[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126873-en":3,"doc-seo-126873-105":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},126873,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis","Construction Engineering and Management (CEM) is a broad, interconnected research domain that drives knowledge sharing across construction-related subdisciplines. Prior scientometric studies evaluate present impact, but future citation trajectories are needed to anticipate high-impact trends and steer new work. A study is conducted using machine learning and social network analysis to predict CEM-related citation metrics from a dataset of 93,868 publications. Random Forest and XGBoost classification are trained and validated, with XGBoost selected after higher balanced accuracy, and SNA identifies which subdisciplines are more likely to yield highly cited papers.","Missouri University of Science and Technology  \nScholars' Mine  \n\n| Civil, Architectural and Environmental\u003Cbr>Engineering Faculty Research & Creative Works | Civil, Architectural and Environmental Engineering |\n| --- | --- |\n| 01 Jun 2024\u003Cbr>Forecasting Future Research Trends in the Construction Engineering and Management Domain using Machine Learning and Social Network Analysis\u003Cbr>Gasser G. Ali\u003Cbr>Islam H. El-adaway\u003Cbr>Missouri University of Science and Technology, [eladaway@mst.edu](eladaway@mst.edu)[ ](eladaway@mst.edu)Muaz O. Ahmed\u003Cbr>Radwa Eissa\u003Cbr>[et. al. For a complete list of authors](et. al. For a complete list of authors), see [https://](https://)scholarsmine. mst. edu/civarc_ enveng_facwork/3274\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/civarc_enveng_facwork](https://scholarsmine.mst.edu/civarc_enveng_facwork)\u003Cbr> Part of the Construction Engineering and Management Commons |  |\n\nRecommended Citation  \nG. G. Ali et al., \"Forecasting Future Research Trends in the Construction Engineering and Management Domain using Machine Learning and Social Network Analysis,\" Modelling, vol. 5, no. 2, pp. 438-457, MDPI, Jun 2024.  \nThe definitive version is available at [https://doi.org/10.3390/modelling5020024](https://doi.org/10.3390/modelling5020024)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article-Journal is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Civil, Architectural and Environmental Engineering Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nArticle  \nForecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis  \nGasser G. Ali 1, *, Islam H. El-adaway 2, Muaz O. Ahmed 2, Radwa Eissa 2, Mohamad Abdul Nabi 2, Tamima Elbashbishy 2 and Ramy Khalef 2  \nCitation: Ali, G.G.; El-adaway, I.H.; Ahmed, M.O.; Eissa, R.; Nabi, M.A.; Elbashbishy, T.; Khalef, R. Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis. Modelling 2024, 5, 438–457 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)modelling5020024  \nAcademic Editor: Miquel SànchezMarrè FiEMSs  \nReceived: 1 March 2024  \nRevised: 24 March 2024  \nAccepted: 3 April 2024  \nPublished: 6 April 2024  \nCopyright: © 2024 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, The University of Texas Rio Grande Valley, Edinburg, TX 78539, USA  \n2 Department of Civil, Architectural, and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA; [eladaway@mst.edu](eladaway@mst.edu) (I.H.E.-a.); [muaz.ahmed@mst.edu](muaz.ahmed@mst.edu) (M.O.A.); [reissa@mst.edu](reissa@mst.edu) (R.E.); [mah59@mst.edu](mah59@mst.edu) (M.A.N.); [telbashbishy@mst.edu](telbashbishy@mst.edu) (T.E.); [ramykhalef@mst.edu](ramykhalef@mst.edu) (R.K.)  \n* Correspondence: [gasser.ali@utrgv.edu](gasser.ali@utrgv.edu)  \nAbstract: Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, thereis a need to predict future citations of CEM publications to identify the expected ","cbCaih4JHrtDLR5a","https://ap.wps.com/l/cbCaih4JHrtDLR5a","pdf",2654586,1,21,"English","en",105,"# Introduction\n## Construction Engineering and Management (CEM) domain scope\n# Abstract\n## Research gap and objective\n## Data, methods, and evaluation\n## Machine learning results and subdiscipline insights\n# Keywords\n## Core terms","[{\"question\":\"What research gap does the study address in CEM?\",\"answer\":\"It addresses the need to predict future citations of CEM publications to identify expected high-impact trends and guide new research efforts.\"},{\"question\":\"What data and models are used to forecast citation metrics?\",\"answer\":\"The study uses a dataset of 93,868 publications and trains classification models including Random Forest and XGBoost to predict CEM-related citation metrics.\"},{\"question\":\"How does social network analysis contribute to the findings?\",\"answer\":\"SNA reveals which CEM subdisciplines are most associated with predicted impactful papers, including “Project planning and design,” “Organizational issues,” and “Information technologies, robotics, and automation.”\"},{\"question\":\"Which model is selected and what performance is reported?\",\"answer\":\"XGBoost is selected after validation; its balanced accuracy on validation is reported at about 79.5% and further testing shows a balanced accuracy of about 80.71%.\"}]","Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis | PDF",1785935333,53,{"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},"forecasting-future-research-trends-in-the-construction-engineering-and-management-domain-using-machine-learning-and-social-network-analysis","",{"@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/forecasting-future-research-trends-in-the-construction-engineering-and-management-domain-using-machine-learning-and-social-network-analysis/126873/",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","2026-08-05",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},"What research gap does the study address in CEM?","Question",{"text":75,"@type":76},"It addresses the need to predict future citations of CEM publications to identify expected high-impact trends and guide new research efforts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and models are used to forecast citation metrics?",{"text":80,"@type":76},"The study uses a dataset of 93,868 publications and trains classification models including Random Forest and XGBoost to predict CEM-related citation metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"How does social network analysis contribute to the findings?",{"text":84,"@type":76},"SNA reveals which CEM subdisciplines are most associated with predicted impactful papers, including “Project planning and design,” “Organizational issues,” and “Information technologies, robotics, and automation.”",{"name":86,"@type":73,"acceptedAnswer":87},"Which model is selected and what performance is reported?",{"text":88,"@type":76},"XGBoost is selected after validation; 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