Bridge Condition Assessment Using Dynamic Response Collected Through Wireless Sensor Networks
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2013
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ndltd-OhioLink-oai-etd.ohiolink.edu-ysu13917765002021-08-03T06:21:45Z Bridge Condition Assessment Using Dynamic Response Collected Through Wireless Sensor Networks Hamid, Hiwa F. Civil Engineering Engineering health monitoring of bridges bridge condition assessment software prestressed box beam bridge wireless sensor network With a large inventory of deficient and aging bridges in the United States, this research focused on developing dynamic response based health monitoring system of prestressed box beam (PSBB) bridges that will provide more realistic and cost-efficient results. The hypothesis is based on the assumption that the dynamic response is a sensitive and important indicator of the physical integrity and condition of a structure. Two wireless sensor networks (WSNs) were deployed for the collection of real-time acceleration response of a 25-year old PSBB bridge under trucks with variable loads and speeds. The acceleration response of the bridge at its newest condition was collected from the dynamic simulations of its full-scale finite element (FE) models mimicking field conditions. The FE model was validated using experimental and theoretical methods. The acceleration data in time domain were transformed into frequency domain using Fast Fourier Transform to determine peak amplitudes and their corresponding fundamental frequencies for the newest and the current condition of the bridge. The analyses and comparisons of the bridge dynamic response between the newest and the current bridge interestingly indicate a 37% reduction in its fundamental frequency over its 25 years of service life. This reduction has been correlated to the current condition rating of the bridge to develop application software for quick and efficient condition assessment of a PSBB bridge. The application software can instantly estimate overall bridge condition rating when used with the WSN deployed on a PSBB bridge under vehicular loads. The research outcome and the software is expected to provide a cost-effective solution for assessing the overall condition of a PSBB bridge, which helps to reduce maintenance costs and provide technologically improved bridge maintenance service. 2013 English text Youngstown State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ysu1391776500 http://rave.ohiolink.edu/etdc/view?acc_num=ysu1391776500 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
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language |
English |
sources |
NDLTD |
topic |
Civil Engineering Engineering health monitoring of bridges bridge condition assessment software prestressed box beam bridge wireless sensor network |
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Civil Engineering Engineering health monitoring of bridges bridge condition assessment software prestressed box beam bridge wireless sensor network Hamid, Hiwa F. Bridge Condition Assessment Using Dynamic Response Collected Through Wireless Sensor Networks |
author |
Hamid, Hiwa F. |
author_facet |
Hamid, Hiwa F. |
author_sort |
Hamid, Hiwa F. |
title |
Bridge Condition Assessment Using Dynamic Response Collected Through Wireless Sensor Networks |
title_short |
Bridge Condition Assessment Using Dynamic Response Collected Through Wireless Sensor Networks |
title_full |
Bridge Condition Assessment Using Dynamic Response Collected Through Wireless Sensor Networks |
title_fullStr |
Bridge Condition Assessment Using Dynamic Response Collected Through Wireless Sensor Networks |
title_full_unstemmed |
Bridge Condition Assessment Using Dynamic Response Collected Through Wireless Sensor Networks |
title_sort |
bridge condition assessment using dynamic response collected through wireless sensor networks |
publisher |
Youngstown State University / OhioLINK |
publishDate |
2013 |
url |
http://rave.ohiolink.edu/etdc/view?acc_num=ysu1391776500 |
work_keys_str_mv |
AT hamidhiwaf bridgeconditionassessmentusingdynamicresponsecollectedthroughwirelesssensornetworks |
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1719435695357952000 |