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Predictive model for bidirectional shear strength of reinforced concrete columns subjected to biaxial cyclic loading
Highlights GEP models are proposed to predict the biaxial shear strength of columns subjected to biaxial cyclic loading. The models were developed using several parameters that predominantly control the biaxial shear strength. The models were compared to the ACI-318 formulation of the uniaxial shear strength.
Abstract Corner columns are subjected to bidirectional lateral loading in three-dimensional framed structures during seismic events, which can deteriorate their shear strength. The majority of the design codes do not consider the effect of biaxial lateral loading in the calculation of the column shear strength, and hence an accurate formulation that can predict the bidirectional shear strength is still lacking. Gene Expression Programming (GEP) was used in this research to predict the bidirectional shear strength of columns subjected to biaxial cyclic loads based on a database collected from different experimental studies found in the literature. Based on the previous experimental studies, it is found that the biaxial shear strength of RC columns are predominantly controlled by the column axial load, cross-sectional area of the columns, column shear reinforcement area, concrete compressive strength, and the height of the column. These parameters were used to develop the GEP models in this research. The models were statistically evaluated in order to ensure their applicability, and then they were compared to the ACI-318 formulation of the uniaxial shear strength. The R2 value obtained using the ACI-318 formulations is low compared to that obtained using the GEP models, where the R2 value obtained using the ACI formulation is 0.35, and that obtained using the first and second GEP models are 0.87 and 0.73, respectively. This confirms that the GEP models are more fitting to the experimental results compared to the ACI-318 formulation.
Predictive model for bidirectional shear strength of reinforced concrete columns subjected to biaxial cyclic loading
Highlights GEP models are proposed to predict the biaxial shear strength of columns subjected to biaxial cyclic loading. The models were developed using several parameters that predominantly control the biaxial shear strength. The models were compared to the ACI-318 formulation of the uniaxial shear strength.
Abstract Corner columns are subjected to bidirectional lateral loading in three-dimensional framed structures during seismic events, which can deteriorate their shear strength. The majority of the design codes do not consider the effect of biaxial lateral loading in the calculation of the column shear strength, and hence an accurate formulation that can predict the bidirectional shear strength is still lacking. Gene Expression Programming (GEP) was used in this research to predict the bidirectional shear strength of columns subjected to biaxial cyclic loads based on a database collected from different experimental studies found in the literature. Based on the previous experimental studies, it is found that the biaxial shear strength of RC columns are predominantly controlled by the column axial load, cross-sectional area of the columns, column shear reinforcement area, concrete compressive strength, and the height of the column. These parameters were used to develop the GEP models in this research. The models were statistically evaluated in order to ensure their applicability, and then they were compared to the ACI-318 formulation of the uniaxial shear strength. The R2 value obtained using the ACI-318 formulations is low compared to that obtained using the GEP models, where the R2 value obtained using the ACI formulation is 0.35, and that obtained using the first and second GEP models are 0.87 and 0.73, respectively. This confirms that the GEP models are more fitting to the experimental results compared to the ACI-318 formulation.
Predictive model for bidirectional shear strength of reinforced concrete columns subjected to biaxial cyclic loading
Zuhair Murad, Yasmin (author)
Engineering Structures ; 244
2021-06-27
Article (Journal)
Electronic Resource
English
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