Characterizing and Modeling Wood and Smart Cement with Additives for Real Time Moisture Detection

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dc.contributor.advisorVipulanandan, Cumaraswamy
dc.contributor.committeeMemberMo, Yi-Lung
dc.contributor.committeeMemberLim, Gino J.
dc.creatorBhatia, Shivam
dc.date.accessioned2021-08-04T02:04:33Z
dc.date.createdAugust 2020
dc.date.issued2020-08
dc.date.submittedAugust 2020
dc.date.updated2021-08-04T02:04:37Z
dc.description.abstractIn this study electrically characterizing the changes in wood (organic) and smart cement (inorganic) due to moisture changes was investigated using 2-probe method. Also, Ultrasonic Pulse Velocity was used to investigate the changes in the compressive wave speed with changes in moisture content. Smart cement was modified by adding UH-biosurfactant and characterized the changes in the initial resistivity, curing characteristics and the piezoresistive behavior. Also, smart cement was exposed to different water levels (external) and changes in the resistivity were correlated with moisture content. The experimental results were correlated with Vicat Appartus tests and were modeled using Vipulanandan models and Artificial Neural Network (ANN) models. Wood, one of the most commonly used natural materials was studied for variable moisture saturation conditions and electrical measurements were then recorded to monitor and characterize the changes. Also, Ultrasonic Pulse Velocity test, one of the most established and widely used non-destructive test (NDT) was used to correlate with the moisture changes and resistivity in the wood.
dc.description.departmentCivil and Environmental Engineering, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/7969
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectSmart Cement, Wood, Moisture, Artiifcial Neural Networks
dc.titleCharacterizing and Modeling Wood and Smart Cement with Additives for Real Time Moisture Detection
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2022-08-01
local.embargo.terms2022-08-01
thesis.degree.collegeCullen College of Engineering
thesis.degree.departmentCivil and Environmental Engineering, Department of
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Civil Engineering

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