Structural damage identification with multi-objective DIRECT algorithm using natural frequencies and single mode shape

2017 ◽  
Author(s):  
Pei Cao ◽  
David Yoo ◽  
Qi Shuai ◽  
J. Tang
Keyword(s):  
Mode Shape ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Single Mode ◽  
Direct Algorithm ◽  
Multi Objective ◽  
Structural Damage Identification

Experimental Verification of the Structural Damage Identification Method Developed for Beam Structures

2006 ◽  
Vol 326-328 ◽  
pp. 1113-1116
Author(s):  
Deokki Youn ◽  
Usik Lee ◽  
Oh Yang Kwon
Keyword(s):  
Experimental Verification ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Mode Shapes ◽  
Damage State ◽  
Intact State ◽  
Identification Method ◽  
Structural Damage Identification ◽  
Cantilevered Beam

In this paper, an experimental verification has been conducted for a frequency response function (FRF)-based structural damage identification method (SDIM) proposed in the previous study [1]. The FRF-based SDIM requires the natural frequencies and mode shapes measured in the intact state and the FRF-data measured in the damaged state. Experiments are conducted for the cantilevered beam specimens with one and three slots. It is shown that the proposed FRF-based SDIM provides damage identification results that agree quite well with true damage state.

scholarly journals A Multi-Objective DIRECT Algorithm Toward Structural Damage Identification With Limited Dynamic Response Information

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Pei Cao ◽  
Qi Shuai ◽  
Jiong Tang
Keyword(s):  
Finite Element ◽  
Dynamic Response ◽  
Structural Damage ◽  
Optimization Problem ◽  
Damage Identification ◽  
Direct Approach ◽  
Direct Algorithm ◽  
Sensitivity Matrix ◽  
Multi Objective ◽  
Response Information

A major challenge in structural health monitoring (SHM) is to accurately identify both the location and severity of damage using the dynamic response information acquired. While in theory the vibration-based and impedance-based methods may facilitate damage identification with the assistance of a credible baseline finite element model, the response information is generally limited, and the measurements may be heterogeneous, making an inverse analysis using sensitivity matrix difficult. Aiming at fundamental advancement, in this research we cast the damage identification problem into an optimization problem where possible changes of finite element properties due to damage occurrence are treated as unknowns. We employ the multiple damage location assurance criterion (MDLAC), which characterizes the relation between measurements and predictions (under sampled elemental property changes), as the vector-form objective function. We then develop an enhanced, multi-objective version of the dividing rectangles (DIRECT) approach to solve the optimization problem. The underlying idea of the multi-objective DIRECT approach is to branch and bound the unknown parametric space to converge to a set of optimal solutions. A new sampling scheme is established, which significantly increases the efficiency in minimizing the error between measurements and predictions. The enhanced DIRECT algorithm is particularly suited to solving for unknowns that are sparse, as in practical situations structural damage affects only a small region. A number of test cases using vibration response information are executed to demonstrate the effectiveness of the new approach.

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