scholarly journals Damage Identification for Underground Structure Based on Frequency Response Function

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3033 ◽  
Author(s):  
Shengnan Wang ◽  
Xiaohong Long ◽  
Hui Luo ◽  
Hongping Zhu
Keyword(s):  
Frequency Response ◽  
Modal Analysis ◽  
Response Function ◽  
Structural Damage ◽  
Frequency Response Function ◽  
Damage Identification ◽  
Underground Structure ◽  
Measurement Data ◽  
Scale Model ◽  
Design Parameters

Damage identification that is based on modal analysis is widely used in traditional structural damage identification. However, modal analysis is difficult in high damping structures and modal concentrated structures. Unlike approaches based on modal analysis, damage identification based on the frequency response function allows for the avoidance of error and easy verification through other test points. An updating algorithm is devised is this study by utilizing the frequency response function together with the dynamic reduction with respect to the selected design parameters. Numerical results indicate that the method can be used to define multiple parameters with large variation and incomplete measurement data and is robust against measurement noise. With the purpose of avoiding the occurrence of resonance and gaining additional information, the trial and error method has been used to choose a proper frequency. Furthermore, an experimental scale model in a soil box is subjected to the excitation of moving load to validate the effectiveness of the damage identification approach. The improved damage identification method for underground structures, which is based on the analysis of the frequency response function, can be adopted as an efficient and functional damage identification tool.

Structural Damage Identification Based on Strain Frequency Response Functions

2018 ◽  
Vol 18 (12) ◽  
pp. 1850159 ◽  
Author(s):  
Fariba Shadan ◽  
Faramarz Khoshnoudian ◽  
Akbar Esfandiari
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Dynamic Characteristics ◽  
Structural Damage ◽  
Frequency Response Function ◽  
Damage Identification ◽  
Model Updating ◽  
Measurement Data ◽  
Sensitivity Equation ◽  
Strain Frequency

Damage identification using the sensitivity of the dynamic characteristics of the structure of concern has been studied considerably. Among the dynamic characteristics used to locate and quantify structural damages, the frequency response function (FRF) data has the advantage of avoiding modal analysis errors. Additionally, previous studies demonstrated that strains are more sensitive to localized damages compared to displacements. So, in this study, the strain frequency response function (SFRF) data is utilized to identify structural damages using a sensitivity-based model updating approach. A pseudo-linear sensitivity equation which removes the adverse effects of incomplete measurement data is proposed. The approximation used for the sensitivity equation utilizes measured natural frequencies to reconstruct the unmeasured SFRFs. Moreover, new approaches are proposed for selecting the excitation and measurement locations for effective model updating. The efficiency of the proposed method is validated numerically through 2D truss and frame examples using incomplete and noise polluted SFRF data. Results indicate that the method can be used to accurately locate and quantify the severity of damage.

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.

scholarly journals Uncertainties Consideration in Empirical Frequency Response Function Data for Damage Identification Based On Artificial Neural Network

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Khairul H. Padil ◽  
◽  
Norhisham Bakhary ◽  
Wan Nur Firdaus Wan Hassan ◽  
Nadirah Darus ◽  
...  
Keyword(s):  
Frequency Response ◽  
Response Function ◽  
Structural Damage ◽  
Frequency Response Function ◽  
Damage Identification ◽  
Principal Component ◽  
Steel Truss Bridge ◽  
Artificial Neural ◽  
Actual Damage ◽  
Truss Bridge

The modern application of frequency response function (FRF) with artificial neural networks (ANN) has become one of the leading methods in vibration-based damage detection approach. However, since full-size empirically obtained FRF data is used as ANN input, a broad composition ANN input layer series would occur. Consequently, principal component analysis (PCA) is adopted to compress the FRF data magnitude. Despite this, PCA alone is unable to select the important FRF data features effectively, due to the exceedingly FRF data size in addition with existing uncertainties. Therefore, this study proposed the merger of a non-probabilistic analysis and ANN approach with PCA by considering the uncertainties effect and the inefficiency of using empirical FRF data. The empirical FRF data is obtained from a steel truss bridge structure. The results show that the PoDE values above 95% are measured at the particular executed damage locations and the DMI values show the damage severity at the actual damage locations. Overall, the results show that the proposed method is capable in considering the uncertainties effect on the empirical FRF data for structural damage identification.

Study on Benchmark Structure Damage Identification Base on Frequency Response Function and Genetic Algorithm

2010 ◽  
Vol 163-167 ◽  
pp. 2765-2769 ◽  
Author(s):  
Wan Jie Zou ◽  
Zhen Luo ◽  
Guo En Zhou
Keyword(s):  
Genetic Algorithm ◽  
Frequency Response ◽  
Response Function ◽  
Frequency Response Function ◽  
Damage Identification ◽  
Fitness Function ◽  
Floating Point ◽  
Combined Method ◽  
Structure Damage ◽  
Floating Point Number

A combined method for the Benchmark structure damage identification base on the frequency response function(FRF) and genetic algorithm(GA) is presented. The reducing factors of element stiffness are used as the optimization variables, and the cross signature assurance criterion (CSAC) of the test FRF and the analysis FRF is used to constructing the optimization object function and the fitness function of the GA. To avoid the weakness of binary encoding, the floating point number encoding is used in the GA. At last, the Benchmark structure established by IASC-ASCE SHM group is caculated by the proposed method, the results show that even if the serious testing noise is considered, the patterns of damage of the Benchmark structure can be identified well. The effectiveness of the presented method is verified.

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