We present a new updating parameters (UPs) selection method to tackle the bottleneck created by having too many UPs and limited measured data in model updating processing. While the model updating is performed by parameter optimization, an ill-conditioned numerical problem may be encountered or the reliability of the result may be unacceptable if too many parameters are used. The selection of UPs thus becomes a key issue, especially for long-span bridges with finite element models that should be divided into at least hundreds of element numbers. A new method is introduced to reduce the number of UPs and retain their physical significance. In this method, original UPs are described by a few macro-parameters based on shape functions. The model subsequently is updated by a normal optimization algorithm, such as the first-order optimization method. Based on a bridge with a three-span continuous beam and a long-span tie-arch, the optimal effects are investigated, with or without a shape function and using different types of shape functions. The results indicate that the effect of the modal updating based on a shape function is more robust than without shape function and the effect of a linear shape function is better than that of a constant value shape function.

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