Application of regularization methods to damage detection in large scale plane frame structures using incomplete noisy modal data

2008 ◽  
Vol 30 (11) ◽  
pp. 3219-3227 ◽  
Author(s):  
Hua-Peng Chen
Keyword(s):  
Damage Detection ◽  
Large Scale ◽  
Frame Structures ◽  
Regularization Methods ◽  
Modal Data ◽  
Plane Frame

scholarly journals Damage Detection of 2D Frame Structures using Incomplete Measurements by Optimization Procedure and Model Reduction

2018 ◽  
Vol 2 (3) ◽  
pp. 164 ◽  
Author(s):  
Du Dinh-Cong ◽  
Sang Pham-Duy ◽  
Trung Nguyen-Thoi
Keyword(s):  
Damage Detection ◽  
Objective Function ◽  
Model Reduction ◽  
Structural Damage ◽  
Optimization Procedure ◽  
Element Model ◽  
Frame Structure ◽  
Frame Structures ◽  
Damage Scenarios ◽  
Modal Data

The article presents an effective method for damage assessment of 2D frame structures using incomplete modal data by optimization procedure and model reduction technique. In this proposed method, the structural damage detection problem is defined as an optimization problem, in which a hybrid objective function and the damage severity of all elements are considered as the objective function and the continuous design variables, respectively. The teaching-learning-based optimization (TLBO) algorithm is applied as a powerful optimization tool to solve the problem. In addition, owing to the use of incomplete measurements, an improved reduction system (IRS) technique is adopted to reduce the mass and stiffness matrices of structural finite element model. The efficiency and robustness of the proposed method are validated with a 4-storey (3 bay) steel plane frame involving several damage scenarios without and with measurement noise. The obtained results clearly demonstrate that even the incompleteness and noisy environment of measured modal data, the present method can work properly in locating and estimating damage of the frame structure by utilizing only the first five incomplete modes' data.  This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Sparse damage detection via the elastic net method using modal data

2021 ◽  
pp. 147592172110219
Author(s):  
Rongrong Hou ◽  
Xiaoyou Wang ◽  
Yong Xia
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Damage Identification ◽  
Measurement Data ◽  
Elastic Net ◽  
Sensitivity Matrix ◽  
Regularization Technique ◽  
Modal Data ◽  
Regularization Techniques ◽  
Net Method

The l1 regularization technique has been developed for damage detection by utilizing the sparsity feature of structural damage. However, the sensitivity matrix in the damage identification exhibits a strong correlation structure, which does not suffice the independency criteria of the l1 regularization technique. This study employs the elastic net method to solve the problem by combining the l1 and l2 regularization techniques. Moreover, the proposed method enables the grouped structural damage being identified simultaneously, whereas the l1 regularization cannot. A numerical cantilever beam and an experimental three-story frame are utilized to demonstrate the effectiveness of the proposed method. The results showed that the proposed method is able to accurately locate and quantify the single and multiple damages, even when the number of measurement data is much less than the number of elements. In particular, the present elastic net technique can detect the grouped damaged elements accurately, whilst the l1 regularization method cannot.

L1 Regularized Model Updating for Structural Damage Detection

2018 ◽  
Vol 18 (12) ◽  
pp. 1850157 ◽  
Author(s):  
Yu-Han Wu ◽  
Xiao-Qing Zhou
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Regularization Parameter ◽  
Structural Vibration ◽  
Mode Shapes ◽  
Modal Data ◽  
Vibration Data ◽  
Stiffness Reduction ◽  
Regularized Model

Model updating methods based on structural vibration data have been developed and applied to detecting structural damages in civil engineering. Compared with the large number of elements in the entire structure of interest, the number of damaged elements which are represented by the stiffness reduction is usually small. However, the widely used [Formula: see text] regularized model updating is unable to detect the sparse feature of the damage in a structure. In this paper, the [Formula: see text] regularized model updating based on the sparse recovery theory is developed to detect structural damage. Two different criteria are considered, namely, the frequencies and the combination of frequencies and mode shapes. In addition, a one-step model updating approach is used in which the measured modal data before and after the occurrence of damage will be compared directly and an accurate analytical model is not needed. A selection method for the [Formula: see text] regularization parameter is also developed. An experimental cantilever beam is used to demonstrate the effectiveness of the proposed method. The results show that the [Formula: see text] regularization approach can be successfully used to detect the sparse damaged elements using the first six modal data, whereas the [Formula: see text] counterpart cannot. The influence of the measurement quantity on the damage detection results is also studied.

On the nonlinear behaviour of reinforced concrete frames

1990 ◽  
Vol 17 (5) ◽  
pp. 698-704 ◽  
Author(s):  
F. J. Vecchio ◽  
S. Balopoulou
Keyword(s):  
Reinforced Concrete ◽  
Large Scale ◽  
Load Capacity ◽  
Concrete Strength ◽  
Theoretical Modelling ◽  
Nonlinear Behaviour ◽  
Concrete Frames ◽  
Membrane Action ◽  
Ultimate Load Capacity ◽  
Plane Frame

An experimental investigation is described in which a large-scale reinforced concrete plane frame is tested to study factors contributing to its nonlinear behaviour under short-term loading conditions. The test results indicate that frame behaviour can be significantly affected by second-order influences such as material nonlinearities, geometric nonlinearities, concrete shrinkage, tension stiffening effects, shear deformations, and membrane action. A nonlinear frame analysis procedure, previously developed taking these mechanisms into account, is shown to accurately predict most aspects of behaviour, including deflection response, ultimate load capacity, and failure mechansim. Aspects of the theoretical modelling which are in need of further improvement are also identified. Key words: analysis, behaviour, deformation, frame, large scale, nonlinear, reinforced concrete, strength, test.

Acoustic Emission Source Characterisation in Large-Scale Composite Structures

2011 ◽  
Vol 70 ◽  
pp. 381-386 ◽  
Author(s):  
Mark J. Eaton ◽  
Rhys Pullin ◽  
C.A. Featherston ◽  
Karen M. Holford
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
Composite Structures ◽  
Large Scale ◽  
Amplitude Ratio ◽  
Matrix Cracking ◽  
Out Of Plane ◽  
Critical Components ◽  
Observed Damage ◽  
Buckling Test

Damage detection and location in aerospace composites is currently of great interest in the research community and is being driven by the need to reduce weight of commercial aircrafts and hence make substantial environmental improvements. The increased use of composites as safety critical components has led to the need for development of structural health monitoring (SHM) systems. Acoustic Emission (AE) offers an excellent potential for delivering the necessary information of damage detection to maintenance engineers in terms of location however there are currently no methodologies that can use AE signals to characterise damage sources. This paper explores a methodology for damage characterisation based on measuring the amplitude ratio (MAR) of the two primary plate wave modes, to allow identification of in-plane (matrix cracking) and out-of-plane sources (delamination). Results from a large-scale buckling test show good correlation between signal characterization and observed damage mechanisms.

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