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A damage regression identification method for large and complex transmission tower structures subjected to static loads was proposed based on the substructure model reduction and data-driven method. According to the structural features of the transmission tower and its deformation under self-weight and ice loading, the full finite element model for the tower was reduced by means of the sub-structure method, the possible damage modes were predicted and the damage indexes defined. The substructure modeling method was used to reduce the orders of the structure with different damage states, and the order reduction model library was established. The calibration load was determined based on the loading characteristics of the tower, and the strain sensor layout was designed according to the deformation and failure modes. The deformations of all the reduced-order models under calibration loads were numerically simulated with the finite element method, and a dataset was then created. With the data measured by the strain sensors as input and the damage indexes as output, a damage regression identification model was built by the BP neural network algorithm. With the identification model, the damage locations can be recognized and the damage indexes can be quantified. This work lays a foundation for real-time health monitoring of transmission tower structures. [ABSTRACT FROM AUTHOR]