[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126951-en":3,"doc-seo-126951-105":30,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126951,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine Learning Predictive Models for Load Identification of Railroad Bridges Subjected to Train-Induced Vibration","The U.S. rail network relies on numerous railroad bridges that are often over a century old, experiencing structural deterioration beyond their practical service life. Rising train weight and speed increase the risk to these vital assets, while traditional load identification and monitoring approaches such as Weigh-in-Motion (WIM) are seldom used due to cost and operational limitations. This thesis develops a cost-effective strategy using vibration sensors and machine learning (LSTM, RNN) with finite element modeling to estimate axle load and train speed from vibration data, achieving acceptable time-series prediction performance.","University of Texas Rio Grande Valley  \nScholarWorks @ UTRGV  \nTheses and Dissertations  \n5-2024  \nMachine Learning Predictive Models for Load Identification of Railroad Bridges Subjected to Train-Induced Vibration  \nMd Masnun Rahman  \nThe University of Texas Rio Grande Valley  \nFollow this and additional works at: [https://scholarworks.utrgv.edu/etd](https://scholarworks.utrgv.edu/etd)  \n Part of the Civil Engineering Commons  \nRecommended Citation  \nRahman, Md Masnun, \"Machine Learning Predictive Models for Load Identification of Railroad Bridges Subjected to Train-Induced Vibration\" (2024) . Theses and Dissertations. 1488.  \n[https://scholarworks.utrgv.edu/etd/1488](https://scholarworks.utrgv.edu/etd/1488)  \nThis Thesis is brought to you for free and open access by ScholarWorks @ UTRGV. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of ScholarWorks @ UTRGV. For more information, [please contact](please contact justin.white@utrgv.edu)[ justin.white@utrgv.edu](please contact justin.white@utrgv.edu), [william.flores01@utrgv.edu](william.flores01@utrgv.edu).  \nMACHINE LEARNING PREDICTIVE MODELS FOR  \nLOAD IDENTIFICATION OF RAILROAD BRIDGES SUBJECTED TO TRAIN-INDUCED VIBRATION  \nA Thesis  \nby  \nMD MASNUN RAHMAN  \nSubmitted in Partial Fulfillment of the Requirements for the Degree of  \nMASTER OF SCIENCE  \nMajor Subject: Civil Engineering  \nThe University of Texas Rio Grande Valley May 2024  \nMACHINE LEARNING PREDICTIVE MODELS FOR  \nLOAD IDENTIFICATION OF RAILROAD BRIDGES SUBJECTED TO TRAIN-INDUCED VIBRATION  \nA Thesis  \nby  \nMD MASNUN RAHMAN  \nCOMMITTEE MEMBERS  \nDr. Mohsen Amjadian Chair of Committee  \nDr. Philip Park  \nCommittee Member  \nDr. Constantine M. Tarawneh Committee Member  \nDr. Fatemeh Nazari  \nCommittee Member  \nCopyright 2024 Md Masnun Rahman All Rights Reserved  \nABSTRACT  \nRahman, Md Masnun, ML Predictive Models for Load Identification of Railroad Bridges Subjected to Train-Induced Vibration. Master of Science (MS), May 2024, 77 pp., 2 tables, 29 figures, 44 references, 6 titles.  \nThe U.S. rail network is among the world's largest, safest, and most efficient. However, many its railway bridges, which play an essential role in its connectivity, are over 100 years old, showing signs of structural deterioration exceeding their practical service life. The increased weight and speed of modern trains also pose a risk to these bridges. Traditional load identification and monitoring technologies like Weigh-in-Motion (WIM) systems are rarely used on railway bridges due to their cost and practical limits. This thesis explores a cost-effective approach to utilize vibration sensors on railway bridges to identify and predict train load features (speed and axle load), using machine learning (ML). The study involves finite element modeling of railway bridges, followed by applying ML algorithms like LSTM and RNN to estimate axle loads and speeds from vibration data. The results show the acceptable performance of developed time series ML models in predicting these parameters.  \nDEDICATION  \nThis master's thesis is dedicated to my parents and siblings, whose unwavering love and encouragement have been my driving force. My mother, Laila Parvin, my father, Md Mobassar Ali, and my siblings, Md Manjurul Alam and Md Mehesum Rahman, are the reasons I am here today. Their belief in my abilities and the sacrifices made for my education inspire me every day.  \nI dedicate this work to all individuals striving for knowledge and positive change. May this thesis contribute, even in a small way, to the collective pursuit of understanding and progress.  \nTo my family, friends, and mentors, thank you for your unwavering support and belief in me.","cbCaim5F34yIkek9","https://ap.wps.com/l/cbCaim5F34yIkek9","pdf",3047883,1,98,"English","en",105,"# Abstract\n# Background and Motivation\n## Limitations of Traditional Load Identification\n# Proposed Approach\n## Finite Element Modeling\n## Vibration-Sensor Data and ML Algorithms\n# Model Training and Evaluation\n## Time-Series Load and Speed Prediction\n# Results and Findings","[{\"question\":\"Why is load identification for railroad bridges important in this thesis?\",\"answer\":\"Railroad bridges form critical connectivity in the U.S. rail network, yet many are aged and deteriorating. Increased train weight and speed further elevate structural risk, making reliable load identification essential.\"},{\"question\":\"What alternative to Weigh-in-Motion (WIM) is proposed?\",\"answer\":\"The thesis proposes using vibration sensors on railway bridges as a more cost-effective monitoring approach. Machine learning then derives train load features such as speed and axle load from vibration data.\"},{\"question\":\"Which machine learning models are used to estimate axle load and speed?\",\"answer\":\"The study applies time-series machine learning algorithms, including LSTM and RNN, to estimate axle loads and train speeds from the measured vibration signals.\"},{\"question\":\"How are the predictive models developed and assessed?\",\"answer\":\"Finite element modeling supports the study, followed by training the ML models on vibration data. Results report acceptable performance for predicting the target parameters over time series.\"}]","Machine Learning Predictive Models for Load Identification of Railroad Bridges Subjected to Train-Induced Vibration | PDF",1785935861,247,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":91,"head_meta":93,"extra_data":95,"updated_unix":28},"machine-learning-predictive-models-for-load-identification-of-railroad-bridges-subjected-to-train-induced-vibration","",{"@graph":36,"@context":90},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-predictive-models-for-load-identification-of-railroad-bridges-subjected-to-train-induced-vibration/126951/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"Why is load identification for railroad bridges important in this thesis?","Question",{"text":76,"@type":77},"Railroad bridges form critical connectivity in the U.S. rail network, yet many are aged and deteriorating. Increased train weight and speed further elevate structural risk, making reliable load identification essential.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What alternative to Weigh-in-Motion (WIM) is proposed?",{"text":81,"@type":77},"The thesis proposes using vibration sensors on railway bridges as a more cost-effective monitoring approach. Machine learning then derives train load features such as speed and axle load from vibration data.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning models are used to estimate axle load and speed?",{"text":85,"@type":77},"The study applies time-series machine learning algorithms, including LSTM and RNN, to estimate axle loads and train speeds from the measured vibration signals.",{"name":87,"@type":74,"acceptedAnswer":88},"How are the predictive models developed and assessed?",{"text":89,"@type":77},"Finite element modeling supports the study, followed by training the ML models on vibration data. 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