{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78337"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78337","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Modeling and monitoring of the dynamic response of railroad bridges using wireless smart sensors","abstract":"Railroad bridges form an integral part of railway infrastructure in the USA carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must report annual inspection of all the bridges. Until now, visual inspection has been the most prevalent practice in monitoring this infrastructure, while high-cost and unreliability can limit the efficiency and accuracy of such assessments. With recent advances in sensing technology, structural health monitoring can be a promising solution for providing a reliable and inexpensive ways for assessing the bridges. Nonetheless, because damage is a local phenomenon, to be able to detect/ monitor existing/potential damage, densely deployed sensors are required, which is inefficient and still expensive. Alternatively, model-based monitoring strategies can be adopted to identify a critical element from a numerical model that has been calibrated with measured field data. However, this approach has been widely adopted and applied for highway bridges, while railroad bridges have received comparably less attention. The main reason for the limited number of studies is due, in part, to fundamental differences between the loading being applied to highway bridges and railroad bridges. Usually, the mass of the vehicles crossing highway bridges is assumed to be relatively small compared to the mass of the bridge itself; as a result, the mass of the vehicles are often neglecting in the problem. In contrast, the mass of a train crossing a railroad bridge can be as large as the mass of the bridge itself. Moreover, trains are typically composed of an engine, followed by multiple cars resulting in a nearly deterministic moving mass/load being applied to the bridge that varies with speed. As a consequence, numerous models have been developed to understand the dynamic response of bridges under in-service train loads, but most fail to provide a simple, yet flexible, representation of the salient features of the responses of the bridge. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge under undesirable and local phenomena, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.","abstract_html":"Railroad bridges form an integral part of railway infrastructure in the USA carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must report annual inspection of all the bridges. Until now, visual inspection has been the most prevalent practice in monitoring this infrastructure, while high-cost and unreliability can limit the efficiency and accuracy of such assessments. With recent advances in sensing technology, structural health monitoring can be a promising solution for providing a reliable and inexpensive ways for assessing the bridges. Nonetheless, because damage is a local phenomenon, to be able to detect/ monitor existing/potential damage, densely deployed sensors are required, which is inefficient and still expensive. Alternatively, model-based monitoring strategies can be adopted to identify a critical element from a numerical model that has been calibrated with measured field data. However, this approach has been widely adopted and applied for highway bridges, while railroad bridges have received comparably less attention. The main reason for the limited number of studies is due, in part, to fundamental differences between the loading being applied to highway bridges and railroad bridges. Usually, the mass of the vehicles crossing highway bridges is assumed to be relatively small compared to the mass of the bridge itself; as a result, the mass of the vehicles are often neglecting in the problem. In contrast, the mass of a train crossing a railroad bridge can be as large as the mass of the bridge itself. Moreover, trains are typically composed of an engine, followed by multiple cars resulting in a nearly deterministic moving mass/load being applied to the bridge that varies with speed. As a consequence, numerous models have been developed to understand the dynamic response of bridges under in-service train loads, but most fail to provide a simple, yet flexible, representation of the salient features of the responses of the bridge. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge under undesirable and local phenomena, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.","abstract_has_math":false,"creators":["Kim, Robin Eunju"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Spencer, Billie F., Jr.","Agha, Gul A.","Fahnestock, Larry A.","Nagayama, Tomonori"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-07-22T22:16:14Z","date_published":"2015-07-22T22:16:14Z","updated_at":"2026-07-22T22:26:11Z","subjects":["Structural Health Monitoring","Wireless Smart Sensors","Railroad Bridges"],"languages":["en"],"rights":["Copyright 2015 Robin E Kim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/78337","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Spencer, Billie F., Jr.","Agha, Gul A.","Fahnestock, Larry A.","Nagayama, Tomonori"]},{"key":"dc:creator","label":"Author","values":["Kim, Robin Eunju"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-07-22T22:16:14Z","2015-05","2015-04-23","2015-5"]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Structural Health Monitoring","Wireless Smart Sensors","Railroad Bridges"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Robin E Kim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/78337"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Railroad bridges form an integral part of railway infrastructure in the USA carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must report annual inspection of all the bridges. Until now, visual inspection has been the most prevalent practice in monitoring this infrastructure, while high-cost and unreliability can limit the efficiency and accuracy of such assessments. With recent advances in sensing technology, structural health monitoring can be a promising solution for providing a reliable and inexpensive ways for assessing the bridges. Nonetheless, because damage is a local phenomenon, to be able to detect/ monitor existing/potential damage, densely deployed sensors are required, which is inefficient and still expensive. Alternatively, model-based monitoring strategies can be adopted to identify a critical element from a numerical model that has been calibrated with measured field data. However, this approach has been widely adopted and applied for highway bridges, while railroad bridges have received comparably less attention. The main reason for the limited number of studies is due, in part, to fundamental differences between the loading being applied to highway bridges and railroad bridges. Usually, the mass of the vehicles crossing highway bridges is assumed to be relatively small compared to the mass of the bridge itself; as a result, the mass of the vehicles are often neglecting in the problem. In contrast, the mass of a train crossing a railroad bridge can be as large as the mass of the bridge itself. Moreover, trains are typically composed of an engine, followed by multiple cars resulting in a nearly deterministic moving mass/load being applied to the bridge that varies with speed. As a consequence, numerous models have been developed to understand the dynamic response of bridges under in-service train loads, but most fail to provide a simple, yet flexible, representation of the salient features of the responses of the bridge. