Damage Detection Using Static Response Data and Optimality Criterion

2007 ◽  
pp. 685-696 ◽  
Author(s):  
Kevin Truman ◽  
Gus Terlaje
Keyword(s):  
Damage Detection ◽  
Optimality Criterion ◽  
Static Response ◽  
Response Data

scholarly journals A Universal Fast Algorithm for Sensitivity-Based Structural Damage Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Q. W. Yang ◽  
J. K. Liu ◽  
C.H. Li ◽  
C.F. Liang
Keyword(s):  
Damage Detection ◽  
Fast Algorithm ◽  
Structural Damage ◽  
Research Area ◽  
Detection Methods ◽  
Structural Damage Detection ◽  
Response Data ◽  
Flexibility Matrix ◽  
New Research ◽  
Measured Response

Structural damage detection using measured response data has emerged as a new research area in civil, mechanical, and aerospace engineering communities in recent years. In this paper, a universal fast algorithm is presented for sensitivity-based structural damage detection, which can quickly improve the calculation accuracy of the existing sensitivity-based technique without any high-order sensitivity analysis or multi-iterations. The key formula of the universal fast algorithm is derived from the stiffness and flexibility matrix spectral decomposition theory. With the introduction of the key formula, the proposed method is able to quickly achieve more accurate results than that obtained by the original sensitivity-based methods, regardless of whether the damage is small or large. Three examples are used to demonstrate the feasibility and superiority of the proposed method. It has been shown that the universal fast algorithm is simple to implement and quickly gains higher accuracy over the existing sensitivity-based damage detection methods.

Damage Identification in Beams Using Experimental Data

2014 ◽  
Vol 607 ◽  
pp. 21-29 ◽  
Author(s):  
E.U.L. Palechor ◽  
R.S.Y.C. Silva ◽  
L.M. Bezerra ◽  
T.N. Bittencourt
Keyword(s):  
Numerical Methods ◽  
Damage Detection ◽  
Damage Identification ◽  
Point Load ◽  
Static Response ◽  
The Finite Element Method ◽  
Precise Analysis ◽  
Damage Simulation ◽  
Non Destructive ◽  
Element Method

There are several techniques for non-destructive damage detection in structures. However, they are costly and require a very precise analysis of the extent of the structure. Numerical methods can help in nondestructive testing of structures, showing the possible location of the damage and thereby decreasing the area of analysis and constituting less expensive non-destructive tests. Outstanding among the numerical methods most used to detect damage are the finite element method and the boundary element method. This paper presents the application of wavelet transform for damage detection to the static response of a beam (profile I) simply supported with a point load. Damage simulation was achieved using saw cuts in the top and bottom flanges of the beam.

Unsupervised deep learning approach using a deep auto-encoder with an one-class support vector machine to detect structural damage

2020 ◽  
pp. 147592172093405
Author(s):  
Zilong Wang ◽  
Young-Jin Cha
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Damage Detection ◽  
Structural Damage ◽  
Training Data ◽  
Support Vector ◽  
Acceleration Response ◽  
Damage Scenarios ◽  
Response Data ◽  
Unsupervised Deep Learning

This article proposes an unsupervised deep learning–based approach to detect structural damage. Supervised deep learning methods have been proposed in recent years, but they require data from an intact structure and various damage scenarios of monitored structures for their training processes. However, the labeling work on the training data is typically time-consuming and costly, and sometimes collecting sufficient training data from various damage scenarios of infrastructures in service is impractical. In this article, the proposed unsupervised deep learning method based on a deep auto-encoder with an one-class support vector machine only uses the measured acceleration response data acquired from intact or baseline structures as training data, which enables future structural damage to be detected. The major contributions and novelties of the proposed method are as follows. First, an appropriate deep auto-encoder is carefully designed through comparative studies on the depth of neural networks. Second, the designed deep auto-encoder is taken as an extractor to obtain damage-sensitive features from the measured acceleration response data, and an one-class support vector machine is used as a damage detector. Third, experimental and numerical studies validate the high accuracy of the proposed method for damage detection: a 97.4% mean average for a 12-story numerical building model and a 91.0% accuracy for a laboratory-scaled steel bridge. Fourth, the proposed method also detects light damage (i.e. a 10% reduction in stiffness) with 96.9% to 99.0% accuracy, which shows its superior performance compared with the current state of the art. Fifth, it provides stable and more robust damage detection performance with reduced tuning parameters.

Feature Extraction for Damage Detection in Structures Based on Nonlinearity Analysis

2009 ◽  
Vol 413-414 ◽  
pp. 627-634 ◽  
Author(s):  
Zhi Ke Peng ◽  
Z.Q. Lang ◽  
C. Wolters ◽  
S.A. Billings
Keyword(s):  
Damage Detection ◽  
Moving Average ◽  
Nonlinear System Identification ◽  
Output Frequency ◽  
Response Data ◽  
Modelling Method ◽  
Auto Regressive ◽  
Identification Approach ◽  
Engineering Significance ◽  
Measured Response

In the present study, a nonlinear system identification approach known as NARMAX (Nonlinear Auto-Regressive Moving Average with eXogenous Inputs) modelling method and the NOFRF (Nonlinear Output Frequency Response Function) are introduced to detect damage in plate. A set of NOFRF-based damage features is proposed, and the procedure about how to extract the features from the measured response data is presented in detail. An experimental application to the detection of damages in aluminium plates demonstrates the effectiveness and engineering significance of the new damage detection technique.

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