Damage detection of multi-storeyed shear structure using sparse and noisy modal data

2015 ◽  
Vol 15 (5) ◽  
pp. 1215-1232
Author(s):  
S.K. Panigrahi ◽  
S. Chakraverty ◽  
S.K. Bhattacharyya
Keyword(s):  
Damage Detection ◽  
Modal Data ◽  
Shear Structure

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.

Damage detection based on MCSS and PSO using modal data

2015 ◽  
Vol 15 (5) ◽  
pp. 1253-1270 ◽  
Author(s):  
Ali Kaveh ◽  
Mohsen Maniat
Keyword(s):  
Damage Detection ◽  
Modal Data

An Optimal Hybrid Expansion—Reduction Damage Detection Method

2003 ◽  
Vol 9 (8) ◽  
pp. 983-995 ◽  
Author(s):  
M. Abdalla ◽  
K. Grigoriadis ◽  
D. Zimmerman
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Linear Matrix ◽  
Test Bed ◽  
Detection Problem ◽  
Lmi Optimization ◽  
Modal Data ◽  
Stiffness Matrices

In this paper, we examine the structural damage detection problem with an incomplete set of measurements. Linear matrix inequality (LMI) optimization methods are proposed to solve this hybrid damage detection problem that integrates modal data expansion and model reduction with an LMI based damage detection procedure. In the proposed hybrid approach, the transformation matrix is based on the measured data avoiding the use of the healthy mass and stiffness matrices. The method is demonstrated using experimental modal data obtained from the NASA eight-bay cantilevered truss test bed. The experimental results of this hybrid approach are shown to provide a clearer indication of damage than using stand-alone expansion or reduction techniques.

DATA FUSION-BASED STRUCTURAL DAMAGE DETECTION UNDER VARYING TEMPERATURE CONDITIONS

2012 ◽  
Vol 12 (06) ◽  
pp. 1250052 ◽  
Author(s):  
YUEQUAN BAO ◽  
YONG XIA ◽  
HUI LI ◽  
YOU-LIN XU ◽  
PENG ZHANG
Keyword(s):  
Data Fusion ◽  
Damage Detection ◽  
Temperature Effect ◽  
Test Data ◽  
Mode Shapes ◽  
Detection Accuracy ◽  
Modal Data ◽  
Temperature Conditions ◽  
Uncertainty Measurement ◽  
Structural Responses

A huge number of data can be obtained continuously from a number of sensors in long-term structural health monitoring (SHM). Different sets of data measured at different times may lead to inconsistent monitoring results. In addition, structural responses vary with the changing environmental conditions, particularly temperature. The variation in structural responses caused by temperature changes may mask the variation caused by structural damages. Integration and interpretation of various types of data are critical to the effective use of SHM systems for structural condition assessment and damage detection. A data fusion-based damage detection approach under varying temperature conditions is presented. The Bayesian-based damage detection technique, in which both temperature and structural parameters are the variables of the modal properties (frequencies and mode shapes), is developed. Accordingly, the probability density functions of the modal data are derived for damage detection. The damage detection results from each set of modal data and temperature data may be inconsistent because of uncertainties. The Dempster–Shafer (D–S) evidence theory is then employed to integrate the individual damage detection results from the different data sets at different times to obtain a consistent decision. An experiment on a two-story portal frame is conducted to demonstrate the effectiveness of the proposed method, with consideration on model uncertainty, measurement noise, and temperature effect. The damage detection results obtained by combining the damage basic probability assignments from each set of test data are more accurate than those obtained from each test data separately. Eliminating the temperature effect on the vibration properties can improve the damage detection accuracy. In particular, the proposed technique can detect even the slightest damage that is not detected by common damage detection methods in which the temperature effect is not eliminated.

Sign in / Sign up

Export Citation Format

Share Document