[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127620-en":3,"doc-seo-127620-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127620,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",6,"Technology","Machine learning technique for damage detection of rails on steel railroad bridges subjected to moving train load","Rail transmits wheel load and guides trains, yet rolling contact fatigue and wear can lead to broken-rail damage and, on bridges, severe consequences including structural failure and costly repairs. The study develops a classification-based machine learning approach to detect broken-rail damage in an open-deck steel railroad bridge using acceleration responses measured under moving train loads at different speeds. A 2D finite element model is built in OpenSEESPy, acceleration changes are analyzed with Hilbert-Huang Transform energy and phase indices, and classifiers including SVM, KNN, and decision trees are trained and tested, yielding acceptable identification precision.","University of Texas Rio Grande Valley  \nScholarWorks @ UTRGV  \n\n| Civil Engineering Faculty Publications and Presentations | College of Engineering and Computer Science |\n| --- | --- |\n\n4-18-2023  \nMachine learning technique for damage detection of rails on steel railroad bridges subjected to moving train load  \nMd Masnun Rahman  \nMohsen Amjadian Mahesh Pokhrel Constantine Tarawneh  \nFollow this and additional works at: [https://scholarworks.utrgv.edu/ce_fac](https://scholarworks.utrgv.edu/ce_fac)  \n Part of the Civil Engineering Commons  \nMachine learning technique for damage detection of rails on steel railroad bridges subjected to moving train load  \nMd Masnun Rahmana, Mohsen Amjadian *a, Mahesh Pokhrela, Constantine Tarawnehba Department of Civil Engineering, The University of Texas Rio Grande Valley, 1201 W University  \nDr, Edinburg, TX 78539.  \nb Department of Mechanical Engineering, The University of Texas Rio Grande Valley, 1201 W  \nUniversity Dr, Edinburg, TX 78539.  \nABSTRACT  \nRail is one of the key elements of the railway system, and its role is to transmit the wheel load to the track bed and guide the train cars along the track. Rail is susceptible to rolling contact fatigue and wear due to being repeatedly subjected to the moving load of the train. This can eventually result in broken-rail damage and train derailment, which if happens on a railroad bridge, it can severely damage the bridge, such as the structural failure of the Tempe Town Lake steel railroad bridge in July 2020 that costed $11 million to repair. Therefore, early detection of defects in rail-bridge system may prevent a critical accident with irreversible damage. The objective of this paper is to use classification-based machine learning techniques to detect broken-rail damage in an open-deck railroad bridge by measuring its acceleration response under the moving load of the train for different speeds. For this purpose, the two-dimensional Finite Element (2D FE) model of a given railroad bridge is created using OpenSEESPy package, which is a Python-3 interpreter of OpenSEES. The changes in the acceleration response due to the damaged rail compared to the undamaged (healthy) rail are characterized by using the Hilbert-Huang Transform in both the time and frequency domains and quantified by defining energy and phase damage indices. The data collected from the 2D FE model are used to train and test several machine learning (ML) classifiers including the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT) algorithms. The results from the data-analytic study show an acceptable level of precision of these classifiers in identifying the damage to the rail-bridge system.  \nKeywords: Defected rail, Steel railroad bridge, Damage detection, Finite-element modelling, Time-frequency domain, and Machine learning.  \n1. INTRODUCTION  \nThe US railway system is one of the largest in the world, with over 140,000 miles of track [1] . It is used to transport a wide range of goods, including coal, oil, and agricultural products, and passengers on long-distance and commuter trains. Despite the growth of other forms of transportation, such as highways and air travel, railways remain an important part of the US transportation network due to their conveniences, safety, and efficiency. Railroad bridges are essential parts of the railway network, providing a crucial link between different zones of the network. These bridges are designed to carry heavy loads oftrain cars, passengers, and cargo across rivers, valleys, and other bodies of water. However, many of these bridges were built in the early 1900s and are now reaching the end of their useful lifespan [2] . In addition, the increase in heavy freight traffic has put additional strain on these ageing structures. Open-deck railroad bridges are more susceptible to structural damage due to the moving load of train as the wheel impact load is much higher in these bridges compared to the bridges with b","cbCaidXw07ykLVD4","https://ap.wps.com/l/cbCaidXw07ykLVD4","pdf",1610927,2,1,13,"English","en",105,"# Abstract\n# Introduction\n# Related Work and Background\n# Methodology\n## Finite Element Modeling (OpenSEESPy)\n## Time-Frequency Damage Indices (Hilbert-Huang Transform)\n# Machine Learning Classifiers\n## SVM, KNN, and Decision Tree\n# Results and Discussion\n# Conclusion","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It addresses early detection of broken-rail damage on steel railroad bridges to prevent accidents and bridge structural failure.\"},{\"question\":\"How is the bridge model created for the study?\",\"answer\":\"A two-dimensional finite element model of the railroad bridge is created using the OpenSEESPy package.\"},{\"question\":\"Which machine learning methods are used for rail damage identification?\",\"answer\":\"The study trains and tests classification models including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT) algorithms.\"}]","Machine learning technique for damage detection of rails on steel railroad bridges subjected to moving train load | 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