Honors Theses

Date of Award

2016

Document Type

Undergraduate Thesis

Department

Civil Engineering

First Advisor

Elizabeth Ervin

Relational Format

Dissertation/Thesis

Abstract

With the current decaying condition of America's infrastructure, a need for a more efficient inspection method is evident. The combination of Structural Health Monitoring data and data analysis tools could be a solution. In this study, laboratory data was captured for both a baseline bare-frame and a wooden wall reinforced steel structure. A MATLAB®-based computer program, the in-house SHE™ accomplishes an array of tasks, including signal processing and modal decomposition among others. A SAP2000® model contextualized the experimental data by producing trend behaviors, such as mode order or common mode shapes. Both sequential and cumulative reinforcement detection was performed on the cases of a baseline configuration, a single reinforcing wall case, and a double reinforcing wall case. The coupled translational, twisting, and bending modes were employed by nine damage detection indices. Relative to all others, Flexibility Percentage Difference performed the best, indicating both damage severity and location. While some of the indices proved more effective than others, none were able to practically detect and locate change for an inspector of this structure.

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