Structural Damage Identification Under Temperature Variations Based on PSO–CS Hybrid Algorithm

Abstract

Using the variations in parameters to detect structural damages has been widely used in damage identification 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 stiffness of the structure, a damage identification method without considering the temperature effects is, in principle, unacceptable. In this study, a damage identification 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 effects in finite element model. Second, a PSO–CS hybrid algorithm is adopted, which applies the updated mechanism of PSO in CS. Third, objective functions comprised of different modal messages with diverse weight coefficients are constructed for the damage identification 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 identification of ASCE Benchmark frame, for which the results indicate a satisfactory accuracy of the effectiveness of the proposed scheme.