Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data

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dc.contributor.author Nasr, Cynthia
dc.date.accessioned 2022-07-26T10:44:23Z
dc.date.available 2022-07-26T10:44:23Z
dc.date.copyright 2022 en_US
dc.date.issued 2022-04-27
dc.identifier.uri http://hdl.handle.net/10725/13879
dc.description.abstract Understanding the material properties of a pavement structure is crucial for evaluating the pavement’s performance and assessing its damage level. Generally, the backcalculation process is extensively used to analyze the Falling Weight Deflectometer (FWD)-data for estimating the layer-moduli of a pavement structure. It is mainly an iterative process that starts with a set of seed (initial) variables, calculates the theoretical pavement surface deflections, and compares them to the measured deflections. Yet, this process is most likely unstable and is prone to numerous errors including the selection of relevant seed variables. The selected seed-variables hold significant consequences on the-final backcalculated-results. This research project aims-to-develop models through classification analysis to predict the seed variables. This involves (1) calculating theoretical surface deflections through a finite element model that simulates different pavement structures and properties, (2) calculating FWD parameters and indices for each structure and (3) using those parameters to build Random Forest models that predict the seed variables with low OOB-error and high accuracy. The dynamic approach is adopted to perform the analysis on 3-layered rigid and flexible pavements. The AC layer is modeled as an LVE/material while the PCC and the unbound layers are modeled as linear/elastic materials with damping. The OOB-Estimate of error rate and the overall accuracy values obtained dictate that the predictor variables selected to build the RF models are efficiently trained and generate accurate predictions for all seed variables except for the Rayleigh Damping Parameter of the PCC layer “𝛼𝑅𝑃𝐶𝐶”. The developed models can be considered as an effective guidance for pavement engineers to select the seed variables that are closer to the actual values to initiate the backcalculation process. en_US
dc.language.iso en en_US
dc.subject Pavements -- Performance -- Evaluation en_US
dc.subject Pavements -- Testing -- Mathematical models en_US
dc.subject Nondestructive testing en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data en_US
dc.type Thesis en_US
dc.term.submitted Spring en_US
dc.author.degree MS in Civil And Environmental Engineering en_US
dc.author.school SOE en_US
dc.author.idnumber 201502137 en_US
dc.author.commembers Khoury, John
dc.author.commembers Srour, Jordan
dc.author.department Civil Engineering en_US
dc.description.physdesc 1 online resource (xv, 169 leaves): col. ill. en_US
dc.author.advisor Chatila, Jean
dc.keywords Seed Variables en_US
dc.keywords Backcalculation en_US
dc.keywords Random Forests en_US
dc.keywords FWD Data en_US
dc.keywords Finite Element Methods en_US
dc.keywords Classification Analysis en_US
dc.description.bibliographiccitations Includes bibliographical references (leaf 160-169) en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.391
dc.author.email cynthia.nasr02@lau.edu en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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