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Can I read Shear strength of circular concrete-filled tube (CCFT) members using human-guided artificial intelligence approach on EtoBox?
Shear strength of circular concrete-filled tube (CCFT) members using human-guided artificial intelligence approach by Abdullah Alghossoon; Ahmad Tarawneh; Ghassan Almasabha; Yasmin Murad; Eman Saleh; Hamza Abu yahia; Abdallah Abu yahya; Haitham Sahawneh is a Engineering article available to read on EtoBox.
What is Shear strength of circular concrete-filled tube (CCFT) members using human-guided artificial intelligence approach about?
The complex shear behavior ofcircular concrete-filled tube (CCFT) members has been a challenge for an adequate design equation. Collapses due to shear failure are primarily seen in shear links, pile foundations, and coupling beams in composite shear walls. The current design provisions are based on limited experimental data, leading to very conservative expressions of shear strength. The recent advances in Artificial Intelligence (AI) technologies provided an opportunity to establish design models directly from the data with no need to postulate a mathematical expression. This study utilized three AI techniques alongside 141 experimental test results from the literature to overcome the complex behavior of the CCFT members by proposing reliable design equations/models. Namely, Gaussian Processing Regression (GPR), Gene Expression Programming (GEP) and Nonlinear Regression (NR) analysis. The predictor variables include axial loading, materials properties, section slenderness ratio and shear span ratio. This paper sheds light on the current data-based techniques in solving complex structural problems by addressing the noted AI methods and their application in predicting the shear capa
Who reads Shear strength of circular concrete-filled tube (CCFT) members using human-guided artificial intelligence approach?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Abdullah Alghossoon; Ahmad Tarawneh; Ghassan Almasabha; Yasmin Murad; Eman Saleh; Hamza Abu yahia; Abdallah Abu yahya; Haitham Sahawneh
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)