{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124660"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124660","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Physics-informed neural network for damage identification in railroad bridges","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","abstract_has_math":false,"creators":["Veluthedath Shajihan, Shaik Althaf"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Chowdhary, Girish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:02Z","subjects":["Physics-informed Neural Network","Damage Identification","Structural Health Monitoring","Railroad Bridges"],"languages":["en","eng"],"rights":["Copyright 2024 Shaik Althaf Veluthedath Shajihan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124660","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish"]},{"key":"dc:creator","label":"Author","values":["Veluthedath Shajihan, Shaik Althaf"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-25"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Physics-informed Neural Network","Damage Identification","Structural Health Monitoring","Railroad Bridges"]}]},{"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 2024 Shaik Althaf Veluthedath Shajihan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124660"]}]},{"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 2026-05-01","The student, Shaik Althaf Veluthedath Shajihan, accepted the attached license on 2024-04-15 at 12:19.","The student, Shaik Althaf Veluthedath Shajihan, submitted this Thesis for approval on 2024-04-15 at 12:55.","This Thesis was approved for publication on 2024-04-25 at 14:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20410 on 2024-09-16 at 00:49:21","Railroad bridges are a crucial component of the U.S. freight rail system which accounts for moving more than 40 percent of freight in the country, playing a critical role in the U.S. economy. The aging infrastructure coupled with the increasing train traffic poses a safety hazard and risks disruption of services. While identifying damages and performing holistic assessments of railroad bridges remain challenging tasks. This research proposes a physics-informed neural network (PINN) based approach for damage identification and model updating of truss railroad bridges. The proposed framework adopts an unsupervised learning method, leveraging train wheel loads and measured responses at bridge nodes as inputs. The PINN model explicitly incorporates the governing differential equations of system dynamics using a recurrent neural network (RNN) based architecture with a custom Runge-Kutta 4th order (RK-4) integrator cell. This approach enables the identification of damage ratios and localization of damaged members in the bridge. To validate the performance of the proposed approach, a case study is conducted on the Calumet bridge in Chicago, utilizing a simplified 2D model with simulated damage scenarios. The results demonstrate the model’s ability to accurately identify and quantify damage under various conditions while maintaining low false-positive rates. Furthermore, the proposed updating pipeline is designed to seamlessly incorporate prior knowledge gathered from site inspections and drone surveys, enabling a context-aware updating and assessment of bridge’s condition."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Physics-informed neural network for damage identification in railroad bridges"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish"],"dc:creator":["Veluthedath Shajihan, Shaik Althaf"],"dc:date":["2024-05","2024-04-25"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-05-01","The student, Shaik Althaf Veluthedath Shajihan, accepted the attached license on 2024-04-15 at 12:19.","The student, Shaik Althaf Veluthedath Shajihan, submitted this Thesis for approval on 2024-04-15 at 12:55.","This Thesis was approved for publication on 2024-04-25 at 14:21.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20410 on 2024-09-16 at 00:49:21","Railroad bridges are a crucial component of the U.S. freight rail system which accounts for moving more than 40 percent of freight in the country, playing a critical role in the U.S. economy. The aging infrastructure coupled with the increasing train traffic poses a safety hazard and risks disruption of services. While identifying damages and performing holistic assessments of railroad bridges remain challenging tasks. This research proposes a physics-informed neural network (PINN) based approach for damage identification and model updating of truss railroad bridges. The proposed framework adopts an unsupervised learning method, leveraging train wheel loads and measured responses at bridge nodes as inputs. The PINN model explicitly incorporates the governing differential equations of system dynamics using a recurrent neural network (RNN) based architecture with a custom Runge-Kutta 4th order (RK-4) integrator cell. This approach enables the identification of damage ratios and localization of damaged members in the bridge. To validate the performance of the proposed approach, a case study is conducted on the Calumet bridge in Chicago, utilizing a simplified 2D model with simulated damage scenarios. The results demonstrate the model’s ability to accurately identify and quantify damage under various conditions while maintaining low false-positive rates. Furthermore, the proposed updating pipeline is designed to seamlessly incorporate prior knowledge gathered from site inspections and drone surveys, enabling a context-aware updating and assessment of bridge’s condition."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124660"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Shaik Althaf Veluthedath Shajihan"],"dc:subject":["Physics-informed Neural Network","Damage Identification","Structural Health Monitoring","Railroad Bridges"],"dc:title":["Physics-informed neural network for damage identification in railroad bridges"],"dc:type":["text"],"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":"2026-07-22T22:25:02Z"}