[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117120-en":3,"doc-seo-117120-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117120,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Road Maintenance through Machine Learning - Thesis","This thesis explores machine learning techniques for road infrastructure maintenance, proposing a framework to improve the efficiency and effectiveness of maintenance strategies. The research develops and implements a predictive road quality monitoring approach using Long Short-Term Memory (LSTM) networks to forecast future road conditions and flag areas needing maintenance before major deterioration. The methodology follows three phases: prototype-based data collection, LSTM predictive analysis, and optimization to support maintenance decisions, enabling a proactive shift, better safety, lower costs, and longer asset lifespan.","Illinois State University  \nISU ReD: Research and eData  \nTheses and Dissertations  \n3-25-2024  \nRoad Maintenance through Machine Learning  \nyashwanth sai Rachala  \nIllinois State University, [yashwanthrachala4898@gmail.com](yashwanthrachala4898@gmail.com)  \nFollow this and additional works at: [https://ir.library.illinoisstate.edu/etd](https://ir.library.illinoisstate.edu/etd)  \nRecommended Citation  \nRachala, yashwanth sai, \"Road Maintenance through Machine Learning\" (2024) . Theses and Dissertations. 1944.  \n[https://ir.library.illinoisstate.edu/etd/1944](https://ir.library.illinoisstate.edu/etd/1944)  \nThis Thesis is brought to you for free and open access by ISU ReD: Research and eData. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of ISU ReD: Research and eData. For more information, please contact [ISUReD@ilstu.edu](ISUReD@ilstu.edu).  \nROAD MAINTENANCE THROUGH MACHINE LEARNING  \nYASHWANTH SAI RACHALA  \n65 Pages  \nThis thesis explores the use of machine learning techniques for road infrastructure maintenance. We propose an innovative machine learning-based approach to improve the efficiency and effectiveness of road maintenance strategies. The focal point of this investigation is the development and implementation of a machine learning framework to enhance road quality monitoring. We use Long ShortTerm Memory (LSTM) networks to accurately predict future road conditions and identify potential areas requiring maintenance before significant deterioration occurs. This predictive approach is designed to enable a shift from reactive to proactive road maintenance, optimizing the use of limited resources and improving overall road safety. The methodology of the research is structured in three phases: the creation of a prototype system for road condition data collection, the application of LSTM networks for predictive analysis, and the utilization of optimization techniques to guide effective maintenance decisions. By focusing on predictive accuracy and the strategic allocation of maintenance efforts, the study seeks to extend the lifespan of road infrastructure, reduce maintenance costs, and enhance the driving experience. This thesis is a contribution to the field of road infrastructure maintenance by introducing a predictive maintenance model that leverages advanced machine learning techniques. It aims to transform the traditional maintenance approach, providing a scalable and efficient solution to road infrastructure management challenges, with the potential to significantly influence policy and practice in infrastructure maintenance.  \nKEYWORDS: Machine learning; Infrastructure maintenance; Proactive maintenance  \nROAD MAINTENANCE THROUGH MACHINE LEARNING  \nYASHWANTH SAI RACHALA  \nA Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of  \nMASTER OF SCIENCE  \nSchool of Information Technology  \nILLINOIS STATE UNIVERSITY  \nCopyright 2024 Yashwanth Sai Rachala  \nROAD MAINTENANCE THROUGH MACHINE LEARNING  \nYASHWANTH SAI RACHALA  \nCOMMITTEE MEMBERS:  \nAbdelmounaam Rezgui, Chair  \nAmmar Nariman  \nACKNOWLEDGMENTS  \nI am immensely grateful for the support and guidance I have received  \nthroughout the course of this research, and I would like to express my sincere thanks to all those who have contributed to the success of this thesis.  \nFirst and foremost, I extend my deepest appreciation to my committee members, Dr. Abdelmounaam Rezgui and Dr. Ammar Nariman, whose expertise and insightful feedback have been invaluable. Dr. Rezgui's rigorous approach to academic research and Dr. Nariman's astute observations have greatly enriched this work. Their encouragement and unwavering support have been instrumental in navigating the challenges ofthis academic endeavor.  \nI would also like to acknowledge the faculty and staff of the School of Information Technology for their supportive environment and for providing theresources necessary to conduct this research. The knowled","cbCaiaKqGh6Kjme8","https://ap.wps.com/l/cbCaiaKqGh6Kjme8","pdf",1838589,1,74,"English","en",105,"# Introduction\n## Cost of Road Maintenance\n## Length of Roads\n## Costs of Building and Maintenance\n## Costs to Drivers\n## Case Studies\n# Research Context\n## Current Challenges in Road Maintenance Funding\n## Research Problem\n## Proposed Solution\n## Significance of the Research\n## Contribution to Existing Knowledge\n## Methodological Framework for Predictive Road Infrastructure Maintenance\n# Literature Review\n## Papers\n# Data Collection Methodology\n## Phase 1: Design and Functionality of the Road Probe Application\n## Phase 2: Data Collection and Processing","[{\"question\":\"What is the main goal of this thesis?\",\"answer\":\"To use machine learning to improve road infrastructure maintenance by enabling predictive road quality monitoring and more proactive decision-making.\"},{\"question\":\"How does the thesis predict future road conditions?\",\"answer\":\"It uses Long Short-Term Memory (LSTM) networks to forecast upcoming road conditions and identify locations likely to deteriorate.\"},{\"question\":\"What are the three phases of the research methodology?\",\"answer\":\"The study follows three phases: building a prototype for road condition data collection, applying LSTM networks for predictive analysis, and using optimization techniques to guide maintenance decisions.\"}]","Road Maintenance through Machine Learning - 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