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.

Structural Damage Identification Based on Hilbert-Huang Transform and Verification via Shaking Table Model Test

2011 ◽  
Vol 255-260 ◽  
pp. 4237-4241 ◽  
Author(s):  
Jian Ping Han ◽  
Jiong Qian ◽  
Pei Juan Zheng
Keyword(s):  
Structural Damage ◽  
Shaking Table Test ◽  
Damage Identification ◽  
Shaking Table ◽  
Reinforced Concrete Frame ◽  
Hilbert Huang Transform ◽  
Concrete Frame ◽  
Marginal Spectrum ◽  
Table Model ◽  
Strength And Stiffness

Damage occurs in components and joints while the structure is affected by strong ground motions. Dynamic characteristics of the structure will change with the deterioration of strength and stiffness. Analyzing and processing the vibration signals is one of the mainstream ways for structural health monitoring and damage identification. In this paper, Hilbert-Huang transform is adopted to identify structural damage. Time-varying instantaneous frequency and instantaneous energy is used to identify the damage evolution of the structure. And relative amplitude of Hilbert marginal spectrum is used to identify the damage location of the structure. Finally, the acceleration records at gauge points from the shaking table test of a 12-storey reinforced concrete frame model are processed. Evolution and location of the model damage are identified. Identification results agree well with experimental observation. This indicates that the proposed approach is capable to identify damage of the structure.

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.

Study on the Sensitive Factors of Structural Nonlinear Damage Based on the Innovation Series

2020 ◽  
Vol 20 (10) ◽  
pp. 2042011
Author(s):  
Liujie Chen ◽  
Yahui Mei ◽  
Jiyang Fu ◽  
Ching Tai Ng ◽  
Zhen Cui
Keyword(s):  
Standard Deviation ◽  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Mean Squared Error ◽  
Ar Model ◽  
Arch Model ◽  
Residual Series ◽  
Auto Regressive ◽  
Shear Building

Constructing a damage-sensitive factor (DSF) is one of the key steps in structural damage detection. In this paper, innovation series extracted from the auto-regressive conditional heteroscedasticity (ARCH) model are proposed to construct a DSF, which is defined as the standard deviation of innovation (SDI). A three-story shear building structure is used to demonstrate and verify the performance of the proposed method, and the results are compared with the standard deviation of the residuals (SDR) based on an auto-regressive (AR) model. In the proposed method, the AR model is established using the acceleration responses obtained from the reference and test states. The residual series are then extracted for fitting the SDR. Subsequently, the ARCH model is constructed based on the residual series from the AR model, and a new DSF of SDI is defined. This study focuses on analyzing the accuracy of fitting AR model and ARCH model to vibration response data via the normal probability distribution, and identifying the characteristics of the residual and innovation series. The mean squared error (MSE) is used as the loss function to calculate the loss on residual and innovation series from the AR model and ARCH model, respectively. The results demonstrate that the SDR can be used for nonlinear damage detection. However, the proposed SDI can provide more accurate nonlinear damage identification and is robust to varying environmental condition and small damages. Thus, the innovation series developed based on ARCH model are promising for expressing and constructing nonlinear DSFs.

Comparison of Analytical Approaches to Tall Building Structural Damage Identification Based on Measured Dynamic Characteristics

2011 ◽  
Vol 105-107 ◽  
pp. 1081-1086
Author(s):  
Jian Jun Wei
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Element Model ◽  
Shaking Table ◽  
Structure Damage ◽  
Damage Indices ◽  
Structural Damage Identification ◽  
Four Levels ◽  
Analytical Approaches ◽  
Story Building

In the past two decades, there are many modal-based indices have been proposed for structural damage identification. In the present study, a modal of a 38-story building was tested on a shaking table to subject to four levels of earthquake, and the acceleration responses at nine floors of the structure are measured and used to construct five damage indices respectively. The experiment makes it possible to verify the applicability of vibration-based identification methods in identifying structure damage without use of structural finite element model. Damage in the structure after experiencing different attacks is evaluated via five modal-based damage indices respectively .By comparing the solution of above indices, it is concluded that the five modal-based indices didn’t have consistency in the identifying and localizing the seismic damage.

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

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