scholarly journals A Substructural Damage Identification Approach for Shear Structure Based on Changes in the First AR Model Coefficient Matrix

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Liu Mei ◽  
Akira Mita ◽  
Jin Zhou
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Coefficient Matrix ◽  
Ar Model ◽  
Unknown Parameters ◽  
Theoretical Derivation ◽  
Model Coefficient ◽  
Identification Approach ◽  
Shear Structure ◽  
Identification Processes

A substructural damage identification approach based on changes in the first AR model coefficient matrix is proposed in this paper to identify structural damage including its location and severity. Firstly, a substructure approach is adopted in the procedure to divide a complete structure into several substructures in order to significantly reduce the number of unknown parameters for each substructure so that damage identification processes can be independently conducted on each substructure. To establish a relation between changes in AR model coefficients and structural damage for each substructure, a theoretical derivation is presented. Thus the accelerations are fed into ARMAX models to determine the AR model coefficients for each substructure under undamaged and various damaged conditions, based on which changes in the first AR model coefficient matrix (CFAR) is obtained and adopted as the damage indicator for the proposed substructure damage identification approach. To better assess the performance of the proposed procedure, a numerical simulation and an experimental verification of the proposed approach are then carried out and the results show that the proposed procedure can successfully locate and quantify the damage in both simulation and laboratory experiment.

scholarly journals Identification of Structural Damage in Bridges Using High-Frequency Vibrational Responses

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ivana Mekjavić
Keyword(s):  
High Frequency ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Damage Model ◽  
Reinforced Concrete Beam ◽  
Computational Technique ◽  
Damage Scenarios ◽  
Identification Approach ◽  
Girder Bridge

The present research aims to develop an effective and applicable structural damage detection method. A damage identification approach using only the changes of measured natural frequencies is presented. The structural damage model is assumed to be associated with a reduction of a contribution to the element stiffness matrix equivalent to a scalar reduction of the material modulus. The computational technique used to identify the damage from the measured data is described. The performance of the proposed technique on numerically simulated real concrete girder bridge is evaluated using imposed damage scenarios. To demonstrate the applicability of the proposed method by employing experimental measured natural frequencies this technique is applied for the first time to a simply supported reinforced concrete beam statically loaded incrementally to failure. The results of the damage identification procedure show that the proposed method can accurately locate the damage and predict the extent of the damage using high-frequency (here beyond the 4th order) vibrational responses.

Damage identification using structural modes based on substructure virtual distortion method

2016 ◽  
Vol 20 (2) ◽  
pp. 257-271 ◽  
Author(s):  
Qingxia Zhang ◽  
Łukasz Jankowski
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Structural Damage ◽  
Frequency Response Function ◽  
Damage Identification ◽  
Element Model ◽  
Frequency Responses ◽  
Computational Work ◽  
Structural Reanalysis ◽  
Identification Approach

A damage identification approach is presented using substructure virtual distortion method which takes the advantage of the fast structural reanalysis technique of virtual distortion method. The formulas of substructure virtual distortion method are deduced in frequency domain, and then the frequency response function of the damaged structure is constructed quickly via the superposition of the frequency response function of the intact structure and the frequency responses caused by the damage-coupling virtual distortions of the substructures. The structural damage extents are identified using the measured modal parameters. Two steps are adopted to increase the efficiency of optimization: the modals of finite element model are estimated quickly from the fast constructed frequency response function during the optimization and the primary distortions of the substructures are extracted by contribution analysis to further reduce the computational work. A six-story frame numerical model and an experiment of a cantilever beam are carried out, respectively, to verify the efficiency and accuracy of the proposed method.

Structural Damage Identification Method and Program Designing Based on Statistical Analysis

2013 ◽  
Vol 639-640 ◽  
pp. 283-286
Author(s):  
Zhuo Bin Wei ◽  
Ke Liu
Keyword(s):  
Structural Damage ◽  
Large Scale ◽  
Damage Identification ◽  
Constant Load ◽  
Structure Condition ◽  
Identification Method ◽  
Analysis Theory ◽  
Model Coefficient ◽  
Auto Regressive ◽  
Damage Condition

In order to realize the monitoring for the large-scale structures and find the change of its condition, a new damage feature factor is put forward. The damage feature factor is based on the PCA (principle component analysis) theory, analyzing the object of AR (Auto-regressive) model coefficient by the constant load, utilizing the theory of ellipse control figure. Then a program is designed combining the damage identification method with LabVIEW. Finally, an experiment is conducted on a steel frame model under different conditions. The results show that the first two principle components contain the main information of the structure condition, and the method can identify the changes of the structure condition correctly. Besides, the program works well and gives sound and light alarm under the damage condition.

