Statistical analysis of the material, geometrical and imperfection characteristics of structural stainless steels and members.

Traditional member-based two-step design approaches included in current structural codes for steel structures, as well as more recent system-based direct-design alternatives, require building rigorous structural reliability frameworks for the calibration of partial coefficients (resistance factors) to achieve specified target reliability requirements. Key design parameters affecting the strength of structures and their random variations are generally modelled by nominal or characteristic values in design standards, which are combined with partial coefficients that need to be calibrated from measurements on real samples. While the statistical characterization of material and geometric properties of structural steels has been consolidated over the last decades, information about the characterization of structural stainless steels is virtually non-existent due to the limited pool of available data. Thus, this paper presents the basic ingredient for developing reliability frameworks for stainless steel structures and components by statistically characterizing the main random parameters affecting their strength through a comprehensive database collected from the literature. Based on the collected data, appropriate probabilistic models are proposed for geometric properties, material properties, imperfections and residual stresses of different stainless steel alloys and cross-section or product types. The data is equally applicable to member-based reliability analyses as described in existing codes and system-based analyses targeted at the direct-design of stainless steel structures by advanced analysis. Unlabelled Image • Data on most relevant random variables is collected through a comprehensive literature review. • Data for different stainless steel alloys and cross-section types are analysed. • The variability of parameters affecting the strength of stainless steel structures is statistically characterized. • Appropriate statistical functions and probabilistic models are provided for geometry, material, imperfections and residual stresses. • Foundational data is provided for complete member- and system-based reliability frameworks, from which safety factors and resistance factors can be derived. [ABSTRACT FROM AUTHOR]