{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115753"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115753","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Development of a model for in-situ non-dry asphalt concrete density prediction using dielectric properties","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Abufares, Lama H A"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Al-Qadi, Imad L."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:55Z","subjects":["GPR","asphalt concrete","dielectric constant","density","moisture content","EM mixing theory"],"languages":["en","eng"],"rights":["Copyright 2022 Lama Abufares"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115753","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Al-Qadi, Imad L."]},{"key":"dc:creator","label":"Author","values":["Abufares, Lama H A"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-27"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["GPR","asphalt concrete","dielectric constant","density","moisture content","EM mixing theory"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Lama Abufares"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115753"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Lama Abufares, accepted the attached license on 2022-04-26 at 09:48.","The student, Lama Abufares, submitted this Thesis for approval on 2022-04-26 at 09:54.","This Thesis was approved for publication on 2022-04-27 at 12:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17958 on 2022-11-11 at 12:58:14","Ground penetrating radar (GPR) is a nondestructive testing technique used on many civil structures, including pavements. It is applied to predict asphalt concrete (AC) layer thicknesses and dry densities. Detecting moisture in AC, which affects the performance of existing and recycled pavements, has been a challenge. Moisture detection would assist in identifying potential problematic spots, so remedial actions may be taken. Knowing moisture content in AC would improve AC density prediction accuracy by GPR. In addition, predicting cold recycling treatment moisture content could help in monitoring the curing process. This would guide decision makers to determine the proper time for opening treated roads to traffic and/or place an overlay. In this study, data were collected from both field cold recycling projects and indoor test slabs. The combined dataset was used to correlate measured moisture content to the dielectric constant of AC mixes and develop prediction models. Al-Qadi Cao Abufares (ACA) model is developed in this study based on the electromagnetic mixing theory. This model is a modification to the Al-Qadi Lahouar Leng (ALL) model; it incorporates moisture effect on the bulk dielectric constant and thus the AC density prediction. The introduced ACA model predicts non-dry AC density with an average error of 2% and predicts moisture content with a root mean square error (RMSE) of 0.5%."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Development of a model for in-situ non-dry asphalt concrete density prediction using dielectric properties"]}]}],"canonical_facts":{"dc:contributor":["Al-Qadi, Imad L."],"dc:creator":["Abufares, Lama H A"],"dc:date":["2022-05","2022-04-27"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-05-01","The student, Lama Abufares, accepted the attached license on 2022-04-26 at 09:48.","The student, Lama Abufares, submitted this Thesis for approval on 2022-04-26 at 09:54.","This Thesis was approved for publication on 2022-04-27 at 12:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17958 on 2022-11-11 at 12:58:14","Ground penetrating radar (GPR) is a nondestructive testing technique used on many civil structures, including pavements. It is applied to predict asphalt concrete (AC) layer thicknesses and dry densities. Detecting moisture in AC, which affects the performance of existing and recycled pavements, has been a challenge. Moisture detection would assist in identifying potential problematic spots, so remedial actions may be taken. Knowing moisture content in AC would improve AC density prediction accuracy by GPR. In addition, predicting cold recycling treatment moisture content could help in monitoring the curing process. This would guide decision makers to determine the proper time for opening treated roads to traffic and/or place an overlay. In this study, data were collected from both field cold recycling projects and indoor test slabs. The combined dataset was used to correlate measured moisture content to the dielectric constant of AC mixes and develop prediction models. Al-Qadi Cao Abufares (ACA) model is developed in this study based on the electromagnetic mixing theory. This model is a modification to the Al-Qadi Lahouar Leng (ALL) model; it incorporates moisture effect on the bulk dielectric constant and thus the AC density prediction. The introduced ACA model predicts non-dry AC density with an average error of 2% and predicts moisture content with a root mean square error (RMSE) of 0.5%."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115753"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Lama Abufares"],"dc:subject":["GPR","asphalt concrete","dielectric constant","density","moisture content","EM mixing theory"],"dc:title":["Development of a model for in-situ non-dry asphalt concrete density prediction using dielectric properties"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}