Application of the EKE and LSE-UI Based Substructure Approach for Damage Detection with Limited Output Measurements

2011 ◽  
Vol 255-260 ◽  
pp. 4171-4175
Author(s):  
Ying Lei ◽  
Chao Liu
Keyword(s):  
Damage Detection ◽  
Least Squares ◽  
Structural Damage ◽  
Structural Parameters ◽  
Least Squares Estimation ◽  
Complex Case ◽  
Benchmark Problem ◽  
Unknown Inputs ◽  
Limited Output ◽  
Substructure Approach

This paper presents an effort to apply the EKE (extended Kalman estimator) and LSE-UI (least squares estimation for unknown input) technique to detect structures damage with limited output measurements. This technique can be extended to detect structural local damage in complex structures based on substructure approach. Structural parameters and the unknown inputs are identified by a recursive algorithm based on sequential application of the extended Kalman estimator for the extended state vector and the least squares estimation for the unknown inputs. Only a limited number of measured acceleration responses of the benchmark structure subject to unmeasured excitation inputs are utilized. This structural damage detection method is applied to the ASCE SHM benchmark building to test its efficacy and provide a solution to the complex case of the Phase I benchmark problem. Damage detection results indicate that the proposed technique can detect and localize structural damage of the complex benchmark problem with good accuracy.

A Multi-State Strategy for Structural Damage Detection Using Sensitivity of Weighted Transmissibility Function

2021 ◽  
pp. 2150144
Author(s):  
Ziwei Luo ◽  
Huanlin Liu ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Fe Model ◽  
Limited Information ◽  
Structural Damage Detection ◽  
State Strategy ◽  
Output Only

In practice, a model-based structural damage detection (SDD) method is helpful for locating and quantifying damages with the aid of reasonable finite element (FE) model. However, only limited information in single or two structural states is often used for model updating in existing studies, which is not reasonable enough to represent real structures. Meanwhile, as an output-only damage indicator, transmissibility function (TF) is proven to be effective for SDD, but it is not sensitive enough to change in structural parameters. Therefore, a multi-state strategy based on weighted TF (WTF) is proposed to improve sensitivity of TF to change in parameters and in order to further obtain a more reasonable FE model for SDD in this study. First, WTF is defined by TF weighted with element stiffness matrix, and relationships between WTFs and change in structural parameters are established based on sensitivity analysis. Then, a multi-state strategy is proposed to obtain multiple structural states, which is used to reasonably update the FE model and detect structural damages. Meanwhile, due to fabrication errors, a two-stage scheme is adopted to reduce the global and local discrepancy between the real structure and the FE model. Further, the [Formula: see text]-norm and the [Formula: see text]-norm regularization techniques are, respectively, introduced for both model updating and SDD problems by considering the characteristics of problems. Finally, the effectiveness of the proposed method is verified by a simply supported beam in numerical simulations and a six-storey frame in laboratory. From the simulation results, it can be seen that the sensitivity to structural damages can be improved by the definition of WTF. For the experimental studies, compared with the FE model updated from the single structural state, the FE model obtained by the multi-state strategy has an ability to more reasonably describe the change of states in the frame. Moreover, for the given structural damages, the proposed method can detect damage locations and degrees accurately, which shows the validity of the proposed method and the reliability of the updated FE model.

Innovative Structural Damage Detection of Bridges by Least Squares Adjustment with Constraints

2016 ◽  
Vol 3 (4) ◽  
pp. 942-946
Author(s):  
Brandes Klaus ◽  
Daum Werner ◽  
Hofmann Detlef ◽  
Basedau Frank ◽  
Kubowitz Petra
Keyword(s):  
Damage Detection ◽  
Least Squares ◽  
Structural Damage ◽  
Structural Damage Detection ◽  
Least Squares Adjustment ◽  
Adjustment With Constraints

Online Tracking of Structural Parameters Based on Recursive Subspace Identification

2010 ◽  
Author(s):  
Jian-Huang Weng ◽  
Chin-Hsiung Loh
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Shaking Table Test ◽  
Structural Parameters ◽  
Shaking Table ◽  
External Loading ◽  
Subspace Identification ◽  
Structural System ◽  
Real Time System ◽  
On Line

