{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129519"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129519","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Integrated approach for simulation and prediction of railway track dynamic responses","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Li, Wenjing"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Tutumluer, Erol","Edwards, J. Riley","Barkan, Christopher","Mishra, Debakanta","Hajj, Ramez"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-15","date_published":"2025-04-15","updated_at":"2026-07-22T22:25:05Z","subjects":["railroad track dynamics","bridge approach","train-track-bridge model","track transitions"],"languages":["en","eng"],"rights":["Copyright 2025 Wenjing Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129519","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tutumluer, Erol","Edwards, J. Riley","Barkan, Christopher","Mishra, Debakanta","Hajj, Ramez"]},{"key":"dc:creator","label":"Author","values":["Li, Wenjing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-15","2025-05"]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["railroad track dynamics","bridge approach","train-track-bridge model","track transitions"]}]},{"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 Wenjing Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129519"]}]},{"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 2027-05-01","The student, Wenjing Li, accepted the attached license on 2025-04-09 at 19:36.","The student, Wenjing Li, submitted this Dissertation for approval on 2025-04-09 at 19:46.","This Dissertation was approved for publication on 2025-04-15 at 16:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21735 on 2025-10-19 at 19:14:34","This PhD study focused on the development of a Train-Track (TT) model and a Trian-Track-Bridge (TTB) model to address the challenging hanging tie problem of railway track transition zones with track irregularities and accurately simulate the dynamic responses of bridge approaches under moving train loads. The study included a comprehensive analysis of field data collected by Tutumluer et al. (2024), development and optimization of analytical models and algorithms, comparison of the developed model with existing models and commercial software, and the development of a track property prediction model based on the fully developed TTB model and machine learning methods. The primary objective was to develop an accurate simulation model and gain a deeper understanding of the mechanisms causing hanging tie problems near bridge abutments. Following the validation of the TTB model with real-world field data, the study explored various conditions, including different shapes of tie gaps, varying lengths of tie gap zones, and different locations of bridge approaches, such as bridge entrances and exits. The effects of train operating speed, tie spacing, and tie types were also investigated. Additionally, a secondary objective was to develop a predictive model for determining track properties, such as ballast stiffness and subgrade stiffness. This model aims to predict and monitor track substructure properties, thereby enabling more efficient scheduling of maintenance activities. An initial research task of this PhD study involved interpreting detailed field data previously collected along the US Amtrak Northeast Corridor near Chester, Pennsylvania (Tutumluer et al., 2024). The data included individual layer deformations of track substructure layers and track geometry data. Statistical analyses were performed on the field data to quantify transient responses and performance trends at the bridge approaches. The field data revealed hanging tie issues at the instrumented bridge approaches, caused by several sequential ties near the bridge abutments experiencing lack-of-support conditions. These ties, with gaps underneath, exhibited oscillatory motion due to the dynamic loading from moving train wheels. This nonuniform support worsened the substructure conditions, leading to significant deformations, including heave due to train passages. The TTB model developed for ballasted track systems in this study is applicable to both regular embankments, bridge entrance and exit locations. Simulation results from the model closely matched the transient displacements collected in various substructure layers from the field instrumentation at different periods within a two-year period. The model parameters were carefully selected based on a comprehensive literature review and property information gathered from manufacturers. The model also accounted for vertical track irregularities and examined the dynamic responses at bridge approach locations. Comparisons were made among the prediction obtained from the validated model and other existing analytical models and available commercial software. Finally, the study also developed a track property prediction model based on machine learning methods applied on the analysis results of the validated train-track-bridge model. Collecting track property information in the field is often difficult, time-consuming, and expensive, and it disrupts train services. The predictive model developed in this study, therefore aimed to alleviate these issues, providing a more efficient approach to maintenance scheduling. This study identified that track transition zones subjected to differential settlements, impact loads, and hanging tie issues exhibited an increasing trend in transient vertical deformations over time, in contrast to open track locations. These transition zones also presented larger tie gaps. The research further demonstrated that bridge entrance locations experienced more substantial dynamic responses than bridge exit locations. Additionally, consistent tie gaps were found to result in higher transient displacements on both ties and ballast at track transitions, while in open track locations, only the transient displacement on top of ties increased. The length of the gap zone with a consistent tie gap did not significantly influence transient displacements on ties and ballast, unlike tie spacing has a significant impact on the track dynamic responses. Moreover, high train operating speeds had a pronounced effect on transient displacements on ties and ballast, whereas lower speeds did not exhibit a significant impact."