Keywords

Structural health monitoring, Time series analysis, Wireless sensor networks

Abstract

Sensing and analysis of a structure for the purpose of detecting, tracking, and evaluating damage and deterioration, during both regular operation and extreme events, is referred to as Structural Health Monitoring (SHM). SHM is a multi-disciplinary field, with a complete system incorporating sensing technology, hardware, signal processing, networking, data analysis, and management for interpretation and decision making. However, many of these processes and subsequent integration into a practical SHM framework are in need of development. In this study, various components of an SHM system will be investigated. A particular focus is paid to the investigation of a previously developed damage detection methodology for global condition assessment of a laboratory structure with a decking system. First, a review of some of the current SHM applications, which relate to a current UCF Structures SHM study monitoring a full-scale movable bridge, will be presented in conjunction with a summary of the critical components for that project. Studies for structural condition assessment of a 4-span bridge-type steel structure using the SHM data collected from laboratory based experiments will then be presented. For this purpose, a time series analysis method using ARX models (Auto-Regressive models with eXogeneous input) for damage detection with free response vibration data will be expanded upon using both wired and wireless acceleration data. Analysis using wireless accelerometers will implement a sensor roaming technique to maintain a dense sensor field, yet require fewer sensors. Using both data types, this ARX based time series analysis method was shown to be effective for damage detection and localization for this relatively complex laboratory structure. Finally, application of the proposed methodologies on a real-life structure will be discussed, along with conclusions and recommendations for future work

Notes

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Graduation Date

2011

Semester

Spring

Advisor

Catbas, F. Necati

Degree

Master of Science in Civil Engineering (M.S.C.E.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0003694

URL

http://purl.fcla.edu/fcla/etd/CFE0003694

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic

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