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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Smart Structures and Systems
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Volume & Issues
Volume 12, Issue 6 - Dec 2013
Volume 12, Issue 5 - Nov 2013
Volume 12, Issue 3_4 - Sep 2013
Volume 12, Issue 2 - Aug 2013
Volume 12, Issue 1 - Jul 2013
Volume 11, Issue 6 - Jun 2013
Volume 11, Issue 5 - May 2013
Volume 11, Issue 4 - Apr 2013
Volume 11, Issue 3 - Mar 2013
Volume 11, Issue 2 - Feb 2013
Volume 11, Issue 1 - Jan 2013
Selecting the target year
Linear system parameter as an indicator for structural diagnosis of short span bridges
Kim, Chul-Woo ; Isemoto, Ryo ; Sugiura, Kunitomo ; Kawatani, Mitsuo ;
Smart Structures and Systems, volume 11, issue 1, 2013, Pages 1~17
DOI : 10.12989/sss.2013.11.1.001
This paper intended to investigate the feasibility of bridge health monitoring using a linear system parameter of a time series model identified from traffic-induced vibrations of bridges through a laboratory moving vehicle experiment on scaled model bridges. This study considered the system parameter of the bridge-vehicle interactive system rather than modal ones because signals obtained under a moving vehicle are not the responses of the bridge itself but those of the interactive system. To overcome the shortcomings of modal parameter-based bridge diagnosis using a time series model, this study considered coefficients of Autoregressive model (AR coefficients) as an early indicator of anomaly of bridges. This study also investigated sensitivity of AR coefficients in detecting anomaly of bridges. Observations demonstrated effectiveness of using AR coefficients as an early indicator for anomaly of bridges.
Application of recursive SSA as data pre-processing filter for stochastic subspace identification
Loh, Chin-Hsiung ; Liu, Yi-Cheng ;
Smart Structures and Systems, volume 11, issue 1, 2013, Pages 19~34
DOI : 10.12989/sss.2013.11.1.019
The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using the Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approach. To reduce the effect of noise on the results of identification, discussion on the pre-processing of data using recursive singular spectrum analysis (rSSA) is presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Through the application of rSSA-SSI-COV to the vibration measurement of bridge during scouring experiment, the ability of the proposed algorithm was proved to be robust to the noise perturbations and offers a very good online tracking capability. The accuracy and robustness offered by rSSA-SSI-COV provides a key to obtain the evidence of imminent bridge settlement and a very stable modal frequency tracking which makes it possible for early warning. The peak values of the identified
mode shape slope ratio has shown to be a good indicator for damage location, meanwhile, the drastic movements of the peak of
mode slope ratio could be used as another feature to indicate imminent pier settlement.
Some precautions to consider in using wavelet transformation for damage detection analysis of plates
Beheshti-Aval, S.B. ; Taherinasab, M. ; Noori, M. ;
Smart Structures and Systems, volume 11, issue 1, 2013, Pages 35~51
DOI : 10.12989/sss.2013.11.1.035
Over the last two decades Wavelet Transformation (WT) method has been widely utilized for the damage identification of structures. The main objective of this paper is to discuss and present some of common shortcomings and limitations of mathematical software, as well as other precautionary measures that need to be considered when using them for wavelet analysis applications. Due to popular usage of MATLABMATLAB(R) comparing to other mathematical tools among researchers for data processing of structural responses through WT analysis, this software was chosen for specific study. To the best of the authors` knowledge, these limitations and observations have not been previously identified or discussed in the literature. In this work, a square plate with a severe damage, in form of a crack, parallel to the left edge of the plate is selected for a pilot study. The steady state harmonic response is used for measuring the deflection shape across the line parallel to one edge and perpendicular to the damage. Several criteria and cases such as the smallest size damage that can be detected, correlation between the crack width and the number of sampling points, and the influence of the damage thickness on the accuracy of the result are investigated.
Particle relaxation method for structural parameters identification based on Monte Carlo Filter
Sato, Tadanobu ; Tanaka, Youhei ;
Smart Structures and Systems, volume 11, issue 1, 2013, Pages 53~67
DOI : 10.12989/sss.2013.11.1.053
In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.
Multi-stage approach for structural damage identification using particle swarm optimization
Tang, H. ; Zhang, W. ; Xie, L. ; Xue, S. ;
Smart Structures and Systems, volume 11, issue 1, 2013, Pages 69~86
DOI : 10.12989/sss.2013.11.1.069
An efficient methodology using static test data and changes in natural frequencies is proposed to identify the damages in structural systems. The methodology consists of two main stages. In the first stage, the Damage Signal Match (DSM) technique is employed to quickly identify the most potentially damaged elements so as to reduce the number of the solution space (solution parameters). In the second stage, a particle swarm optimization (PSO) approach is presented to accurately determine the actual damage extents using the first stage results. One numerical case study by using a planar truss and one experimental case study by using a full-scale steel truss structure are used to verify the proposed hybrid method. The identification results show that the proposed methodology can identify the location and severity of damage with a reasonable level of accuracy, even when practical considerations limit the number of measurements to only a few for a complex structure.
Damage identification using chaotic excitation
Wan, Chunfeng ; Sato, Tadanobu ; Wu, Zhishen ; Zhang, Jian ;
Smart Structures and Systems, volume 11, issue 1, 2013, Pages 87~102
DOI : 10.12989/sss.2013.11.1.087
Vibration-based damage detection methods are popular for structural health monitoring. However, they can only detect fairly large damages. Usually impact pulse, ambient vibrations and sine-wave forces are applied as the excitations. In this paper, we propose the method to use the chaotic excitation to vibrate structures. The attractors built from the output responses are used for the minor damage detection. After the damage is detected, it is further quantified using the Kalman Filter. Simulations are conducted. A 5-story building is subjected to chaotic excitation. The structural responses and related attractors are analyzed. The results show that the attractor distances increase monotonously with the increase of the damage degree. Therefore, damages, including minor damages, can be effectively detected using the proposed approach. With the Kalman Filter, damage which has the stiffness decrease of about 5% or lower can be quantified. The proposed approach will be helpful for detecting and evaluating minor damages at the early stage.
Particle filter for model updating and reliability estimation of existing structures
Yoshida, Ikumasa ; Akiyama, Mitsuyoshi ;
Smart Structures and Systems, volume 11, issue 1, 2013, Pages 103~122
DOI : 10.12989/sss.2013.11.1.103
It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.
Structural modal identification through ensemble empirical modal decomposition
Zhang, J. ; Yan, R.Q. ; Yang, C.Q. ;
Smart Structures and Systems, volume 11, issue 1, 2013, Pages 123~134
DOI : 10.12989/sss.2013.11.1.123
Identifying structural modal parameters, especially those modes within high frequency range, from ambient data is still a challenging problem due to various kinds of uncertainty involved in vibration measurements. A procedure applying an ensemble empirical mode decomposition (EEMD) method is proposed for accurate and robust structural modal identification. In the proposed method, the EEMD process is first implemented to decompose the original ambient data to a set of intrinsic mode functions (IMFs), which are zero-mean time series with energy in narrow frequency bands. Subsequently, a Sub-PolyMAX method is performed in narrow frequency bands by using IMFs as primary data for structural modal identification. The merit of the proposed method is that it performs structural identification in narrow frequency bands (take IMFs as primary data), unlike the traditional method in the whole frequency space (take original measurements as primary data), thus it produces more accurate identification results. A numerical example and a multiple-span continuous steel bridge have been investigated to verify the effectiveness of the proposed method.