Numerous fatigue crack mechanism models have been proposed based on an in-depth study of material fatigue mechanisms and engineering requirements. However, due to many of the parameters in these models being difficult to determine, their application to engineering is limited. The fatigue crack of the steel structure was calculated based on the improved McEvily model. To begin, based on the theory of linear elastic fracture mechanics, some parameters of the McEvily fatigue crack growth model were deduced and determined by using more reasonable assumptions and empirical formulas. Second, the effectiveness of the improved McEvily fatigue crack growth model was proven by comparison to the results of the improved model with the classical Paris model. Finally, the improved McEvily model was applied to practical engineering, and the typical fatigue crack of steel structure was selected and compared with the results of the Paris model and nominal stress method to verify its feasibility in engineering. The results reveal that the application conditions of the improved McEvily model can be extended from laboratory conditions to practical engineering, and its accuracy is better than that of the Paris model, which can well evaluate the fatigue crack life of steel structures.

Coastal long-span bridges are intensively constructed along the western Pacific coast. While the coast is frequently disturbed by typhoons. Given that a coastal long-span bridge will experience a number of typhoons during its service life, it is important to have an understanding of the wind fields formed by typhoons with different crossing paths and corresponding structural dynamic responses. Wind and acceleration data of the Xihoumen Bridge under the influence of onshore, offshore, and landfalling typhoons were acquired by the structural health monitoring system (SHMS). Wind field features of typhoons with three types of paths were compared. The landfalling Typhoon In-fa was used to investigate the differences of wind-induced structural dynamic responses under the influence of each typhoon structural region. The covariance-driven stochastic subspace identification (SSI-COV) was used to identify the modal parameters of the bridge. The results revealed that wind field features at bridge site varied with typhoon crossing paths. The SSI-COV accurately identified the structural frequencies of first 15 vertical modes using the acceleration data under the influence of spiral rainband. The first eyewall has the most detrimental wind conditions to the bridge because the stiffening girder vibrated violently in this region.