Non-intrusive bridge weigh-in-motion: integrating geophones and strain sensors for accurate vehicle characterization.

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dc.contributor.authorIshtiaq, Taimur
dc.contributor.examiningcommitteeBakht, Baidar (Civil Engineering)
dc.contributor.examiningcommitteeRegehr, Jonathan (Civil Engineering)
dc.contributor.supervisorMufti, Aftab
dc.contributor.supervisorThomson, Douglas
dc.date.accessioned2023-12-21T16:05:26Z
dc.date.available2023-12-21T16:05:26Z
dc.date.issued2023-12-20
dc.date.submitted2023-12-20T19:55:21Zen_US
dc.date.submitted2023-12-20T20:18:10Zen_US
dc.degree.disciplineCivil Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)
dc.description.abstractThis study introduces an innovative Bridge Weigh-in-Motion (BWIM) approach, utilizing a geophone, a novel sensor in the field of Structural Health Monitoring (SHM). Vehicle overloading poses a serious threat to bridge safety and service life. Loaded vehicles exert excessive stress on bridge decks, road pavements, and girders, leading to accelerated degradation of bridge structural components. Therefore, accurate information regarding real traffic loads, especially heavy vehicles, is critical for assessing bridge health. The proposed BWIM system combines geophones and strain sensors to accurately determine axle loads, axle spacing, and Gross Vehicle Weight (GVW) in regular traffic flow. The research methodology consists of bridge span instrumentation, data acquisition, processing, storage, and analysis, detailing the methods for extracting vehicle characteristics from measured bridge responses. Validation is done with field experiments on a real instrumented bridge in Winnipeg, Canada. This study focuses on loaded trucks. Velocity measurements exhibited an error range of -5% to 3.8%, with a confident 95% interval of -0.4% to 0.54% and an R2 value of 0.95, based on a sample of 64 vehicles. GVW calculations demonstrated an error range of -4.6% to +3.2%, and 95% confidence interval of -2.7% to 3.2%, derived from 6 runs of known GVWs. Axle detection accuracy was 95%, assessed across a sample of 41 trucks exceeding 150 kN in GVW. Axle spacings and loads were calculated in the error ranges of -10.52% to 7.8% and -4.97% to 10.48%, respectively. Confidence intervals for these metrics ranged from -2.4% to 3.2% and 1.05% to 8.6%, respectively. This study offers a contribution to the domain of SHM and Civionics, providing a reliable solution for axle detection of loaded trucks and assessing real traffic loads on instrumented bridges.
dc.description.noteFebruary 2024
dc.description.sponsorshipVector Scholarship for research in Civionics Engineering (JMBT)
dc.identifier.urihttp://hdl.handle.net/1993/37886
dc.language.isoeng
dc.rightsopen accessen_US
dc.subjectAxle loads
dc.subjectBridge Weight in Motion
dc.subjectNon intrusive monitoring
dc.subjectBridge instrumentation
dc.subjectGeophone sensors
dc.subjectGross Vehicle Weights
dc.titleNon-intrusive bridge weigh-in-motion: integrating geophones and strain sensors for accurate vehicle characterization.
dc.typemaster thesisen_US
local.subject.manitobano
oaire.awardNumberUMGF
oaire.awardTitleUniversity of Manitoba Graduate Fellowship
oaire.awardURIhttps://umanitoba.ca/graduate-studies/funding-awards-and-financial-aid/university-manitoba-graduate-fellowship-umgf
project.funder.identifierhttps://doi.org/10.13039/100010318
project.funder.nameUniversity of Manitoba
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