[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118772-en":3,"doc-seo-118772-105":30,"detail-sidebar-cat-0-en-105":95},{"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},118772,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance","Maintaining and rehabilitating pavement on schedule is essential for preserving and improving road condition, with roughness serving as a key quality indicator. Accurate road roughness prediction supports sustainable transportation by enabling planners to design cost-effective, long-term maintenance and rehabilitation strategies. Conventional statistical approaches may underperform due to restrictive assumptions, motivating the use of explainable supervised machine learning to forecast the International Roughness Index (IRI) for asphalt concrete pavements. Using pavement age and cumulative traffic volume for 2013–2018 Sri Lankan arterial roads, five ML models outperformed regression with R2 > 0.75 (except SVM). Random Forest delivered the best results, and SHAP explanations confirmed positive variable effects, guiding trusted decision-making.","Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance  \nThis is the Published version of the following publication  \nSandamal, Kelum, Shashiprabha, Sachini, Muttil, Nitin and Rathnayake, Upaka (2023) Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance.  \nSustainability, 15 (12) . ISSN 2071-1050  \nThe publisher’s official version can be found at [https://www.mdpi.com/2071-1050/15/12/9617](https://www.mdpi.com/2071-1050/15/12/9617)[ ](https://www.mdpi.com/2071-1050/15/12/9617)Note that access to this version may require subscription.  \nDownloaded from VU Research Repository [https://vuir.vu.edu.au/47976/](https://vuir.vu.edu.au/47976/)  \n sustainability   \nArticle  \nPavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance  \nKelum Sandamal 1,*, Sachini Shashiprabha 1, Nitin Muttil 2,3 and Upaka Rathnayake 4, *  \nCitation: Sandamal, K.;  \nShashiprabha, S.; Muttil, N.; Rathnayake, U. Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance. Sustainability 2023, 15, 9617. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)su15129617  \nAcademic Editors: Ramadhansyah Putra Jaya, Khairil Azman Masri and Zaid Hazim Al-Saffar  \nReceived: 18 May 2023  \nRevised: 9 June 2023  \nAccepted: 13 June 2023  \nPublished: 15 June 2023  \nCopyright: © 2023 by the authors. 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://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka  \n2 Institute for Sustainable Industries & Liveable Cities, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia  \n3 College of Sport, Health and Engineering, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia  \n4 Department of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, F91 YW50 Sligo, Ireland  \n* Correspondence: [sandamal.k@sliit.lk](sandamal.k@sliit.lk) (K.S.); [upaka.rathnayake@atu.ie](upaka.rathnayake@atu.ie) (U.R.)  \nAbstract: Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018 . Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms' predictions were superior to those of the regression model, with a coefficient of determination (R2 ) of more than 0 .75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables ha","cbCaisNMV8HYQnHY","https://ap.wps.com/l/cbCaisNMV8HYQnHY","pdf",2407440,1,18,"English","en",105,"# 1. Introduction\n# 2. Methodology and Models\n## 2.1 Predictor variables\n## 2.2 Machine learning models vs regression\n## 2.3 SHAP-based explainability\n# 3. Results and Discussion\n## 3.1 Model comparison and R2 performance\n## 3.2 Interpretation of variable impacts\n# 4. Implications for Sustainable Pavement Management","[{\"question\":\"Why is predicting pavement roughness important for sustainable transportation?\",\"answer\":\"Accurate roughness prediction helps planners develop cost-effective, sustainable maintenance and rehabilitation strategies, improving how long pavements can perform before major reconstruction.\"},{\"question\":\"Which predictor variables were used to forecast IRI in the study?\",\"answer\":\"The study used pavement age and cumulative traffic volume as the two predictor variables to model International Roughness Index progression from 2013 to 2018.\"},{\"question\":\"How did the machine learning models perform compared with regression models?\",\"answer\":\"The ML models achieved superior predictive accuracy, with R2 greater than 0.75 in most cases (except SVM), and Random Forest performed best among the five ML models.\"},{\"question\":\"What role did SHAP analysis play in the research?\",\"answer\":\"SHAP analysis explained how each predictor influenced IRI progression, showing both variables had positive impacts and enabling more transparent trust in black-box model decisions.\"}]","Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance | PDF",1785720164,45,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"pavement-roughness-prediction-using-explainable-and-supervised-machine-learning-technique-for-long-term-performance","",{"@graph":36,"@context":89},[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/pavement-roughness-prediction-using-explainable-and-supervised-machine-learning-technique-for-long-term-performance/118772/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting pavement roughness important for sustainable transportation?","Question",{"text":75,"@type":76},"Accurate roughness prediction helps planners develop cost-effective, sustainable maintenance and rehabilitation strategies, improving how long pavements can perform before major reconstruction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which predictor variables were used to forecast IRI in the study?",{"text":80,"@type":76},"The study used pavement age and cumulative traffic volume as the two predictor variables to model International Roughness Index progression from 2013 to 2018.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the machine learning models perform compared with regression models?",{"text":84,"@type":76},"The ML models achieved superior predictive accuracy, with R2 greater than 0.75 in most cases (except SVM), and Random Forest performed best among the five ML models.",{"name":86,"@type":73,"acceptedAnswer":87},"What role did SHAP analysis play in the research?",{"text":88,"@type":76},"SHAP analysis explained how each predictor influenced IRI progression, showing both variables had positive impacts and enabling more transparent trust in black-box model decisions.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]