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge under undesirable and local phenomena, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-07-22 without embargo terms","The student, Robin Kim, accepted the attached license on 2015-04-23 at 09:50.","The student, Robin Kim, submitted this Dissertation for approval on 2015-04-23 at 09:51.","This Dissertation was approved for publication on 2015-04-23 at 12:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #7761 on 2015-07-22 at 10:30:46","Made available in DSpace on 2015-07-22T22:16:14Z (GMT). No. of bitstreams: 2 KIM-DISSERTATION-2015.pdf: 5745625 bytes, checksum: 073034a436368abf76c8dfcdc15d9151 (MD5) LICENSE.txt: 4206 bytes, checksum: c0f61ffefda5aec84e1206ad5738c8a6 (MD5) Previous issue date: 2015-04-23","Restricted access to closed for two years by request of the author. Change made by Ayla Stein (astein@illinois.edu) on 2015-09-29","Open"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Modeling and monitoring of the dynamic response of railroad bridges using wireless smart sensors"]}]}],"canonical_facts":{"dc:contributor":["Spencer, Billie F., Jr.","Agha, Gul A.","Fahnestock, Larry A.","Nagayama, Tomonori"],"dc:creator":["Kim, Robin Eunju"],"dc:date":["2015-07-22T22:16:14Z","2015-05","2015-04-23","2015-5"],"dc:description":["Railroad bridges form an integral part of railway infrastructure in the USA carrying approximately 40 % of the ton-miles of freight. The US Department of Transportation (DOT) forecasts current rail tonnage to increase up to 88 % by 2035. Within the railway network, a bridge occurs every 1.4 miles of track, on average, making them critical elements. In an effort to accommodate safely the need for increased load carrying capacity, the Federal Railroad Association (FRA) announced a regulation in 2010 that the bridge owners must report annual inspection of all the bridges. Until now, visual inspection has been the most prevalent practice in monitoring this infrastructure, while high-cost and unreliability can limit the efficiency and accuracy of such assessments. With recent advances in sensing technology, structural health monitoring can be a promising solution for providing a reliable and inexpensive ways for assessing the bridges. Nonetheless, because damage is a local phenomenon, to be able to detect/ monitor existing/potential damage, densely deployed sensors are required, which is inefficient and still expensive. Alternatively, model-based monitoring strategies can be adopted to identify a critical element from a numerical model that has been calibrated with measured field data. However, this approach has been widely adopted and applied for highway bridges, while railroad bridges have received comparably less attention. The main reason for the limited number of studies is due, in part, to fundamental differences between the loading being applied to highway bridges and railroad bridges. Usually, the mass of the vehicles crossing highway bridges is assumed to be relatively small compared to the mass of the bridge itself; as a result, the mass of the vehicles are often neglecting in the problem. In contrast, the mass of a train crossing a railroad bridge can be as large as the mass of the bridge itself. Moreover, trains are typically composed of an engine, followed by multiple cars resulting in a nearly deterministic moving mass/load being applied to the bridge that varies with speed. As a consequence, numerous models have been developed to understand the dynamic response of bridges under in-service train loads, but most fail to provide a simple, yet flexible, representation of the salient features of the responses of the bridge. The objective of this research is to develop appropriate modeling and monitoring techniques for railroad bridges toward understanding the dynamic responses under a moving train. To achieve the research objective, the following issues are considered specifically. For modeling, a simple, yet effective, model is developed to capture salient features of the bridge responses under a moving train. A new hybrid model is then proposed, which is a flexible and efficient tool for estimating bridge responses for arbitrary train configurations and speeds. For monitoring, measured field data is used to validate the performance of the numerical model. Further, interpretation of the proposed models showed that those models are efficient tools for predicting response of the bridge under undesirable and local phenomena, such as fatigue and resonance. Finally, fundamental software, hardware, and algorithm components are developed for providing synchronized sensing for geographically distributed networks, as can be found in railroad bridges. The results of this research successfully demonstrate the potentials of using wirelessly measured data to perform model development and calibration that will lead to better understanding the dynamic responses of railroad bridges and to provide an effective tool for prediction of bridge response for arbitrary train configurations and speeds.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-07-22 without embargo terms","The student, Robin Kim, accepted the attached license on 2015-04-23 at 09:50.","The student, Robin Kim, submitted this Dissertation for approval on 2015-04-23 at 09:51.","This Dissertation was approved for publication on 2015-04-23 at 12:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #7761 on 2015-07-22 at 10:30:46","Made available in DSpace on 2015-07-22T22:16:14Z (GMT). No. of bitstreams: 2 KIM-DISSERTATION-2015.pdf: 5745625 bytes, checksum: 073034a436368abf76c8dfcdc15d9151 (MD5) LICENSE.txt: 4206 bytes, checksum: c0f61ffefda5aec84e1206ad5738c8a6 (MD5) Previous issue date: 2015-04-23","Restricted access to closed for two years by request of the author. Change made by Ayla Stein (astein@illinois.edu) on 2015-09-29","Open"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/78337"],"dc:language":["en"],"dc:rights":["Copyright 2015 Robin E Kim"],"dc:subject":["Structural Health Monitoring","Wireless Smart Sensors","Railroad Bridges"],"dc:title":["Modeling and monitoring of the dynamic response of railroad bridges using wireless smart sensors"],"dc:type":["text"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:11Z"}