Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data
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 pa...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | masterThesis |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/13879 https://doi.org/10.26756/th.2022.391 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513468351119360 |
|---|---|
| author | Nasr, Cynthia |
| author_facet | Nasr, Cynthia |
| author_role | author |
| dc.creator.none.fl_str_mv | Nasr, Cynthia |
| dc.date.none.fl_str_mv | 2022-07-26T10:44:23Z 2022-07-26T10:44:23Z 2022 2022-04-27 |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10725/13879 https://doi.org/10.26756/th.2022.391 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Lebanese American University |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Pavements -- Performance -- Evaluation Pavements -- Testing -- Mathematical models Nondestructive testing Lebanese American University -- Dissertations Dissertations, Academic |
| dc.title.none.fl_str_mv | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data |
| dc.type.none.fl_str_mv | Thesis info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/masterThesis |
| description | 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 “”. 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. |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| id | LAURepo_c4eee6a4ccc16de0cf49684293b6a4df |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/13879 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Lebanese American University |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD DataNasr, CynthiaPavements -- Performance -- EvaluationPavements -- Testing -- Mathematical modelsNondestructive testingLebanese American University -- DissertationsDissertations, AcademicUnderstanding 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 “”. 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.1 online resource (xv, 169 leaves): col. ill.Includes bibliographical references (leaf 160-169)Lebanese American University2022-07-26T10:44:23Z2022-07-26T10:44:23Z20222022-04-27Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/13879https://doi.org/10.26756/th.2022.391http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.phpeninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/138792022-08-23T06:46:48Z |
| spellingShingle | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data Nasr, Cynthia Pavements -- Performance -- Evaluation Pavements -- Testing -- Mathematical models Nondestructive testing Lebanese American University -- Dissertations Dissertations, Academic |
| status_str | publishedVersion |
| title | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data |
| title_full | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data |
| title_fullStr | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data |
| title_full_unstemmed | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data |
| title_short | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data |
| title_sort | Development of Seed Variables Prediction Models for Use in Dynamic Backcalculation of FWD Data |
| topic | Pavements -- Performance -- Evaluation Pavements -- Testing -- Mathematical models Nondestructive testing Lebanese American University -- Dissertations Dissertations, Academic |
| url | http://hdl.handle.net/10725/13879 https://doi.org/10.26756/th.2022.391 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |