Estimating the punching shear capacities of concrete slabs reinforced by steel and FRP rebars with ANN-Based GUI toolbox. (April 2023)

• Record Type: Journal Article
• Authors: Naseri Nasab, Mina; Jahangir, Hashem; Hasani, Hamed; Majidi, Mohammad-Hassan; Khorashadizadeh, Saeed
• Is Part Of: Structures. Volume 50(2023)
• Page Start: 1204
• Page End: 1221
• Publication Date: 2023-04
• Subjects: RC slabs -- Punching shear capacity -- Steel and FRP rebar -- Artificial neural networks -- GUI toolbox; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2023.02.072
• Languages: English
• ISSNs: 2352-0124

Abstract

Graphical abstract: Highlights: The punching shear capacity of RC slabs with steel and FRP rebars was estimated. The columns with circular, square and rectangular cross-sections were considered. Various ANN models with single and multiple hidden layers were implemented. The performance of the proposed models was compared to nine existing models. A GUI toolbox in the Matlab environment was provided to be used in practice. Abstract: In this paper, the punching shear capacity of reinforced concrete (RC) slabs reinforced by steel and fibre-reinforced polymer (FRP) rebars jointed to circular, square, and rectangular columns was estimated by various artificial neural networks (ANNs). A large experimental database, including 164 tests, was compiled to achieve this goal. The influential input parameters contained the cross-section area of the column, the perimeter of the critical section in the RC slab, the effective depth of the RC slab, the modulus of elasticity, the reinforcement ratio of steel and FRP rebars, and the compressive strength of concrete. The results showed that considering 8 neurons in the single hidden layer, named ANN-6-8-1, and respectively 15 and 5 neurons in the first and second hidden layers in multi-layer models, named ANN-6-15-5-1, were the optimized configurations. The results showed the ANN-6-15-5-1 model with an R value of 0.9925, and a MAPE error value of 7.48% is more accurate. Among the existing models, the Ospina et al. and Metwally models, respectively, with R values of 0.9473 and 0.9386 and MAPE values of 18.77% and 15.40 %, were the best ones. Eventually, a graphical user interface (GUI) toolbox is provided to enable the user to calculate the punching shear capacity of RC slabs in practice.