Elastic critical shear buckling stress of large-scale corrugated steel web used in bridge girders

Abstract

The shear buckling behaviors of corrugated steel plates (CSPs) have continually been investigated over the past decades. However, most studies did not take the dimension differences of CSPs in buildings or culverts and bridge girders into account. Compared to the CSPs in buildings or culverts, the plate thickness and corrugation wavelength of the corrugated steel web (CSW) in bridge girders have been significantly enlarged to bear the much larger shear force, while the overall dimensions (web height and length) keep similar. These differences may lead to deviations in predicting the shear buckling stresses of large-scale CSWs using previous formulas. This paper aims to propose a new calculation method for the elastic critical shear buckling stress of large-scale CSWs in bridge girders based on finite element (FE) analyses. Firstly, the transition web heights between local and interactive buckling, interactive and global buckling are obtained, which can be used to determine the critical shear buckling mode for a given CSW. Subsequently, the previous formulas for predicting the elastic critical global, local and interactive shear buckling stresses are evaluated. The results showed that under simply supported boundary conditions, the global and local shear buckling stresses of large-scale CSWs are considerably underestimated and overestimated, respectively, and considerable scatter in the predictions of interactive shear buckling stress is observed by using different exponent n. Then, the effects of geometric parameters on the global shear buckling coefficient (kG), local shear buckling coefficient (kL), and interactive shear buckling coefficient (kI) are analyzed, and the fitting formulas to evaluate kG, kL, and kI of large-scale CSWs are recommended. Finally, a new calculation method for predicting the elastic critical shear buckling stress of large-scale CSWs is proposed, which could simplify the calculation process and improve the prediction accuracy.