{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/130065"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/130065","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Development and application of the Illinois Buckle Risk Model (IBRM) using multi-source track condition data","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","abstract_has_math":false,"creators":["Thakur, Neeraj"],"institution":"University of Illinois 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":2025,"date_issued":"2025-07-24","date_published":"2025-07-24","updated_at":"2026-07-22T22:25:06Z","subjects":["Track Buckles","Track Lateral Strength","Sun Kink","Buckle Risk","Track Maintenance Prioritization","Machine Vision Application"],"languages":["en","eng"],"rights":["Copyright 2025 Neeraj Thakur"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/130065","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":["Thakur, Neeraj"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-07-24","2025-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Track Buckles","Track Lateral Strength","Sun Kink","Buckle Risk","Track Maintenance Prioritization","Machine Vision Application"]}]},{"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 2025 Neeraj Thakur"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/130065"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","The student, Neeraj Thakur, accepted the attached license on 2025-07-23 at 15:19.","The student, Neeraj Thakur, submitted this Thesis for approval on 2025-07-23 at 15:38.","This Thesis was approved for publication on 2025-07-24 at 16:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22711 on 2025-10-21 at 10:06:19","The widespread use of Continuously Welded Rail (CWR) has provided many benefits to the rail industry by reducing the stress state of track infrastructure. One drawback of CWR is its higher propensity to buckle compared to jointed track due to the lack of locations to accommodate axial thermal expansion. Data from the Federal Railroad Administration (FRA) accident database reveal that buckled-track derailments have been a persistent safety concern for U.S. railroads. The FRA initiated an extensive research program in the 1980s to develop and experimentally verify a dynamic buckling theory which culminated in the development of the CWR-SAFE software. The Buckle module of CWR-SAFE accepts quantitative track condition input parameters and assesses the buckling risk of track in terms of its Buckling Safety Margin (BSM). Since the development of CWR-SAFE, there have been notable advancements in track inspection technologies capable of providing high-resolution track health data. The Illinois Buckle Risk Model (IBRM) leverages the outputs from three-dimensional machine vision and track geometry measurements systems into the CWR-SAFE environment to perform buckle risk assessment at an individual crosstie resolution. IBRM uses results from field and laboratory experiments to calibrate inspection system outputs into quantified inputs for CWR-SAFE. The application of IBRM is demonstrated using data collected from a Class I railroad subdivision. IBRM 2.0 extends this framework by aggregating crosstie-level outputs over track segments that correspond to typical track buckle lengths. Critical track strength parameters such as lateral strength, longitudinal stiffness, and torsional resistance are averaged or normalized across these segments to produce a more representative assessment of overall buckle behavior. The use of IBRM 2.0 is also demonstrated using Class I subdivision data and these results are compared to earlier results from IBRM. Two notable features of IBRM are its flexibility and scalability. Localized track conditions can be simulated by adjusting track strength and condition parameters. This adaptability supports the creation of a positive feedback loop, enabling iterative model refinement and improved predictive performance. Furthermore, IBRM can process subdivision-level data within a nominal computational timeframe, making it scalable for network-wide applications. IBRM can efficiently handle large volumes of data from track inspection systems, facilitating comprehensive and proactive track strength assessment across large rail networks."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Development and application of the Illinois Buckle Risk Model (IBRM) using multi-source track condition data"]}]}],"canonical_facts":{"dc:contributor":["Edwards, John Riley"],"dc:creator":["Thakur, Neeraj"],"dc:date":["2025-07-24","2025-08"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01","The student, Neeraj Thakur, accepted the attached license on 2025-07-23 at 15:19.","The student, Neeraj Thakur, submitted this Thesis for approval on 2025-07-23 at 15:38.","This Thesis was approved for publication on 2025-07-24 at 16:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22711 on 2025-10-21 at 10:06:19","The widespread use of Continuously Welded Rail (CWR) has provided many benefits to the rail industry by reducing the stress state of track infrastructure. One drawback of CWR is its higher propensity to buckle compared to jointed track due to the lack of locations to accommodate axial thermal expansion. Data from the Federal Railroad Administration (FRA) accident database reveal that buckled-track derailments have been a persistent safety concern for U.S. railroads. The FRA initiated an extensive research program in the 1980s to develop and experimentally verify a dynamic buckling theory which culminated in the development of the CWR-SAFE software. The Buckle module of CWR-SAFE accepts quantitative track condition input parameters and assesses the buckling risk of track in terms of its Buckling Safety Margin (BSM). Since the development of CWR-SAFE, there have been notable advancements in track inspection technologies capable of providing high-resolution track health data. The Illinois Buckle Risk Model (IBRM) leverages the outputs from three-dimensional machine vision and track geometry measurements systems into the CWR-SAFE environment to perform buckle risk assessment at an individual crosstie resolution. IBRM uses results from field and laboratory experiments to calibrate inspection system outputs into quantified inputs for CWR-SAFE. The application of IBRM is demonstrated using data collected from a Class I railroad subdivision. IBRM 2.0 extends this framework by aggregating crosstie-level outputs over track segments that correspond to typical track buckle lengths. Critical track strength parameters such as lateral strength, longitudinal stiffness, and torsional resistance are averaged or normalized across these segments to produce a more representative assessment of overall buckle behavior. The use of IBRM 2.0 is also demonstrated using Class I subdivision data and these results are compared to earlier results from IBRM. Two notable features of IBRM are its flexibility and scalability. Localized track conditions can be simulated by adjusting track strength and condition parameters. This adaptability supports the creation of a positive feedback loop, enabling iterative model refinement and improved predictive performance. Furthermore, IBRM can process subdivision-level data within a nominal computational timeframe, making it scalable for network-wide applications. IBRM can efficiently handle large volumes of data from track inspection systems, facilitating comprehensive and proactive track strength assessment across large rail networks."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/130065"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Neeraj Thakur"],"dc:subject":["Track Buckles","Track Lateral Strength","Sun Kink","Buckle Risk","Track Maintenance Prioritization","Machine Vision Application"],"dc:title":["Development and application of the Illinois Buckle Risk Model (IBRM) using multi-source track condition data"],"dc:type":["text"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:06Z"}