[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121307-en":3,"doc-seo-121307-105":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},121307,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine Learning Applications for Predicting Longitudinal Cracking in Continuously Reinforced Concrete Pavement","The longevity of continuously reinforced concrete pavement (CRCP) relies on accurate prediction of longitudinal cracking. This study applies machine learning algorithms using data from the long-term pavement performance (LTPP) database, comparing SVM, ensemble trees, Gaussian process regression (GPR), linear regression, regression trees, artificial neural networks, and kernel-based approaches. Random Forest supports feature relevance evaluation, identifying temperature and annual average daily truck traffic (AADTT) as key predictors. Model quality is evaluated via R-squared and RMSE, with the GPR squared exponential kernel yielding the best performance. Sensitivity analysis highlights the substantial influence of pavement age, traffic loads, and environmental variables, especially temperature and precipitation.","Construction Economics and Building  \nVol. 25, No. 1 March 2025  \n© 2025 by the author(s) . This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4 . 0) License ( [https://](https://)[ ](https://)[creativecommons.org/licenses/](creativecommons.org/licenses/)[ ](creativecommons.org/licenses/)[by/4.0/](by/4.0/)), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.  \nCitation: Alnaqbi, A. , AlKhateeb, G. G. , Zeiada, W. 2025. Machine Learning Applications for Predicting Longitudinal Cracking in Continuously Reinforced Concrete Pavement. Construction Economics and Building , 25:1, 143–170.  [https://](https://)[ ](https://)[doi.org/10.5130/AJCEB](doi.org/10.5130/AJCEB).  \nv25i1 .9143  \nISSN 2204-9029 | Published by UTS ePRESS | [https://epress](https://epress). [lib. uts.edu.au/journals/index](lib. uts.edu.au/journals/index). php/AJCEB  \n143  \nRESEARCH ARTICLE  \nMachine Learning Applications for Predicting Longitudinal Cracking in Continuously Reinforced Concrete Pavement  \nAli Alnaqbi1*, Ghazi G. Al-Khateeb1, Waleed Zeiada1,2  \n1 Department of Civil and Environmental Engineering, University of Sharjah, Sharjah P. O. Box 27272, United Arab Emirates  \n2 Department of Public Works Engineering, Mansoura  \nCorresponding author: Ali Alnaqbi , College of Engineering, University of Sharjah, UAE , [U21102866@sharjah.ac.ae](U21102866@sharjah.ac.ae)  \nDOI: [https://doi.org/10.5130/AJCEB.v25i1.9143](https://doi.org/10.5130/AJCEB.v25i1.9143)  \nArticle History: Received 15/05/2024; Revised 17/09/2024; Accepted 27/09/2024; Published 31/03/2025  \nAbstract  \nThe longevity of continuously reinforced concrete pavement (CRCP) depends on the accurate prediction of longitudinal cracking. In this work, longitudinal cracking is predicted using machine learning algorithms, and data from the long-term pavement performance (LTPP) database. Multiple models, such as support vector machines (SVM), ensemble trees, Gaussian process regression (GPR), linear regression, regression trees, artificial neural networks (ANN), and kernel approaches, are compared. The Random Forest approach is used in statistical studies and feature relevance evaluation to identify temperature and annual average daily truck traffic (AADTT) as important predictors.  \nR-squared and root mean squared error (RMSE) metrics are used to assess the models. Regression trees and ensemble approaches also perform competitively, but the GPR model with a squared exponential kernel performs better than the others, obtaining the best R-squared value (0 . 78) and the lowest RMSE (11 .84) . The study illustrates the shortcomings of traditional regression models and the benefits of sophisticated machine learning methods for identifying intricate nonlinear correlations. Sensitivity analysis demonstrates that pavement age, traffic loads, and environmental factors—specifically, temperature and precipitation—have a considerable impact on longitudinal cracking.  \nDECLARATION OF CONFLICTING INTEREST The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. FUNDING The author(s) received no financial support for the research, authorship, and/or publication of this article.  \nAlnaqbi  \nKeywords  \nLongitudinal Cracking; Continuously Reinforced Concrete Pavement; LTPP; Statistical Analysis; Machine Learning  \nIntroduction  \nContinuously reinforced concrete pavement (CRCP) is a widely used pavement type in transportation infrastructure due to its exceptional durability and longevity (Benmokrane, et al., 2020). Unlike traditional concrete pavements, CRCP employs continuous reinforcement, eliminating the need for expansion joints (Roesler, et al., 2011). This unique feature, achieved by re","cbCaisqGbAd4v6Hd","https://ap.wps.com/l/cbCaisqGbAd4v6Hd","pdf",3934668,1,28,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n# Declaration of Conflicting Interest\n# Funding","[{\"question\":\"Which machine learning models are compared for predicting longitudinal cracking in CRCP?\",\"answer\":\"The study compares SVM, ensemble trees, Gaussian process regression (GPR), linear regression, regression trees, artificial neural networks (ANN), and kernel approaches.\"},{\"question\":\"What features are identified as important predictors of longitudinal cracking?\",\"answer\":\"Random Forest feature relevance evaluation identifies temperature and annual average daily truck traffic (AADTT) as important predictors.\"},{\"question\":\"How is model performance evaluated and which model performs best?\",\"answer\":\"Performance is assessed using R-squared and RMSE. The GPR model with a squared exponential kernel achieves the best results, with the highest R-squared and the lowest RMSE.\"}]","Machine Learning Applications for Predicting Longitudinal Cracking in Continuously Reinforced Concrete Pavement | PDF",1785734997,71,{"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},"machine-learning-applications-for-predicting-longitudinal-cracking-in-continuously-reinforced-concrete-pavement","",{"@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/machine-learning-applications-for-predicting-longitudinal-cracking-in-continuously-reinforced-concrete-pavement/121307/",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},"Which machine learning models are compared for predicting longitudinal cracking in CRCP?","Question",{"text":75,"@type":76},"The study compares SVM, ensemble trees, Gaussian process regression (GPR), linear regression, regression trees, artificial neural networks (ANN), and kernel approaches.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What features are identified as important predictors of longitudinal cracking?",{"text":80,"@type":76},"Random Forest feature relevance evaluation identifies temperature and annual average daily truck traffic (AADTT) as important predictors.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated and which model performs best?",{"text":84,"@type":76},"Performance is assessed using R-squared and RMSE. 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