An important objective of structural health monitoring (SHM) for civil infrastructure is to identify the state of the structure and to detect these changes when it occurred. The changes of features in a structural system may due to the nonlinear inelastic response of structure or due to the structural damage subjected to severe external loading. Therefore, damage detection in large structural system, such as buildings and bridges, can improve safety and reduce maintenance costs and the design of damage detection system is one of the goals of SHM. The objective of this paper is to develop an on-line and almost real-time system parameter estimation and damage detection technique from the response measurements through using the Recursive Subspace identification (RSI) or Recursive Stochastic Subspace Identification (RSSI) approaches. Verification of the proposed method by using data from both numerical simulation and the shaking table test of a steel structure with abrupt change of inter-story stiffness are discussed. With the implementation of forgetting factor in RSSI/RSI methods the ability of on-line damage detection of structural system can be enhanced.

System Identification of High-Rise Building under Unknown Seismic Excitation with Limited Output Measurements

2010 ◽  
Vol 163-167 ◽  
pp. 3947-3951
Author(s):  
Ying Lei ◽  
Chao Liu ◽  
Yong Qiang Jiang
Keyword(s):  
System Identification ◽  
Structural Parameters ◽  
Frequency Equation ◽  
Seismic Excitation ◽  
Element Formulation ◽  
Unknown Inputs ◽  
High Rise ◽  
High Rise Building ◽  
Limited Output ◽  
Shear Building

In this paper, a system identification approach is proposed for high-rise building under unknown seismic excitation with limited output measurements. A high-rise building is decomposed into small size substructures based on its finite element formulation. Interaction effect between adjacent substructures is considered as ‘equivalent known inputs’ to each substructure. Unknown seismic excitation is considered as ‘equivalent unknown inputs’ at the first floor. By sequentially utilizing the extended Kalman estimator for the extended state vectors and the least squares estimation for the ‘equivalent unknown inputs’, structural parameters above the first story of a shear building can be identified. Then, with the analysis of the measured absolute acceleration responses in frequency domain and the peak-picking method for the estimation of the first natural frequency of the building, structural parameters of the first story can be identified from the frequency equation. Finally, the unknown seismic excitation can be identified via the numerical solution of a first-order differential equation. It is shown by a numerical example that the proposed method can identify high-rise building parameters and the seismic excitation with good accuracy.

Identification of Nonlinear Structural Parameters under Unmeasured Excitation

2010 ◽  
Vol 168-170 ◽  
pp. 768-772 ◽  
Author(s):  
Ying Lei ◽  
Yan Wu ◽  
Tao Li
Keyword(s):  
Structural Damage ◽  
Structural Parameters ◽  
Frame Structure ◽  
Recursive Least Squares ◽  
Identification Algorithm ◽  
Unknown Inputs ◽  
Nonlinear Structural ◽  
Nonlinear Elastic ◽  
Shear Frame ◽  
Elastic Shear

Recently, detection of structural damage based on the system identification has received great attention. In this paper, a technique is proposed for the identification of nonlinear structural parameters under unmeasured excitation. The identification algorithm is based on the extended Kalman filter for the extended state vectors including nonlinear parameters and the recursive least squares estimation for the unknown inputs. Two different models are used to simulate nonlinear structures: One is a 4-storey Duffing-type nonlinear elastic shear-frame structure, the other is a 4-storey Bouc-Wen hysteretic shear-frame structure.Two numerical examples are carried out on the two kinds of models. The simulation results demonstrate that the proposed approach is capable of identifying the nonlinear structural parameters and unknown inputs with good accuracy.

Structural Damage Detection Using Auto/Cross-Correlation Functions Under Multiple Unknown Excitations

2014 ◽  
Vol 14 (05) ◽  
pp. 1440006 ◽  
Author(s):  
Pinghe Ni ◽  
Yong Xia ◽  
Siu-Seong Law ◽  
Songye Zhu
Keyword(s):  
Damage Detection ◽  
Correlation Functions ◽  
Structural Damage ◽  
Cross Correlation ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Detection Methods ◽  
Structural System ◽  
Vibration Responses ◽  
Structural System Identification

Traditional structural system identification and damage detection methods use vibration responses under single excitation. This paper presents an auto/cross-correlation function-based method using acceleration responses under multiple ambient white noise or impact excitations. The auto/cross-correlation functions are divided into two parts. One is associated with the structural parameters and the other with the energy of the excitation. These two parts are updated sequentially using a two-stage method. Numerical and experimental studies are conducted to demonstrate the accuracy and robustness of the proposed method. The effects of measurement noise and number of measurement points on the identification results are also studied.

