Award Date
5-1-2015
Degree Type
Thesis
Degree Name
Master of Science in Engineering (MSE)
Department
Civil and Environmental Engineering
First Committee Member
Aly M. Said
Second Committee Member
Pramen P. Shrestha
Third Committee Member
Neil Opfer
Fourth Committee Member
Matt Pedersen
Number of Pages
76
Abstract
The shear strength and deformation capacities of reinforced concrete (RC) columns are governed by a multitude of variables related to material properties of the steel and concrete used in the design and construction of the columns. Predicting performance of RC columns using design variables is a complex, non-linear problem. The prediction of shear strength and ductility for these types of structural members has historically been performed using empirically or semi-empirically derived formulae based on experimental results. The introduction of cyclical lateral loading, such as the forces imposed on a structure during an earthquake, can result in severe degradation of shear strength and ductility as load cycles continue. This can increase the complexity of predicting performance even further, as shear failure of the column occurs at relatively low deformations and can significantly affect the ability of the structure to resist lateral loading. Most existing models consider monotonic loading only and do not address this at all, which can result in extremely poor structural performance in a seismic event when compared to performance predictions.
Keywords
Artificial neural network; Buildings; Reinforced concrete – Earthquake effects; Earthquakes; Genetic algorithm; Hysteresis; Lateral loads; Neural networks (Computer science); Seismic response; Shear (Mechanics); Shear failure
Disciplines
Civil Engineering | Geotechnical Engineering | Structural Materials
File Format
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Gordon, Nicholas, "Prediction of Shear Strength and Ductility of Cyclically Loaded Reinforced Concrete Columns Using Artificial Intelligence" (2015). UNLV Theses, Dissertations, Professional Papers, and Capstones. 2356.
http://dx.doi.org/10.34917/7645900
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Included in
Civil Engineering Commons, Geotechnical Engineering Commons, Structural Materials Commons