Predicting Shear Strength of RC Columns Using Artificial Neural Networks

  • A Said Architectural Engineering Department, The Pennsylvania State university, 104 Engineering Unit A, University Park, PA.
  • N Gordon Structural Engineering Division, Tesla Motors Inc., Las Vegas, Nevada.
Keywords: reinforced concrete, , neural network, artificial intelligence, shear, seismic, columns

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.

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References

ACI Committee 318 (2011). Building Code Requirements for Structural Concrete (ACI 318-11) and Commentary (ACI 318R-11). Farmington Hills, Mich.: American Concrete Institute.

ACI Committee 318 (2014). Building code requirements for structural concrete (ACI 318-14) and commentary. Farmington Hills, Mich.: American Concrete Institute.

Ascheim, M. A. and J. P. Moehle (1992). Shear Strength and Deformability of RC Bridge Columns Subjected to Inelastic Cyclic Displacements. Report UCB/EERC-92/04. Berkeley, CA: University of California, Earthquake Engineering Research Center.

Biskinis, Dionysis E., George K. Roupakias and Michael N. Fardis (2004). “Degradation of Shear Strength of Reinforced Concrete Members with Inelastic Cyclic Displacements.” ACI Structural Journal V. 101.6: 773-783.

Elwood, K. (2002). “Shake Table Tests and Analytical Studies on the gravity Load Collapse of Reinforced Concrete Frames”. Ph. D. Dissertation, Department of Civil and Environmental Engineering, University of California Berkeley. p. 419.

Federal Emergency Management Agency, FEMA-356 (2000). “Prestandard and Commentary for Seismic Rehabilitation of Buildings.” Washington D.C.

Federal Emergency Management Agency, FEMA-368 (2000),. “NEHRP Recommended Provisions for New Buildings and Other Structures.” Washington D.C.

Haykin, S. (1994). Neural Networks. A Comprehensive Foundation. Macmillan, New York, NY.

Moehle, J. P., K. J. Elwood, and H. Sezen (2002). “Gravity Load Collapse of Building Frames During Earthquakes.” ACI SP-197. Behavior and Design of Concrete Structures for Seismic Performance: 215-238.

Priestley, M. J. N., R. Verma and Y Xiao (1994). “Seismic Shear Strength of Reinforced Concrete Columns.” Journal of the Structural Division, ASCE V. 120.8.

Published
2019-07-26
How to Cite
Said , A., & Gordon , N. (2019). Predicting Shear Strength of RC Columns Using Artificial Neural Networks. Journal of Building Materials and Structures, 6(2), 64-76. https://doi.org/10.5281/zenodo.3352303
Section
Original Articles