Using the variations in parameters to detect structural damages has been widely used in damage identi¯cation of structures. When exposed to varying temperatures, not only the displacements and stresses of a structure will change, but also the elastic modulus of the materials, such as concrete and steel, of which the structure is made. Since the variation in elastic modulus will result in the variation of the sti®ness of the structure, a damage identi¯cation method without considering the temperature e®ects is, in principle, unacceptable. In this study, a damage identi¯cation method using the particle swarm optimization combined with the cuckoo search (PSO-CS) under the noise and temperature environment is proposed. First, the temperature variations are combined with the elastic modulus variation for addressing the temperature e®ects in¯nite element model. Second, a PSO-CS hybrid algorithm is adopted, which applies the updated mechanism of PSO in CS. Third, objective functions comprised of di®erent modal messages with diverse weight coe±cients are constructed for the damage identi¯cation and validated by numerical analysis of a simply supported beam. The results show that the performance of the PSO-CS is better than either PSO or CS individually. Finally, the PSO-CS is applied to the damage identi¯cation of ASCE Benchmark frame, for which the results indicate a satisfactory accuracy of the e®ectiveness of the proposed scheme.
Structures are always exposed to environmental conditions such as varying temperatures and noises; as a consequence, the dynamic features of structures are changed accordingly. But the model-based methods, used to detect damage using optimization algorithms to get global optimal solution, are highly sensitive to environmental conditions, experimental noises, or numerical errors. While the mechanisms of optimization algorithms are limited by local optimal solution, their convergences are not always assured. In the study, a model-based damage-identification method considering temperature variations, comprised of particle swarm optimization and cuckoo search, is implemented to detect structural damage. First, to eliminate the influence of environmental temperature, temperature change is considered as a parameter of structural material elastic modulus. A function relationship is established between environmental temperature and the material elastic modulus, and an objective function composed of natural frequency, mode shape and modal strain energy with different weight coefficients is constructed. Second, the hybrid optimization algorithm, a combination of particle swarm optimization and cuckoo search, is proposed. Third, to solve the problem of optimization algorithm convergence, the optimization performance of the hybrid optimization algorithm is validated by utilizing four benchmark functions, and it is found that the performance of the hybrid optimization algorithm is the best. In order to test the performance of the three algorithms in damage identification, a numerical simply supported beam is adopted. The results show that the hybrid optimization algorithm can identify the damage location and severity under four different damage cases without considering temperature variations and two cases considering temperature variations. Finally, the hybrid optimization algorithm is introduced to test the damage-identification performance of I-40 Bridge, an actual steel–concrete composite bridge under temperature variations, whose results show that the hybrid optimization algorithm can preferably distinguish between real damages and temperature effects (temperature gradient included); its good robustness and engineering applicability are validated.
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