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)...
| Authors: | , , |
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| Format: | article |
| Publication Date: | 2020 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/331159 |
| Online Access: | https://hdl.handle.net/2117/331159 https://dx.doi.org/10.1016/j.jcsr.2020.106378 |
| Access Level: | Open access |
| Keyword: | Stainless steel Stainless steel structures Statistical data Reliability calibration Geometric properties Material properties Imperfections Residual stresses Acer inoxidable -- Estructures Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Materials i estructures metàl·liques |
| Summary: | 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. |
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