{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127517"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127517","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Railway track condition change detection: A data-driven approach to track geometry and component degradation modeling","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","abstract_has_math":false,"creators":["Venancio Da Silva Ramos, Jose Augusto"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Edwards, John Riley"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-11","date_published":"2024-12-11","updated_at":"2026-07-22T22:25:04Z","subjects":["Railroad","Track Degradation","Track Geometry","Track Components","Big Data","Ballast."],"languages":["eng","en"],"rights":["Copyright 2024 Jose Augusto Venancio da Silva Ramos"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127517","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Edwards, John Riley"]},{"key":"dc:creator","label":"Author","values":["Venancio Da Silva Ramos, Jose Augusto"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-11","2024-12"]},{"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":["Railroad","Track Degradation","Track Geometry","Track Components","Big Data","Ballast."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng","en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Jose Augusto Venancio da Silva Ramos"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127517"]}]},{"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 2026-12-01","The student, Jose Augusto Venancio Da Silva Ramos, accepted the attached license on 2024-12-10 at 17:35.","The student, Jose Augusto Venancio Da Silva Ramos, submitted this Thesis for approval on 2024-12-10 at 17:53.","This Thesis was approved for publication on 2024-12-11 at 13:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21566 on 2025-03-28 at 14:57:08","The railroad track system is a critical transportation asset that is responsible for transferring wheel loads from rolling stock to the roadbed. To ensure safe and efficient operations, U.S. Class I railroads conduct frequent inspections, generating a substantial amount of track health data. With the growing adoption of data science tools, these datasets offer new opportunities for more sophisticated maintenance and safety analyses. While many railroads leverage data trending for geometry prediction, they often overlook the impact of evolving component condition and unrecorded maintenance, which are crucial for accurate degradation modeling. This thesis evaluates the relationship between track geometry degradation and ballast profiles across curved and tangent track segments using data from a primary corridor on a U.S. Class I railroad. A stochastic approach revealed a significant correlation between degradation, initial profile conditions, and the Ballast Health Index (BHI), with faster deterioration observed in areas with poor initial geometry and high BHI values. Additionally, cross-correlation was employed to address the challenge of identifying unrecorded maintenance activities, proving effective in detecting track changes and improving data quality metrics for linear degradation models. These findings provide a quantifiable method for assessing degradation under varying conditions, enhancing maintenance prioritization and demonstrating that cross-correlation can identify maintenance events and support track monitoring and maintenance planning."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Railway track condition change detection: A data-driven approach to track geometry and component degradation modeling"]}]}],"canonical_facts":{"dc:contributor":["Edwards, John Riley"],"dc:creator":["Venancio Da Silva Ramos, Jose Augusto"],"dc:date":["2024-12-11","2024-12"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01","The student, Jose Augusto Venancio Da Silva Ramos, accepted the attached license on 2024-12-10 at 17:35.","The student, Jose Augusto Venancio Da Silva Ramos, submitted this Thesis for approval on 2024-12-10 at 17:53.","This Thesis was approved for publication on 2024-12-11 at 13:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21566 on 2025-03-28 at 14:57:08","The railroad track system is a critical transportation asset that is responsible for transferring wheel loads from rolling stock to the roadbed. To ensure safe and efficient operations, U.S. Class I railroads conduct frequent inspections, generating a substantial amount of track health data. With the growing adoption of data science tools, these datasets offer new opportunities for more sophisticated maintenance and safety analyses. While many railroads leverage data trending for geometry prediction, they often overlook the impact of evolving component condition and unrecorded maintenance, which are crucial for accurate degradation modeling. This thesis evaluates the relationship between track geometry degradation and ballast profiles across curved and tangent track segments using data from a primary corridor on a U.S. Class I railroad. A stochastic approach revealed a significant correlation between degradation, initial profile conditions, and the Ballast Health Index (BHI), with faster deterioration observed in areas with poor initial geometry and high BHI values. Additionally, cross-correlation was employed to address the challenge of identifying unrecorded maintenance activities, proving effective in detecting track changes and improving data quality metrics for linear degradation models. These findings provide a quantifiable method for assessing degradation under varying conditions, enhancing maintenance prioritization and demonstrating that cross-correlation can identify maintenance events and support track monitoring and maintenance planning."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127517"],"dc:language":["eng","en"],"dc:rights":["Copyright 2024 Jose Augusto Venancio da Silva Ramos"],"dc:subject":["Railroad","Track Degradation","Track Geometry","Track Components","Big Data","Ballast."],"dc:title":["Railway track condition change detection: A data-driven approach to track geometry and component degradation modeling"],"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":"2026-07-22T22:25:04Z"}