ◽  
Zhenwei Zhou ◽  
Lei Zhao ◽  
Chunfeng Wan ◽  
Hesheng Tang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Structural Health Monitoring ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Damage Identification ◽  
Engineering Structures ◽  
Structural Health ◽  
Civil Engineering Structures

Since physical parameters are much more sensitive than modal parameters, structural parameter identification with an extended Kalman filter (EKF) has received extensive attention in structural health monitoring for civil engineering structures. In this paper, EKF-based parameter identification technique is studied with numerical and experimental approaches. A four-degree-of-freedom (4-DOF) system is simulated and analyzed as an example. Different integration methods are examined and their influence to the final identification results of the structural stiffness and damping is also studied. Furthermore, the effect of different kinds of noise is studied as well. Identification results show that the convergence speed and estimation accuracy under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments with a five-story steel frame are conducted to verify the damage identification capacity of the EKF. The results show that stiffness with different damage degrees can be identified effectively, which indicates that the EKF is capable of being applied for damage identification and health monitoring for civil engineering structures.

Probability-Based Structural Health Monitoring Through Markov Chain Monte Carlo Sampling

2016 ◽  
Vol 16 (07) ◽  
pp. 1550039 ◽  
Author(s):  
P. J. Li ◽  
D. W. Xu ◽  
J. Zhang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Parameters ◽  
Fe Model ◽  
Property A ◽  
Stochastic Sampling ◽  
Structural Health

The classical nonuniqueness problem exists due to uncertainty in the finite element (FE) calibration field. Namely, multiple models with different intrinsic parameters may all fit the observed data well, thus the selected single “best” model probably is not the truly best model to reflect the structural intrinsic property. A probability-based method using a population of FE models, not the single “best” method, is proposed to deal with the nonuniqueness problem. In this method, the Markov Chain Monte Carlo (MCMC) technique is first performed to sample the key structural parameters representing the main sources of uncertainty. Then a FE model population is generated using the samples, and the posterior probability of each model is evaluated by calculating the correlation between the simulation results and measurements through the Bayesian theorem. Finally, all the FE models from the stochastic sampling with their posterior probabilities are used for structural identification (St-Id) and performance evaluation. The advantage of the proposed method is that it not only identifies the magnitudes of structural parameters, but also generates their probability distributions for subsequent probability-based reliability analysis and risk evaluation. The feature provided by the stochastic sampling and statistical techniques makes the proposed method suitable for dealing with uncertainty. The example of the Phase I IASC-ASCE benchmark structure investigated demonstrates the effectiveness of the proposed method for probability-based structural health monitoring.

scholarly journals Kappa-PSO-FAN based method for damage identification on composite structural health monitoring

2018 ◽  
Vol 95 ◽  
pp. 1-13 ◽  
Author(s):  
Mario A. de Oliveira ◽  
Nelcileno V.S. Araujo ◽  
Daniel J. Inman ◽  
Jozue Vieira Filho
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
Structural Health

scholarly journals A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

2018 ◽  
Vol 18 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Nguyen Lu Dang Khoa ◽  
Yang Wang ◽  
Bijan Samali ◽  
Xinqun Zhu
Keyword(s):  
Data Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Damage Identification ◽  
Tensor Analysis ◽  
Support Vector ◽  
Damage Scenarios ◽  
Structural Health ◽  
Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

Structural Health Monitoring and Damage Identification

2020 ◽  
pp. 1-72
Author(s):  
R. Fuentes ◽  
E.J. Cross ◽  
P.A. Gardner ◽  
L.A. Bull ◽  
T.J. Rogers ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
Structural Health

Optimal Accelerometer Locations for Structural Health Monitoring Using Time-Domain System Identification

2006 ◽  
Vol 321-323 ◽  
pp. 273-277 ◽  
Author(s):  
Soon Jung Kwon ◽  
Hae Sung Lee ◽  
Soo Bong Shin
Keyword(s):  
System Identification ◽  
Structural Health Monitoring ◽  
Fisher Information ◽  
Health Monitoring ◽  
Time Domain ◽  
Fisher Information Matrix ◽  
Structural Parameters ◽  
Hessian Matrix ◽  
Information Matrix ◽  
Structural Health

The paper presents two algorithms for determining optimal accelerometer locations for structural health monitoring when structural condition is assessed by a system identification scheme in time-domain. The accelerometer locations are determined by ranking the components of an effective independent distribution vector computed from a Fisher information matrix. One of the proposed algorithms formulates a Fisher information matrix by multiplying acceleration matrix with its transpose and the other as a Gauss-Newton Hessian matrix composed of acceleration sensitivities with respect to structural parameters. Since the structural parameters cannot be known exactly in an actual application, a statistical approach is proposed by setting an error bound between the actual and the baseline values. To examine the algorithm, simulation studies have been carried out on a two-span planar truss. The results using locations selected by the two algorithms were compared.

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