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

Language

English

Available for download on Thursday, May 31, 2018


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