Design of structural steel members by advanced inelastic analysis with strain limits

Abstract

Structural steel design is traditionally a two step process: first, the internal forces and moments in the structure are determined from a structural analysis. Then, a series of design checks are carried out to assess the strength and stability of individual members. The structural analysis is typically performed using beam finite elements, which are usually not able to capture local buckling explicitly. Instead, the assessment of local buckling and rotation capacity is made through the concept of cross-section classification, which places class-specific restrictions on the analysis type (i.e. plastic or elastic) and defines the cross-section resistance based on idealised stress distributions (e.g. the plastic, elastic or effective moment capacity in bending). This approach is however considered to be overly simplistic and creates artificial steps in the capacity predictions of structural members. A more consistent approach is proposed herein, whereby a second-order inelastic analysis of the structure or structural component is performed using beam finite elements, and strain limits are employed to mimic the effects of local buckling, control the spread of plasticity and ultimately define the structural resistance. The strain limits are obtained from the continuous strength method. It is shown that not only can local buckling be accurately represented in members experiencing uniform cross-sectional deformations along the length, but, by applying the strain limits to strains that are averaged over a defined characteristic length, the beneficial effects of local moment gradients can also be exploited. The proposed method is assessed against benchmark shell finite element results on isolated members subjected to bending, compression and combined loading. Compared to conventional steel design provisions and even to existing advanced design approaches utilising second-order elastic analysis, the proposed design approach provides consistently more accurate capacity predictions.