Shear strength prediction for steel-plate composite (SC) member-to-RC member L-joints under cyclic loading: Analytical and regression-based approaches
The steel-plate composite (SC) member to reinforced concrete (RC) member L-joints are critical components in modular nuclear power plant structures, yet they are vulnerable to brittle shear failure under cyclic loading due to their inherent weaknesses. However, limited research has been conducted on their shear strength evaluation. This study proposes an analytical model based on the softened strut-and-tie model (SSTM) framework to predict the shear strength of SC-RC L-joints. The model incorporates nonlinear flexural theory to derive precise formulas for the depth of diagonal struts. It overcomes the limitations of conventional SSTM approaches by capturing asymmetric deformation characteristics and the distinct confinement effects from different connected members. Verification against experimental results demonstrates that the proposed model provides accurate strength predictions and offers deeper insight into the joint failure mechanisms. Furthermore, due to the overestimation of shear strength by existing design codes, an explicit regression-based formula is proposed to enhance prediction accuracy and facilitate practical engineering design.
Authors
- Quanquan Guo (ORCID: https://orcid.org/0000-0001-5376-2189)
- Yongwang Lei (ORCID: https://orcid.org/0009-0002-9888-8521)
- Wei Ding (ORCID: https://orcid.org/0009-0004-2098-3419)
- Li‐Yan Xu (ORCID: https://orcid.org/0000-0001-8595-0109)
- Yaowen Yang (ORCID: https://orcid.org/0000-0002-7856-2009)
Institutions
- Nanyang Technological University (SG)
- Beihang University (CN)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Structures
- Published
- 2025-06-16
- DOI
- https://doi.org/10.1016/j.istruc.2025.109426
- Primary Topic
- Structural Load-Bearing Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Scholarship Council
- Beijing Nova Program