Damage Detection of a Continuous Bridge from Response of a Moving Vehicle

This paper presents a multistage multipass method to identify the damage location of a continuous bridge from the response of a vehicle moving on the rough road surface of the bridge. The vehicle runs over the bridge several times at different velocities and the corresponding responses of the vehicle can be obtained. The vertical accelerations of the vehicle running on the intact and damaged bridges are used for identification. The multistage damage detection method is implemented by the modal strain energy based method and genetic algorithm. The modal strain energy based method estimates the damage location by calculating a damage indicator from the frequencies extracted from the vehicle responses of both the intact and damaged states of the bridge. At the second stage, the identification problem is transformed into a global optimization problem and is solved by genetic algorithm techniques. For each pass of the vehicle, the method can identify the location of the damage until it is determined with acceptable accuracy. A two-span continuous bridge is used to verify the method. The numerical results show that this method can identify the location of damage reasonably well.

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Structural Damage Detection in Space Frames Using Modal Strain Energy Method and Genetic Algorithm

Lecture Notes in Civil Engineering - ICSCEA 2019 ◽

10.1007/978-981-15-5144-4_115 ◽

2020 ◽

pp. 1169-1178

Author(s):

Quang-Huy Le ◽

Van-Phuong Huynh ◽

Minh-Tuan Ha ◽

Duc-Duy Ho

Keyword(s):

Genetic Algorithm ◽

Damage Detection ◽

Energy Method ◽

Structural Damage ◽

Strain Energy ◽

Structural Damage Detection ◽

Modal Strain Energy ◽

Modal Strain Energy Method ◽

Space Frames ◽

Strain Energy Method

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Composite Skin/Stringer Panel Damage Detection Based on Modal Strain Energy and Neural Network Technique

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.322 ◽

2011 ◽

Vol 66-68 ◽

pp. 322-327 ◽

Cited By ~ 3

Author(s):

Jia Hui ◽

Xiao Peng Wan ◽

Mei Ying Zhao

Keyword(s):

Neural Network ◽

Numerical Simulation ◽

Damage Detection ◽

Strain Energy ◽

Modal Strain Energy ◽

Structure Damage ◽

Damage Indicator ◽

Material Stiffness ◽

Stiffness Properties

This paper proposed a new method to detect the damage of composite skin/stringer panel structure using modal strain energy combined with neural network. The change ratio of element modal strain energy is choosen as damage indicator because of it’s highly sensitivity to the location and severity of structure damage. Neural network here play the role of a tool to indentity the damage according to the change ratio of modal strain energy. To achive this, a three layers neural network model is built and the BP arithmetic is used. The proposed method is validated using a numerical simulation of a composite skin/stringer panel with damages in some elements of its FEM mode, which are simulated by reducing elements’ material stiffness properties. The result shows that, this method is robust, accurate and highly efficient with the maximal error limited in 10%.

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Two-Stage Damage Identification Based on Modal Strain Energy and Revised Particle Swarm Optimization

International Journal of Structural Stability and Dynamics ◽

10.1142/s0219455414400057 ◽

2014 ◽

Vol 14 (05) ◽

pp. 1440005 ◽

Cited By ~ 8

Author(s):

Sheng-Lan Ma ◽

Shao-Fei Jiang ◽

Liu-Qing Weng

Keyword(s):

Parameter Estimation ◽

Particle Swarm Optimization ◽

Damage Detection ◽

Strain Energy ◽

Particle Swarm ◽

Noise Tolerance ◽

Two Stage ◽

Modal Strain Energy ◽

Swarm Optimization ◽

Second Stage

This paper presents a novel two-stage damage detection method by integrating modal strain energy and revised particle swarm optimization (RPSO). In the first stage, the modal strain energy change ratio (MSECR) is used to roughly identify the locations of damaged elements via an appropriate MSECR threshold which is determined through parameter estimation. In the second stage, RPSO that integrates evolutionary theory with general PSO is used to precisely locate and quantify the damage with the gravity position of the selected excellent particles in the current entire population taken into consideration. Two numerical simulations and a seven-story steel frame experiment in laboratory conditions are performed to validate the proposed method, and a comparison is made between the proposed approach and existing methods. The results show that: (1) the proposed method can not only effectively locate damage, but also accurately evaluate the extent of damage. Meanwhile, it also enjoys good noise-tolerance and adaptability; (2) the damage threshold of the MSECR presented in this paper can be determined by the parameter estimation and reliability index, and then used to reduce the number of elements to be analyzed and to improve the computation efficiency in the second stage; and (3) compared with general PSO algorithm, RPSO is more efficient and robust for damage detection with a better noise-tolerance. This study shows that the proposed method can provide a reliable and fast tool to accurately identify, locate and quantify single- and multi-damage of complex engineering structures.

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