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To excite sufficient vibrations of bridges for more effective modal experiments to take place, traveling vehicles were utilized in many bridge field inspection campaigns in history. However, the dynamics of a bridge subjected to the traveling vehicles are of changed resonant properties compared with those of the same structure with the ambient excitation in nature. Therefore, the field modal experiments employing vehicle excitations might unfavorably lead to shifted measurement results of low-order natural frequencies for long-span bridges of large mass. Using Xinjiang Bridge (a single-tower pre-stressed concrete cable-stayed bridge with spans 40m + 150m + 150m + 40m) as the engineering background, this paper validates this technical notion based on reliable physical experiments and numerical simulations and seeks a practical approach to deal with the issue. It is found that the maximum deviation between the low-order modal frequencies measured on Xinjiang Bridge for the vehicle excitation case and those measured for the ambient excitation case (the accurate values) reaches 0.65Hz, and the deviation between the lower limit of the usual vehicle excitation’s energy band and the modal frequency measured (factor 1) and the traveling vehicles’ speed (factor 2) are the two key factors influencing the modal frequency shifts. An empirical model to compensate for modal frequencies’ deviations is formulated considering the two key factors accordingly, which is of high-practical significance in future applications of the modal experiments using vehicle excitations to other long-span bridges. [ABSTRACT FROM AUTHOR]

In this paper, the simple genetic algorithm (SGA) is improved by combining with the simulated annealing algorithm (SAA) for the parameter identification of a reinforced concrete (RC) frame on elastic foundation. SGA adopts parallel search strategy, which is based on the concept of "survival of the fittest" in optimization while SAA adopts a serial form and the process is endowed with time-variety probable jumping property so that local optimization could be prevented. The global searching ability is developed by combining the two methods and the new algorithm is named genetic annealing hybrid algorithm (GAHA). Modal experiments were carried out on a four-storey RC frame structural model with isolated embedded footings in laboratory. The measured natural frequencies and mode shapes have been utilized to identify the physical parameters of the frame by the proposed method. Four cases of concrete elastic modulus and foundation dynamic shear modulus are identified, and the results are compared with the usual sensitivity methods (SM). By model updating, the results show that the elastic modulus of concrete increases with respect to the storey. The identified elastic modulus of the concrete is generally larger than that found by compressive testing because the dynamic modulus of concrete is larger than the static modulus of concrete. The identified soil dynamic shear modulus also increases with the storey since the soil property depends on the pressure exerted on the soil. It is also shown that the identified results by GAHA are better than that of SM. [ABSTRACT FROM AUTHOR]