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.

Abstract-In this paper, an optimization method of wavelet neural network for structure damage identification is established. The suspension bridge is used as the research object. Firstly, wavelet coefficients modulus maxima are obtained by wavelet transform to determine the location of structural damage. Then, the connection weights and thresholds of the neural network are optimized by the particle swarm optimization, the optimized neural network is constructed to determine the degree of damage of the bridge. The validity of the method is verified by numerical simulation of multi span suspension bridge.

Structural damage detection and diagnosis is a very important factor to the structural safety. Wavelet transform combined with artificial immune algorithm can be used for improving the application of artificial intelligence algorithms.for structural damage detection.The result of the simulation and verification by analyzing suspension bridge structure suggest that this approach combines the advantages of wavelet analysis and artificial immune algorithm : quickly and accurately identifying the damage location and calculating the extent of the damage. This method can be used in engineering practice of structural damage detection to improve the efficiency of structural damage identification.

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.

Abstract.Due to disadvantages of premature convergence and poor local optimization, a structural damage identification based on wavelet-genetic stimulated annealing algorithm(abbreviated to SAGA) was proposed.Firstly, wavelet analysis is applied to identify the structural damage location and the number of damaged unit.Then, the objective function is established by using the natural frequencies and modal shapes of the structure, the damage degree of the structure is taken as the design variable.The quantification of the structural damage were performed with genetic-stimulated annealing algorithm.The combination of wavelet analysis and genetic simulated annealing algorithm not only makes up for the deficiency of the two methods, but also reduces the unknown quantity after the wavelet analysis determines the damage location.Finally,a numerical simulation of a continuous beam model was given.The result showed that the damage location and the degree of the structure can be identified well.The effectiveness of the proposed method was verified.