scholarly journals Research on Damage Identification of Nonuniform Microcrack in Beam Structures

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Jia Guo ◽  
Deqing Guan ◽  
Yanran Pan
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Calculation Method ◽  
Structural Damage ◽  
Damage Identification ◽  
Wavelet Coefficient ◽  
Particle Swarm ◽  
Crack Identification ◽  
Swarm Optimization ◽  
Damage Location

Nonuniform microcrack identification is of great significance in mechanical, aerospace, and civil engineering. In this study, the nonuniform crack is simplified as a semielliptical crack, and simplified calculation methods are proposed for damage severity and damage identification of semielliptical cracks. The proposed methods are based on the calculation method for uniform cracks. The wavelet transform and the intelligent algorithm (IA) are used to identify the damage location and the damage severity of the structure, respectively. The singularity of the wavelet coefficient can be used to identify the signal singularity quickly and accurately, and IA efficiently and accurately calculates the structural damage severity. The particle swarm optimization (PSO) algorithm and the genetic algorithm (GA), widely used, are applied to identify the damage severity of the beam. Numerical simulations and experimental analyses of beams with transfixion and semielliptical cracks are carried out to evaluate the accuracy of the semielliptical crack calculation method and the method of wavelet analysis combined with PSO and GA for nonuniform crack identification. The results show that the wavelet-particle swarm optimization (WPSO) and the wavelet-genetic algorithm (WGA) can accurately and efficiently identify the structural semielliptical damage location and severity and that these methods are not easily influenced by noise. The damage severity calculation method for semielliptical cracks can accurately calculate the semielliptical size and can be used to identify damage in beams with semielliptical cracks.

scholarly journals Structural Damage Identification Based on l1Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Minshui Huang ◽  
Yongzhi Lei ◽  
Xifan Li
Keyword(s):  
Particle Swarm Optimization ◽  
Structural Damage ◽  
Damage Identification ◽  
Particle Swarm ◽  
Large Change ◽  
The Novel ◽  
Swarm Optimization ◽  
Structural Damage Identification ◽  
The Mathematical Model ◽  
Bare Bones

Structural damage identification (SDI) plays a major role in structural health monitoring (SHM), which has been demanded by researchers to better face the challenges in the aging civil engineering, such as bridge structure and building structure. Many methods have been developed for the application to the real structures, but there are still some difficulties which result in inaccurate, even false damage identification. As a variant of particle swarm optimization (PSO), bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimization tool. However, it is easy to be trapped in the local optimal state like other PSO algorithms, especially in SDI problems. In order to improve its performance in SDI problems, this paper aims to propose a novel optimization algorithm which is named as bare bones particle swarm optimization with double jump (BBPSODJ) for finding a new solution to the SDI problem in SHM field. To begin with, after the introduction of sparse recovery theory, the mathematical model for SDI is established where an objective function based on l1 regularization is constructed. Secondly, according to the basic theory of the BBPSODJ, a double jump strategy based on the BBPSO is designed to enhance the dynamic of particles, and it is able to make a large change in particle searching scopes, which can improve the search behaviour of BBPSO and prevent the algorithm from being trapped into local minimum state. Thirdly, three optimization test functions and a numerical example are utilized to validate the optimization performance of BBPSO, traditional PSO, and genetic algorithm (GA) comparatively; it is obvious that the proposed BBPSODJ shows great self-adapting property and good performance in the optimization process by introducing the novel double jump strategy. Finally, in the laboratory, an experimental example of steel frame with 4 damage cases is implemented to further assess the damage identification capability of the BBPSODJ with l1 regularization. From the damage identification results, it can be seen that the proposed BBPSODJ algorithm, which is efficient and robust, has great potential in the field of SHM.

Structural Damage Identification Using Improved Particle Swarm Optimization

2012 ◽  
Vol 446-449 ◽  
pp. 3171-3175
Author(s):  
Hui Yong Guo ◽  
Jun Sheng Yuan ◽  
Zheng Liang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Structural Damage ◽  
Damage Identification ◽  
Particle Swarm ◽  
Identification Problem ◽  
Modal Strain Energy ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Structural Damage Identification ◽  
Fusion Theory

In order to solve structural damage identification problem, a damage identification method based on improved Particle Swarm Optimization (IPSO) is presented. First, structural frequency and modal strain energy are utilized to obtain damage information. Then, evidence fusion theory is utilized to integrate the information and preliminarily identify structural damage locations. After the damaged locations are determined, Particle Swarm Optimization (PSO) is used to identify structural damage extent. Some improved strategies are presented to enhance the search efficiency. The simulation results demonstrate that the proposed method can estimate the damage locations and extent with good accuracy.

scholarly journals Structural Damage Identification Based on the Wavelet Transform and Improved Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Jia Guo ◽  
Deqing Guan ◽  
Jianwei Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Optimization Algorithm ◽  
Structural Damage ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Wavelet Coefficients ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Damage Location

A method based on the wavelet transform and improved particle swarm optimization (WIPSO) algorithm is proposed to identify the microdamage of structures. First, the singularity of wavelet coefficients is used to identify the structural damage location, and then, the improved particle swarm optimization (IPSO) algorithm is used to calculate the optimal solution of the objective function of the structural damage location to determine the structural damage severity. To study the performance of WIPSO, the structural microdamage severity is set within 10%, and a numerical simulation and experimental structure under different damage scenarios are considered. In addition, the ability of wavelet coefficients to identify the location of the structural damage under different noise levels is studied. To evaluate the performance of IPSO, the standard particle swarm optimization algorithm with an inertia weight factor of 0.8 (0.8PSO), the genetic algorithm (GA), and the bat algorithm (BA) are also considered. The results show that WIPSO can effectively and accurately identify the structural damage location and severity. Wavelet transform is very robust to the structural damage location. Compared with the standard 0.8PSO and other mainstream algorithms, IPSO has good convergence and performs more stable and more accurate in the identification of structural damage severity.

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