A Nonparametric Method for Identifying Structural Damage in Bridges Based on the Best-Fit Auto-Regressive Models

2020 ◽  
Vol 20 (10) ◽  
pp. 2042012
Author(s):  
Tung Khuc ◽  
Phat Tien Nguyen ◽  
Andy Nguyen ◽  
F. Necati Catbas
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Window Size ◽  
Information Criterion ◽  
Ar Model ◽  
Auto Regressive ◽  
Damage Condition ◽  
Two Parameters ◽  
Ar Models ◽  
Best Fit

An enhanced method to determine the best-fit auto-regressive model (AR model) for structural damage identification is proposed in this paper. Whereby, two parameters of the model, including the number of model order and the window size of data, are analyzed simultaneously in order to accomplish the optimized values by means of Akaike’s Information Criterion (AIC) algorithm. The damage condition of structures can be detected by defined damage indicators obtained from the first three AR coefficients of the best-fit AR models. The ability of the proposed damage identification method is compared with the process that only utilizes conventional AR models without concern of parameter selection. The proposed method is verified using experimental data previously collected from a large-size bridge structure in the Structural Laboratory at the University of Central Florida. The results indicate that this method can detect and locate damage more effectively.

scholarly journals A Probabilistic Damage Identification Approach for Structures under Unknown Excitation and with Measurement Uncertainties

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Ying Lei ◽  
Ying Su ◽  
Wenai Shen
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Probabilistic Approach ◽  
Damage Index ◽  
Deterministic Algorithm ◽  
Measurement Noise ◽  
Identification Accuracy ◽  
Identification Approach ◽  
High Level

Recently, an innovative algorithm has been proposed by the authors for the identification of structural damage under unknown external excitations. However, identification accuracy of this proposed deterministic algorithm decreases under high level of measurement noise. A probabilistic approach is therefore proposed in this paper for damage identification considering measurement noise uncertainties. Based on the former deterministic algorithm, the statistical values of the identified structural parameters are estimated using the statistical theory and a damage index is defined. The probability of identified structural damage is further derived based on the reliability theory. The unknown external excitations to the structure are also identified by statistical evaluation. A numerical example of the identification of structural damage of a multistory shear-type building and its unknown excitation shows that the proposed probabilistic approach can accurately identify structural damage and the unknown excitations using only partial measurements of structural acceleration responses contaminated by intensive measurement noises.

scholarly journals Structural Damage Identification Based on AR Model with Additive Noises Using an Improved TLS Solution

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4341
Author(s):  
Wu ◽  
Li ◽  
Zhang
Keyword(s):  
Least Squares ◽  
Structural Damage ◽  
Shaking Table Test ◽  
Damage Identification ◽  
Shaking Table ◽  
Ar Model ◽  
Design Matrix ◽  
Damage Degree ◽  
Structural Damage Identification ◽  
Auto Regressive

Structural damage is inevitable due to the structural aging and disastrous external excitation. The auto-regressive (AR) based method is one of the most widely used methods for structural damage identification. In this regard, the classical least-squares algorithm is often utilized to solve the AR model. However, this algorithm generally could not take all the observed noises into account. In this study, a partial errors-in-variables (EIV) model is used so that both the current and prior observation errors are considered. Accordingly, a total least-squares (TLSE) solution is introduced to solve the partial EIV model. The solution estimates and accounts for the correlations between the current observed data and the design matrix. An effective damage indicator is chosen to count for damage levels of the structures. Both mathematical and finite element simulation results show that the proposed TLSE method yields better accuracy than the classical LS method and the AR model. Finally, the response data of a high-rise building shaking table test is used for demonstrating the effectiveness of the proposed method in identifying the location and damage degree of a model structure.

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