{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-2050"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-2050","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Validating the empirical distress prediction models within the AASHTOWare Pavement Mechanistic-Empirical Design, using Tennessee pavement performance data from the Long Term Pavement Performance database","abstract":"The Mechanistic-Empirical Pavement Design Guide (MEPDG) represents a recent pavement design approach, developed to address the shortcomings of the 1993 AASHTO Guide for Design of Pavement Structures. The Pavement Mechanistic-Empirical Design (PMED) software is set to operationalize the mechanistic-empirical principles outlined in MEPDG. However, empirical distress prediction models integrated into PMED were developed and calibrated using national data, necessitating validation and local calibration before implementation on a local scale. Moreover, updates to PMED present possibilities for continuous evaluation to account for improvements in the distress prediction models. This study validates PMED version 2.6.2.2’s prediction models using Tennessee pavement performance data from the Long Term Pavement Performance (LTPP) database, along with previously established local and current global calibration coefficients. Findings from statistical evaluations support the need for local calibration and recalibration of the distress prediction models, in order to suit Tennessee conditions.","abstract_html":"The Mechanistic-Empirical Pavement Design Guide (MEPDG) represents a recent pavement design approach, developed to address the shortcomings of the 1993 AASHTO Guide for Design of Pavement Structures. The Pavement Mechanistic-Empirical Design (PMED) software is set to operationalize the mechanistic-empirical principles outlined in MEPDG. However, empirical distress prediction models integrated into PMED were developed and calibrated using national data, necessitating validation and local calibration before implementation on a local scale. Moreover, updates to PMED present possibilities for continuous evaluation to account for improvements in the distress prediction models. This study validates PMED version 2.6.2.2’s prediction models using Tennessee pavement performance data from the Long Term Pavement Performance (LTPP) database, along with previously established local and current global calibration coefficients. Findings from statistical evaluations support the need for local calibration and recalibration of the distress prediction models, in order to suit Tennessee conditions.","abstract_has_math":false,"creators":["Otieno, Maxine"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Onyango, Mbakisya","Fomunung, Ignatius; Bathi, Jejal Reddy; Owino, Joseph","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:47:13Z","subjects":["Pavements, Concrete--Cracking","Pavements--Design and construction","Pavements--Performance--United States--Tennessee"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/872","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Onyango, Mbakisya","Fomunung, Ignatius; Bathi, Jejal Reddy; Owino, Joseph","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Otieno, Maxine"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-08-01T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Pavements, Concrete--Cracking","Pavements--Design and construction","Pavements--Performance--United States--Tennessee"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/872"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Civil and Chemical Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["The Mechanistic-Empirical Pavement Design Guide (MEPDG) represents a recent pavement design approach, developed to address the shortcomings of the 1993 AASHTO Guide for Design of Pavement Structures. The Pavement Mechanistic-Empirical Design (PMED) software is set to operationalize the mechanistic-empirical principles outlined in MEPDG. However, empirical distress prediction models integrated into PMED were developed and calibrated using national data, necessitating validation and local calibration before implementation on a local scale. Moreover, updates to PMED present possibilities for continuous evaluation to account for improvements in the distress prediction models. This study validates PMED version 2.6.2.2’s prediction models using Tennessee pavement performance data from the Long Term Pavement Performance (LTPP) database, along with previously established local and current global calibration coefficients. Findings from statistical evaluations support the need for local calibration and recalibration of the distress prediction models, in order to suit Tennessee conditions."]},{"key":"dc:title","label":"Title","values":["Validating the empirical distress prediction models within the AASHTOWare Pavement Mechanistic-Empirical Design, using Tennessee pavement performance data from the Long Term Pavement Performance database"]}]}],"canonical_facts":{"dc:contributor":["Onyango, Mbakisya","Fomunung, Ignatius; Bathi, Jejal Reddy; Owino, Joseph","College of Engineering and Computer Science"],"dc:creator":["Otieno, Maxine"],"dc:date":["2024-08-01T07:00:00Z"],"dc:description":["Dept. of Civil and Chemical Engineering","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["The Mechanistic-Empirical Pavement Design Guide (MEPDG) represents a recent pavement design approach, developed to address the shortcomings of the 1993 AASHTO Guide for Design of Pavement Structures. The Pavement Mechanistic-Empirical Design (PMED) software is set to operationalize the mechanistic-empirical principles outlined in MEPDG. However, empirical distress prediction models integrated into PMED were developed and calibrated using national data, necessitating validation and local calibration before implementation on a local scale. Moreover, updates to PMED present possibilities for continuous evaluation to account for improvements in the distress prediction models. This study validates PMED version 2.6.2.2’s prediction models using Tennessee pavement performance data from the Long Term Pavement Performance (LTPP) database, along with previously established local and current global calibration coefficients. Findings from statistical evaluations support the need for local calibration and recalibration of the distress prediction models, in order to suit Tennessee conditions."],"dc:identifier":["https://scholar.utc.edu/theses/872"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Pavements, Concrete--Cracking","Pavements--Design and construction","Pavements--Performance--United States--Tennessee"],"dc:title":["Validating the empirical distress prediction models within the AASHTOWare Pavement Mechanistic-Empirical Design, using Tennessee pavement performance data from the Long Term Pavement Performance database"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:13Z"}