Abstract
A primary objective in the seismic design of structures is to ensure that the capacity of individual members of a structure exceeds the associated demands. For reinforced concrete (RC) columns, several parameters involving steel and concrete material properties control behavior and strength. Furthermore, it is unrealistic to simply consider the shear strength calculation as the sum of concrete and steel contributions while accounting for axial force when, in fact, all those parameters are interacting. Consequently, it is challenging to reasonably estimate the shear capacity of a column while accounting for all the factors. This study investigates the viability of using artificial neural networks (ANN) to estimate the shear capacity of RC columns. Results from ANN are compared with both experimental values and calculated values, using semi-empirical and empirical formulas from the literature. Results show that ANNs are significantly accurate in predicting shear strength when trained with accurate experimental results, and meet or exceed the performance of existing empirical formulas. Accordingly, ANNs could be used in the future for analytical predictions of shear strength of RC members.
Highlights
In the seismic design of structures, it is essential to ensure that the deformation capacities of a structure and its components exceed the associated deformation demands
This study aims to improve upon existing empirical equations and models by implementing artificial intelligence algorithms to predict the shear strength of reinforced concrete (RC) columns based on a number of different variables
Neural networks extend beyond the typical realm of empirically based equations, but have the important requirement of computing power and a meaningful database to predict the shear strength of columns
Summary
In the seismic design of structures, it is essential to ensure that the deformation capacities of a structure and its components exceed the associated deformation demands. This concept is implicitly addressed in capacity-based design procedures, and is an explicit core requirement of displacement-based design procedures. Shear failure of reinforced concrete (RC) members is inherently brittle, resulting in a significant drop in lateral load resistance at low deformation; this is highly undesirable in seismic design. Existing seismic design guidelines for RC structures require special reinforcement for zones where plastic hinges are expected to form in order to ensure that brittle modes of failure are avoided
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