[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118929-en":3,"doc-seo-118929-105":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},118929,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Prediction of Distresses in Pavement Networks - A Machine Learning Approach","Pavement network quality is strongly influenced by multiple distress modes, including cracking, potholes, rutting, and other deformations, making accurate distress modeling essential for effective pavement management. This research develops machine learning models to predict key distress-related indicators: the International Roughness Index (IRI), fatigue cracking, and longitudinal cracking. Data are extracted from the Long-Term Pavement Performance (LTPP) database, targeting environmental conditions comparable to Egypt. The dataset includes 8537 datapoints across 221 pavement sections. Six algorithms are evaluated using MAE and R2, and XGBoost achieves the best overall results for all outputs.","Graduate Studies  \nPrediction of Distresses in Pavement Networks: A Machine  \nLearning Approach  \nA THESIS SUBMITTED BY  \nMahmoud Mostafa Kotb  \nTO THE  \nConstruction Engineering Department  \nSUPERVISED BY  \nDr. Maram Saudy  \nJanuary 3rd, 2024  \nin partial fulfillment of the requirements for the degree of  \nMaster of Science in Construction Management  \nDeclaration of Authorship  \nI, Mahmoud Kotb, declare that this thesis titled, “Prediction of Distresses in Pavement Networks: A Machine Learning Approach” and the work presented in it are my own. I confirm that:  \n• This work was done wholly or mainly while in candidature for a research degree at this University.  \n• Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated.  \n• Where I have consulted the published work of others, this is always clearly attributed.  \n• Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.  \n• I have acknowledged all main sources of help.  \n• Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself.  \nSigned:  \nDate:  \nAbstract  \nThe quality of pavement networks is greatly affected by different distresses. These distresses appear in many forms, such as cracking, potholes, rutting and different types of deformation. As a result, to ensure effective pavement management, accurate modeling of these different distresses has become essential. Moreover, machine learning models have shown great potential in modeling pavement performance in recent years. The objective of this research is to develop machine learning models for modeling key parameters of pavement distress, specifically the International Roughness Index (IRI), fatigue and longitudinal cracking. Data for this investigation were extracted from the Long-Term Pavement Performance (LTPP) database, with a focus on areas exhibiting environmental conditions similar to those in Egypt. By doing so, the models would be applicable to Egyptian settings. The dataset comprised of 8537 datapoints on 221 different pavement sections. The variables collected include IRI, temperature, precipitation, Equivalent Single Axle Loads (ESALs), pavement age, time since last maintenance, asphalt concrete layer thickness, average asphalt content, bulk specific gravity, granular base thickness, percentage of fatigue cracking, and percentage of longitudinal cracking.  \nSix machine learning algorithms were used for modeling each output variable: XGBoost, Random Forest, K-Nearest Neighbors (KNN), Bayesian Regression, Ridge Regression, and Decision Trees. Model performance was assessed using Mean Absolute Error (MAE) and R2 as evaluation metrics. Comparative analysis revealed that the XGBoost algorithm demonstrated superior performance in modeling all three output variables. The results showed a MAE of 0.17 and R2 of 0.729 for modeling IRI. For modeling fatigue cracking and longitudinal cracking, the model produced a MAE of 4.92% and 2.96%, respectively, with an R2 of 0.672 and 0.692 respectively.  \nThe findings are significant for many reasons. Firstly, they offer a framework for modeling pavement distress parameters, which is crucial for effective pavement management and maintenance strategies. Secondly, the study confirms the efficacy of machine learning algorithms in modeling pavement performance indicators, especially when using ensemble models. Lastly, the exceptional performance of the XGBoost algorithm indicates its reliability as a tool for both future research and practical applications in pavement management. Importantly, the models are tailored to be applicable in Egypt, providing a data-driven approach to improve the quality of road infrastructure in the region.  \nAcknowledgements  \nI express my profound grat","cbCaik9NIxGRtE8P","https://ap.wps.com/l/cbCaik9NIxGRtE8P","pdf",2978450,1,107,"English","en",105,"# Contents\n## Chapter 1","[{\"question\":\"What distress parameters does the research aim to predict?\",\"answer\":\"The study models three pavement performance indicators: IRI, fatigue cracking, and longitudinal cracking.\"},{\"question\":\"Which dataset is used to build and evaluate the models?\",\"answer\":\"The models are trained using data extracted from the Long-Term Pavement Performance (LTPP) database, focusing on conditions similar to Egypt.\"},{\"question\":\"Why is XGBoost highlighted as the best-performing model?\",\"answer\":\"Comparative results show XGBoost achieves superior predictive performance across all three output variables, with the lowest reported errors and strong R2 values.\"}]","Prediction of Distresses in Pavement Networks - A Machine Learning Approach | PDF",1785721000,270,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"prediction-of-distresses-in-pavement-networks-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@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/prediction-of-distresses-in-pavement-networks-a-machine-learning-approach/118929/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What distress parameters does the research aim to predict?","Question",{"text":75,"@type":76},"The study models three pavement performance indicators: IRI, fatigue cracking, and longitudinal cracking.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset is used to build and evaluate the models?",{"text":80,"@type":76},"The models are trained using data extracted from the Long-Term Pavement Performance (LTPP) database, focusing on conditions similar to Egypt.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is XGBoost highlighted as the best-performing model?",{"text":84,"@type":76},"Comparative results show XGBoost achieves superior predictive performance across all three output variables, with the lowest reported errors and strong R2 values.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]