An integrated real-time structural damage detection method based on extended Kalman filter and dynamic statistical process control

2016 ◽  
Vol 20 (4) ◽  
pp. 549-563 ◽  
Author(s):  
Chenhao Jin ◽  
Shinae Jang ◽  
Xiaorong Sun
Keyword(s):  
Kalman Filter ◽  
Process Control ◽  
Damage Detection ◽  
Real Time ◽  
Statistical Process Control ◽  
Extended Kalman Filter ◽  
Structural Damage ◽  
Detection Method ◽  
Structural Parameters ◽  
Statistical Process

Real-time structural parameter identification and damage detection are of great significance for structural health monitoring systems. The extended Kalman filter has been implemented in many structural damage detection methods due to its capability to estimate structural parameters based on online measurement data. Current research assumes constant structural parameters and uses static statistical process control for damage detection. However, structural parameters are typically slow-changing due to variations such as environmental and operational effects. Hence, false alarms may easily be triggered when the data points falling outside of the static statistical process control range due to the environmental and operational effects. In order to overcome this problem, this article presents a novel real-time structural damage detection method by integrating extended Kalman filter and dynamic statistical process control. Based on historical measurements of damage-sensitive parameters in the state-space model, extended Kalman filter is used to provide real-time estimations of these parameters as well as standard derivations in each time step, which are then used to update the control limits for dynamic statistical process control to detect any abnormality in the selected parameters. The numerical validation is performed on both linear and nonlinear structures, considering different damage scenarios. The simulation results demonstrate high detection accuracy rate and light computational costs of the developed extended Kalman filter–dynamic statistical process control damage detection method and the potential for implementation in structural health monitoring systems for in-service civil structures.

Enhanced Sensitivity for Structural Damage Detection Using Incomplete Modal Data

2018 ◽  
Vol 18 (04) ◽  
pp. 1850054 ◽  
Author(s):  
Akbar Esfandiari ◽  
Maryam Vahedi
Keyword(s):  
Damage Detection ◽  
Mode Shape ◽  
Structural Damage ◽  
Structural Model ◽  
Model Updating ◽  
Early Stage ◽  
Structural Parameters ◽  
Accurate Estimation ◽  
Sensitivity Equation ◽  
Shape Data

The necessity of detecting structural damages in an early stage has led to the development of various procedures for structural model updating. In this regard, sensitivity-based model updating methods utilizing mode shape data are known as effective tools. For this purpose, accurate estimation of the mode shape changes is desired to achieve successful model updating. In this paper, Wang’s method is improved by including measured natural frequencies of the damaged structure in derivation of the sensitivity equation. The sensitivity equation is then solved using an incomplete subset of mode shape data in evaluation of the changes of the structural parameters. A comparative study of the results obtained by the proposed method with those by the modal method for a truss and a frame model indicated that the former is significantly more effective for damage detection than the latter. Furthermore, the capability of the proposed method for model updating in the presence of measurement and mass modeling errors is investigated.

Structural Damage Detection and Identification Using Learning Vector Quantization Neural Network

1999 ◽  
Author(s):  
Qinzhong Shi ◽  
Ichiro Hagiwara ◽  
Toshiaki Sekine
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Vector Quantization ◽  
Structural Damage ◽  
Structural Parameters ◽  
Learning Vector Quantization ◽  
Structural Damage Detection ◽  
Practical Application ◽  
Detection And Identification ◽  
Local Stiffness

Abstract This research deals with the structural damage detection by experimental measured modal parameters, such as the modal frequencies and the modal shapes. Changes of local structural parameters, induced by damage, will affect the local stiffness and cause the change of modal frequencies and modal shapes of structure. Use of these observable values to detect the damage of the structure is feasible and implement. Learning Vector Quantization (LVQ) Neural Network based on pattern classifier is used to detect the location of damage, and a method of releasing the dense of input vector to neural network is proposed to increase the accuracy of detection. Several numerical examples show the proposed method is effective to increase the rate of damage detection. Finally, a practical application example of damage detection for a turbine blade is used to demonstrate and verify the approach developed.

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