[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128424-en":3,"doc-seo-128424-105":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},128424,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Assessment of Airport Pavement Condition Index (PCI) Using Machine Learning","Pavement condition assessment underpins airport pavement management systems (APMS) by enabling safe, efficient operations. Traditional inspection-driven and computation-intensive approaches often consume significant time and resources, motivating Industry 4.0 adoption of machine learning. This study evaluates five algorithms—Linear Regression, Decision Tree, Random Forest, Artificial Neural Network, and Support Vector Machine—predicting numerical PCI (0–100) and categorical PCI (3 and 7 classes) from distress data. Class imbalance is handled via SMOTE and RUS, and 10-fold cross-validation reports strong agreement (Kappa 0.88–0.93), low error (\u003C7.17%), and ROC AUC >0.93. Results show Random Forest performs best overall for numerical and three-class PCI, while accurate seven-class prediction requires oversampling.","Article  \nAssessment of Airport Pavement Condition Index (PCI) Using Machine Learning  \nBertha Santos 1,2, *, André Studart 1,2 and Pedro Almeida 1,2  \nAcademic Editor: Mario DiNardo  \nReceived: 13 September 2025  \nRevised: 11 October 2025  \nAccepted: 17 October 2025  \nPublished: 24 October 2025  \nCitation: Santos, B.; Studart, A.; Almeida, P. Assessment of Airport Pavement Condition Index (PCI) Using Machine Learning. Appl. Syst. Innov. 2025, 8, 162. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/asi8060162](10.3390/asi8060162)  \nCopyright: © 2025 by the authors. Published by MDPI on behalf of the International Institute of Knowledge Innovation and Invention. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons](https://creativecommons). org/licenses/by/4.0/) .  \n1 Department of Civil Engineering and Architecture, University of Beira Interior, 6200-358 Covilhã, Portugal; [andre.studart@ubi.pt](andre.studart@ubi.pt) (A.S.); [galmeida@ubi.pt](galmeida@ubi.pt) (P.A.)  \n2 GeoBioTec, University of Beira Interior, 6200-358 Covilhã, Portugal  \n* [Correspondence: bsantos@ubi.pt](Correspondence: bsantos@ubi.pt)  \nAbstract  \nPavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI) . Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes) . To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93 . The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices.  \nKeywords: airport pavement management system (APMS); pavement condition index (PCI); machine learning (ML); predictive modeling  \n1. Introduction  \n1.1. Framework  \nAirports play a vital role in society, connecting people around the world. The evergrowing world population, estimated to reach 9.7 billion by 2050 [1], requires infrastructures such as airports to withstand intense traffic and heavily loaded aircraft while ensuring the safety of users and operations. As a result, maintenance programs are in high demand to  \nensure that airports remain operational and functional. However, budget shortfalls, nonpreventive maintenance pro","cbCaicX1iLJuHnNm","https://ap.wps.com/l/cbCaicX1iLJuHnNm","pdf",1088390,2,1,23,"English","en",105,"# Introduction\n## Framework\n# Abstract","[{\"question\":\"What problem does the study address in airport pavement management?\",\"answer\":\"It targets the inefficiency of conventional PCI assessment, which depends on extensive on-site inspections and complex calculations, making it time- and resource-intensive.\"},{\"question\":\"Which machine learning algorithms are compared for PCI prediction?\",\"answer\":\"The study compares Linear Regression, Decision Tree, Random Forest, Artificial Neural Network, and Support Vector Machine.\"},{\"question\":\"How does the study handle class imbalance for categorical PCI prediction?\",\"answer\":\"It applies random oversampling using SMOTE and undersampling using RUS to improve learning for imbalanced PCI condition classes.\"}]","Assessment of Airport Pavement Condition Index (PCI) Using Machine Learning | PDF",1785947481,58,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"assessment-of-airport-pavement-condition-index-pci-using-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/assessment-of-airport-pavement-condition-index-pci-using-machine-learning/128424/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address in airport pavement management?","Question",{"text":76,"@type":77},"It targets the inefficiency of conventional PCI assessment, which depends on extensive on-site inspections and complex calculations, making it time- and resource-intensive.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are compared for PCI prediction?",{"text":81,"@type":77},"The study compares Linear Regression, Decision Tree, Random Forest, Artificial Neural Network, and Support Vector Machine.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the study handle class imbalance for categorical PCI prediction?",{"text":85,"@type":77},"It applies random oversampling using SMOTE and undersampling using RUS to improve learning for imbalanced PCI condition classes.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]