In civil engineering structures, modal changes produced by environmental conditions, especially temperature, can be equivalent to or greater than the ones produced by damage. Therefore, it is necessary to distinguish the variations in structural properties caused by environmental changes from those caused by structural damages. In this paper, we present a review of the technical literature concerning variations in the vibration properties of civil structures under varying temperature conditions and damage identification methods for bridge structures. First, the literature on the effect of temperature on vibration properties is roughly divided into experimental and theoretical studies. According to the classification of theoretical research methods, the progress in research on the probability analysis method, the artificial intelligence method, and the optimization algorithm method in this field is reviewed. Based on the different methods of experimental research employed in this field, the experimental research is reviewed according to qualitative and quantitative analyses. Then, damage identification methods for bridge structures are reviewed, considering data-based and model-based methods. Finally, different research methods are summarized.
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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