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Integrated approach for simulation and prediction of railway track dynamic responses"]}]}],"canonical_facts":{"dc:contributor":["Tutumluer, Erol","Edwards, J. Riley","Barkan, Christopher","Mishra, Debakanta","Hajj, Ramez"],"dc:creator":["Li, Wenjing"],"dc:date":["2025-04-15","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, Wenjing Li, accepted the attached license on 2025-04-09 at 19:36.","The student, Wenjing Li, submitted this Dissertation for approval on 2025-04-09 at 19:46.","This Dissertation was approved for publication on 2025-04-15 at 16:38.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21735 on 2025-10-19 at 19:14:34","This PhD study focused on the development of a Train-Track (TT) model and a Trian-Track-Bridge (TTB) model to address the challenging hanging tie problem of railway track transition zones with track irregularities and accurately simulate the dynamic responses of bridge approaches under moving train loads. The study included a comprehensive analysis of field data collected by Tutumluer et al. (2024), development and optimization of analytical models and algorithms, comparison of the developed model with existing models and commercial software, and the development of a track property prediction model based on the fully developed TTB model and machine learning methods. The primary objective was to develop an accurate simulation model and gain a deeper understanding of the mechanisms causing hanging tie problems near bridge abutments. Following the validation of the TTB model with real-world field data, the study explored various conditions, including different shapes of tie gaps, varying lengths of tie gap zones, and different locations of bridge approaches, such as bridge entrances and exits. The effects of train operating speed, tie spacing, and tie types were also investigated. Additionally, a secondary objective was to develop a predictive model for determining track properties, such as ballast stiffness and subgrade stiffness. This model aims to predict and monitor track substructure properties, thereby enabling more efficient scheduling of maintenance activities. An initial research task of this PhD study involved interpreting detailed field data previously collected along the US Amtrak Northeast Corridor near Chester, Pennsylvania (Tutumluer et al., 2024). The data included individual layer deformations of track substructure layers and track geometry data. Statistical analyses were performed on the field data to quantify transient responses and performance trends at the bridge approaches. The field data revealed hanging tie issues at the instrumented bridge approaches, caused by several sequential ties near the bridge abutments experiencing lack-of-support conditions. These ties, with gaps underneath, exhibited oscillatory motion due to the dynamic loading from moving train wheels. This nonuniform support worsened the substructure conditions, leading to significant deformations, including heave due to train passages. The TTB model developed for ballasted track systems in this study is applicable to both regular embankments, bridge entrance and exit locations. Simulation results from the model closely matched the transient displacements collected in various substructure layers from the field instrumentation at different periods within a two-year period. The model parameters were carefully selected based on a comprehensive literature review and property information gathered from manufacturers. The model also accounted for vertical track irregularities and examined the dynamic responses at bridge approach locations. Comparisons were made among the prediction obtained from the validated model and other existing analytical models and available commercial software. Finally, the study also developed a track property prediction model based on machine learning methods applied on the analysis results of the validated train-track-bridge model. Collecting track property information in the field is often difficult, time-consuming, and expensive, and it disrupts train services. The predictive model developed in this study, therefore aimed to alleviate these issues, providing a more efficient approach to maintenance scheduling. This study identified that track transition zones subjected to differential settlements, impact loads, and hanging tie issues exhibited an increasing trend in transient vertical deformations over time, in contrast to open track locations. These transition zones also presented larger tie gaps. The research further demonstrated that bridge entrance locations experienced more substantial dynamic responses than bridge exit locations. Additionally, consistent tie gaps were found to result in higher transient displacements on both ties and ballast at track transitions, while in open track locations, only the transient displacement on top of ties increased. The length of the gap zone with a consistent tie gap did not significantly influence transient displacements on ties and ballast, unlike tie spacing has a significant impact on the track dynamic responses. Moreover, high train operating speeds had a pronounced effect on transient displacements on ties and ballast, whereas lower speeds did not exhibit a significant impact."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129519"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Wenjing Li"],"dc:subject":["railroad track dynamics","bridge approach","train-track-bridge model","track transitions"],"dc:title":["Integrated approach for simulation and prediction of railway track dynamic responses"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}