[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124383-en":3,"doc-seo-124383-105":30,"detail-sidebar-cat-0-en-105":90},{"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},124383,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","A comparative analysis of machine learning models for predicting faulting in jointed plain concrete pavements - Research report","Faulting in jointed plain concrete pavements reflects elevation variations at transverse joints driven by environmental conditions, subgrade characteristics, and repeated traffic loading. The study addresses the limits of conventional regression approaches by applying machine learning models trained on LTPP data from dry climate zones. Input variables include precipitation, temperature, freeze-thaw cycles, and structural properties such as pavement thickness, age, tensile strength, and optimum moisture content. Five methods are compared, with ANN achieving the highest accuracy (R²=0.81) and sensitivity analysis used to evaluate variable influence.","A comparative analysis of machine learning models for predicting faulting in jointed plain concrete pavements  \nT. Ahmed, M. Isied, M.I. Souliman  \nThe University of Texas at Tyler, Tyler, Texas, The United States  \nABSTRACT: Faulting is defined by variations in elevation at transverse joints in Jointed Plain Concrete Pavements resulting from environmental factors, subgrade properties, and traffic loads. It is a major distress for rigid pavements, possessing crucial challenges for maintaining road safety standards. Traditional regression methods often fail to address the complexities of faulting, while machine learning approach utilizes data driven learning to enhance prediction accuracy. Datasets for this study were sourced from the LTPP database, focusing on dry climate zones. Key environmental factors affecting wheel path faulting include Yearly Precipitation, Temperature, Freeze-Thaw Cycles, along with structural properties such as Pavement Thickness, Pavement Age, Tensile Strength, and Optimum Moisture Content are utilized as model input. Five machine learning methodologies, including Support Vector Machine, Decision Tree, Linear Discriminant Analysis, Ensemble and Artificial Neural Network were implemented. Among these, ANN demonstrated highest prediction accuracy, attaining an R² of 0.81. The ANN model was further evaluated to assess the influence of the input variables on the model output through sensitivity analysis.  \n1 LITERATURE REVIEW  \nFaulting is a major issue in jointed concrete pavements (JCPs) . Many prediction models are being developed for predicting fault failure. In the AASHTO 1993 version of the pavement design guide, faulting and cracking were accounted for by maintaining and serviceability above a defined threshold. In the 1990s, Simpson et al. attempted to separate these two concerns and forecast faults independently based on pavement design, traffic, weather conditions (Simpson et al., 1994) . In recent years various studies suggested that faulting in rigid pavements, particularly in Jointed Plain Concrete Pavement (JPCP), is influenced by a multitude of factors that span structural, environmental, and design considerations (Hossain, Gopisetti and Miah, 2019; Ehsani, Moghadas Nejadand Hajikarimi, 2023; Ahmed, Isied and Souliman, 2024) . Traffic loads and the cumulative effect of axle load distributions are significant contributors, as they induce stress and deformation in the pavement layers, particularly affecting the base layer's plastic deformation (Chen, Saha and Lytton, 2020) . Most of the prediction models used the LTPP database, which included faulting measurements at doweled and nondoweled joints and some measurements at transverse crack locations. Ehsani et al. used both artificial neural and random forest methods with 19 input variables to develop a prediction model (Ehsani, Moghadas  \nNejad and Hajikarimi, 2023) . Ker et al. developed a prediction model for transverse joint faulting incorporating the ERESBACK 2.2 program for back calculation to get more accurate data (Ker, Lee and Lin, 2008) . The mechanistic-empirical erosion-based faulting model incorporated traffic parameters with the application of erosion test showed the correlation between traffic and environmental factors with faulting (Jung and Zollinger, 2011) . The current faulting model integrated into the Pavement ME design procedure considers pavement response, climatic conditions, traffic, and base erodibility. This model is uniformly applied to all types of JCPs, regardless of their structural makeup (such as conventional concrete pavement, unbonded concrete overlay, bonded concrete overlay, etc.) . This suggests that the pumping mechanism is assumed to be consistent across all pavement structures. Furthermore, it assumes uniformity in the rate of faulting development and the maximum faulting regardless of pavement structure. The focus of this study is to develop a machine learning-based approach for predicting faulting JCPs","cbCaim6aApevZatb","https://ap.wps.com/l/cbCaim6aApevZatb","pdf",464653,1,4,"English","en",105,"# Literature review\n# Objectives\n# Data collection, selection, and processing","[{\"question\":\"What causes faulting in jointed plain concrete pavements according to the study?\",\"answer\":\"Faulting results from elevation variations at transverse joints shaped by environmental factors, subgrade properties, and traffic loads.\"},{\"question\":\"Which dataset and climate region are used to train the prediction models?\",\"answer\":\"Training data come from the LTPP database, focused on dry climate zones in the United States.\"},{\"question\":\"How does the ANN model perform compared with other machine learning methods?\",\"answer\":\"Among five tested methods, the artificial neural network achieves the highest prediction accuracy, reaching an R² of 0.81.\"}]","A comparative analysis of machine learning models for predicting faulting in jointed plain concrete pavements - Research report | PDF",1785821909,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"a-comparative-analysis-of-machine-learning-models-for-predicting-faulting-in-jointed-plain-concrete-pavements-research-report","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/a-comparative-analysis-of-machine-learning-models-for-predicting-faulting-in-jointed-plain-concrete-pavements-research-report/124383/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What causes faulting in jointed plain concrete pavements according to the study?","Question",{"text":74,"@type":75},"Faulting results from elevation variations at transverse joints shaped by environmental factors, subgrade properties, and traffic loads.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which dataset and climate region are used to train the prediction models?",{"text":79,"@type":75},"Training data come from the LTPP database, focused on dry climate zones in the United States.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the ANN model perform compared with other machine learning methods?",{"text":83,"@type":75},"Among five tested methods, the artificial neural network achieves the highest prediction accuracy, reaching an R² of 0.81.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]