NON-DESTRUCTIVE TESTING FOR QUALITY ASSURANCE OF CONCRETE & PERFORMANCE PREDICTION OF BRIDGE DECKS WITH MACHINE LEARNING

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dc.contributor.advisorGoulias, Dimitrios DGen_US
dc.contributor.authorGhahri Saremi, Setareen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2023-02-01T06:34:37Z
dc.date.available2023-02-01T06:34:37Z
dc.date.issued2022en_US
dc.description.abstractNon-destructive testing (NDT) methods are particularly valuable in the quality assurance (QA) process since they do not interfere with production of concrete and reduce testing time and cost. NDTs can provide early warnings in meeting strength requirements at early ages of concrete as well as long term strength. NDTs are also valuable in providing evaluation of health of in-service infrastructures such as bridge and pavement. The results of this study can be used for potential adoption of an NDT-based QA plan. Their adoption in QA will provide the opportunity to test a larger portion of concrete during assessment without a significant increase in QA cost and testing time. To achieve that purpose, the selected NDTs should be fast, accurate, reliable and simple to run. The NDT methods explored in this study included infrared thermography, ultrasonic pulse velocity (UPV), fundamental resonance frequency, rebound hammer, ground penetrating radar (GPR), and ultrasonic pulse echo (UPE). Different sets of NDTs were selected in each experimental study undertaken in this dissertation appropriate to the research objectives and goals in each case. For strength gain monitoring, (i.e., maturity modeling during early ages of hydration), the suggested NDTs need to provide an assessment of the mechanical properties of concrete. To assess the concrete quality during production and/or construction the selected NDTs should rapidly identify potential issues concerning uniformity and/or the presence of production and placement defects. For evaluating the condition of concrete bridge decks with asphalt overlays, GPR response was used to detect layer thickness and concrete quality and to evaluate reinforcement condition. For addressing the transition from lab to field results, machine learning modeling was used to predict the structure condition. Therefore, two artificial neural network (ANN) models were proposed and assessed in this study to predict the condition of bridge decks in Maryland and Massachusetts. Thus, the objectives of this research were to identify and assess alternative NDT methods that can be used in: i) monitoring and/or estimating strength gain (i.e., maturity modeling) in concrete; ii) evaluating concrete uniformity and production quality; iii) detecting and measuring the extent of delamination in concrete slab representing small scale field conditions; iv) evaluating GPR in assessing the condition of pavement layers, concrete quality and reinforcement in bridge decks; and v) employing machine learning modeling to predict the condition of bridge decks.  en_US
dc.identifierhttps://doi.org/10.13016/m0of-ijj3
dc.identifier.urihttp://hdl.handle.net/1903/29561
dc.language.isoenen_US
dc.subject.pqcontrolledCivil engineeringen_US
dc.subject.pquncontrolledConcreteen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.subject.pquncontrolledMaturityen_US
dc.subject.pquncontrolledNon-destructive testingen_US
dc.subject.pquncontrolledSequence Classificationen_US
dc.subject.pquncontrolledultrasonic pulse velocityen_US
dc.titleNON-DESTRUCTIVE TESTING FOR QUALITY ASSURANCE OF CONCRETE & PERFORMANCE PREDICTION OF BRIDGE DECKS WITH MACHINE LEARNINGen_US
dc.typeDissertationen_US

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