scholarly journals Structural Damage Identification of Bridges from Passing Test Vehicles

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4035 ◽  
Author(s):  
Yang Yang ◽  
Yuanhao Zhu ◽  
Li Wang ◽  
Bao Jia ◽  
Ruoyu Jin
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Least Squares Method ◽  
Field Tests ◽  
Bending Stiffness ◽  
Frequency Domain Method ◽  
Residual Vector ◽  
Test Vehicle ◽  
Domain Method ◽  
Structural Damage Identification

This paper presents two approaches for the structural damage identification of a bridge from the dynamic response recorded from a test vehicle during its passage over the bridge. Using the acceleration response recorded by the vibration sensors mounted on a test vehicle during its passage over the bridge, along with the computed displacement response, the bending stiffness of the bridge can be determined using either: (1) the frequency-domain method based on the improved directed stiffness method with the identified frequency and corresponding mode shape, or (2) the time-domain method based on the residual vector of the least squares method with a fourth-order displacement moment. By comparing the bending stiffness values identified from the vehicle-collected data for the bridge under the undamaged and damaged states that are monitored regularly by the test vehicle, the bridge damage location and severity can be identified. Through numerical simulations and field tests, the present approaches are shown to be effective and feasible.

A Novel Frequency-Domain Method of Structural Damage Identification

2001 ◽  
Author(s):  
Usik Lee ◽  
Jinho Shin
Keyword(s):  
Frequency Domain ◽  
Structural Damage ◽  
Damage Identification ◽  
Dynamic Stiffness ◽  
Equation Of Motion ◽  
Frequency Domain Method ◽  
Response Functions ◽  
Structural Damage Identification ◽  
Measured Frequency Response ◽  
Frequency Domain Approach

Abstract This paper introduces a frequency-domain approach of structural damage identification method (SDIM). The present SDIM is formulated from the exact dynamic stiffness matrix (DSM) equation of motion and then applied to beam structures. The appealing features of the present SDIM are: (1) it needs the DSM only for intact structure, (2) the excitation forces and the measured frequency response functions (FRFs) of damaged structure are only the required input data, and (3) it can locate and quantify many local damages at a time. The feasibility of the present SDIM is verified through some numerically simulated damage identification tests.

Optimal Sensor Placement Algorithm for Structural Damage Identification

2020 ◽  
Vol 14 (1) ◽  
pp. 69-81
Author(s):  
C.H. Li ◽  
Q.W. Yang
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Sensor Placement ◽  
Optimization Procedure ◽  
Frame Structure ◽  
Truss Structures ◽  
Optimal Sensor Placement ◽  
Structural Damage Identification ◽  
Placement Algorithm ◽  
Sensor Locations

Background: Structural damage identification is a very important subject in the field of civil, mechanical and aerospace engineering according to recent patents. Optimal sensor placement is one of the key problems to be solved in structural damage identification. Methods: This paper presents a simple and convenient algorithm for optimizing sensor locations for structural damage identification. Unlike other algorithms found in the published papers, the optimization procedure of sensor placement is divided into two stages. The first stage is to determine the key parts in the whole structure by their contribution to the global flexibility perturbation. The second stage is to place sensors on the nodes associated with those key parts for monitoring possible damage more efficiently. With the sensor locations determined by the proposed optimization process, structural damage can be readily identified by using the incomplete modes yielded from these optimized sensor measurements. In addition, an Improved Ridge Estimate (IRE) technique is proposed in this study to effectively resist the data errors due to modal truncation and measurement noise. Two truss structures and a frame structure are used as examples to demonstrate the feasibility and efficiency of the presented algorithm. Results: From the numerical results, structural damages can be successfully detected by the proposed method using the partial modes yielded by the optimal measurement with 5% noise level. Conclusion: It has been shown that the proposed method is simple to implement and effective for structural damage identification.

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