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Long-Term Monitoring and Evaluation of the Varina-Enon Bridge

dc.contributor.authorDahiya, Ankujen
dc.contributor.committeechairRoberts-Wollmann, Carin L.en
dc.contributor.committeememberKoutromanos, Ioannisen
dc.contributor.committeememberSarlo, Rodrigoen
dc.contributor.departmentCivil and Environmental Engineeringen
dc.date.accessioned2021-03-31T08:00:42Zen
dc.date.available2021-03-31T08:00:42Zen
dc.date.issued2021-03-30en
dc.description.abstractTo make sound decisions about the remaining life of a structure, the precise calculation of the prestress losses is very important. In post-tensioned structures, the prestress losses due to creep and shrinkage can cause serviceability issues and can reduce flexural capacity. The Varina-Enon Bridge is a cable-stayed, precast, segmental, post-tensioned box girder bridge located in Richmond, Virginia. Observation of flexural cracks in the bridge by inspectors promoted a study regarding long-term prestress losses in the structure. For understanding and sustaining the structure throughout its remaining service life, accurately quantifying prestress losses is important. Two approaches are used to predict long-term prestress losses on the Varina-Enon Bridge. The first approach involves a finite element computer model of the bridge which run a timedependent staged-construction analysis to obtain predicted prestress losses using the CEB-FIP '90 code expressions for creep and shrinkage. The second approach involves the compilation of data from instrumentation mounted in the bridge to back calculate the effective prestress force. The analysis using the computer model predicted the prestress losses as 44.6 ksi in Span 5, 47.9 ksi in Span 6, 45.3 ksi in Span 9, and 45.9 ksi in Span 11. The prestress losses estimated from field data were 50.0 ksi in Span 5, 48.0 ksi in Span 6, 46.7 ksi in Span 9, and 49.1 ksi in Span 11. It can be seen that relative to the results of field data estimations, the finite element analyses underestimated prestress loss, but given the degree of uncertainty in each form of estimation, the results are considered to fit well.en
dc.description.abstractgeneralIn order to apply a precompression force to concrete structures, post-tensioned concrete employs stressed steel strands. To construct lighter, stiffer structures, this popular building technology can be used. The steel strands undergo a reduction in force known as prestress losses over time. To make good decisions about the remaining life of a structure, the precise calculation of the prestress losses is very important. The Varina-Enon Bridge is a post-tensioned concrete box-girder bridge in Richmond Virginia. In July of 2012, observation of flexural cracks in the bridge by the inspectors promoted a study regarding long-term prestress losses in the structure. Two techniques are used to predict long-term prestress losses for this bridge. A computer model of the bridge is used in the first method to calculate losses using the design code. In order to measure prestress losses, the second technique used data from sensors mounted on the bridge. It was found that the estimation of losses closely matched those predicted at the time of the bridge construction and the computer model results. Based on this the final conclusion is made that the prestress loss in the Varina-Enon Bridge is not significantly more than expected.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:29419en
dc.identifier.urihttp://hdl.handle.net/10919/102891en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectPost-tensioned concreteen
dc.subjectPrestress lossen
dc.subjectVarina-Enon Bridgeen
dc.subjectFinite element modelingen
dc.subjectCreepen
dc.subjectShrinkageen
dc.subjectThermal gradienten
dc.titleLong-Term Monitoring and Evaluation of the Varina-Enon Bridgeen
dc.typeThesisen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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