[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122504-en":3,"doc-seo-122504-105":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},122504,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Advancing resilience in infrastructure projects through machine learning-driven models","Traditional risk management supports infrastructure operations but often falls short when facing complexity and uncertainty, prompting greater focus on resilience—especially the readiness dimension—to enable earlier threat detection and prevention. While Machine Learning (ML) can strengthen resilience modelling, empirically validated ML methods, particularly neural-network approaches, remain limited. This study builds a neural-network resilience model optimized for training efficiency and predictive accuracy. Using feature-importance techniques, it improves interpretability and identifies key determinants, with “Operational resilience” emerging as the strongest driver of project success. ","Production Planning & Control  \nThe Management of Operations  \nISSN: 0953-7287 (Print) 1366-5871 (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/tppc20)[www.tandfonline.com/journals/tppc20](homepage: www.tandfonline.com/journals/tppc20)  \nAdvancing resilience in infrastructure projects through machine learning-driven models  \nUdech ukwu Ojia ko, Tse Chiu Wong, Craig John Smith, Maxwell Chipul u, M. K.  \nS. Al-Mhdawi, Babajide Oyewo & Lawrence O bo koh  \nTo cite this article: Udech ukwu Ojia ko, Tse Chiu Wong, Craig John Smith, Maxwell Chipul u, M. K. S. Al-Mhdawi, Babajide Oyewo & Lawrence O bo koh (05 Nov 2025): Advancing resilience in infrastructure projects through machine learning-driven models, Production Planning & Control, DOI: 10. 1080/09537287 .2025.2583300  \nTo link to this article: [https://doi.org/10.1080/09537287.2025.2583300](https://doi.org/10.1080/09537287.2025.2583300)  \n© 2025 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n\n|  Published online: 05 Nov 2025. |  |\n| --- | --- |\n|  | Submit your article to this journal  |\n|  | Article views: 782 |\n|  | View related articles  |\n|  View Crossmark data |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tppc20](https://www.tandfonline.com/action/journalInformation?journalCode=tppc20)  \nPRODUCTION PLANNING & CONTROL  \n[https://doi.org/10.1080/09537287.2025.2583300](https://doi.org/10.1080/09537287.2025.2583300)  \nAdvancing resilience in infrastructure projects through machine learning-driven models  \nUdechukwu Ojiakoa, b,c, Tse Chiu Wongb, Craig John Smithb, Maxwell Chipulud , M. K. S. Al-Mhdawie,f, Babajide Oyewog, and Lawrence Obokohc  \naBailey College of Engineering & Technology, Indiana State University, Terre Haute, USA; bDepartment of Design, Manufacturing & Engineering Management, University of Strathclyde, Glasgow, UK; cJohannesburg Business School, University of Johannesburg, Johannesburg, South Africa; dThe Business School, Edinburgh Napier University, Edinburgh, UK; eSchool of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, UK; fDepartment of Civil, Structural and Environmental Engineering, Trinity College Dublin, Dublin, Ireland; gEssex Business School, University of Essex, Colchester, UK  \nABSTRACT  \nTraditional risk management enhances infrastructure utility but remains limited in addressing complexity and uncertainty. This has shifted attention towards resilience, particularly the readiness dimension, to improve early threat detection and prevention. Machine Learning (ML) offers opportunities to advance resilience modelling, yet empirically validated ML-enabled approaches, especially those using neural networks, are scarce, restricting accuracy, reliability, and applicability. This study develops a neural network-enabled resilience model optimized for training efficiency and predictive performance. By incorporating established feature importance techniques, the model improves accuracy, interpretability, and the identification of influential factors. The findings extend resilience typologies by ranking factor importance in critical infrastructure, highlighting ‘Operational resilience’ as the most significant determinant of project success. Practically, the model provides managers with clearer insights for decision-making, supporting earlier threat recognition and stronger disruption detection. The framework is adaptable across resilience contexts with appropriate industry-or platform-specific modifications.  \nARTICLE HISTORY  \nReceived 20 April 2025 Accepted 27 October 2025  \nKEYWORDS  \nMachine learning; resilience; readiness; infrastructure; project success; modelling  \n1. Introduction  \nIn recent years, global demand has surged for complex, multi-stakeholder, and technologically advanced projects that deliver not only individual critical infrastructure assets and platforms (Roehr","cbCaibcf4kpWKYje","https://ap.wps.com/l/cbCaibcf4kpWKYje","pdf",4151215,1,26,"English","en",105,"# Introduction\n## Critical infrastructure and interdependence\n## Risks, disruptions, and project success\n# Abstract","[{\"question\":\"What gap does the study target in existing resilience modelling?\",\"answer\":\"Empirically validated ML-enabled resilience approaches are scarce, especially neural network methods, which limits accuracy, reliability, and practical applicability.\"},{\"question\":\"How does the proposed neural network model improve results?\",\"answer\":\"It is optimized for training efficiency and predictive performance, and feature importance techniques enhance accuracy and interpretability.\"},{\"question\":\"Which resilience factor is identified as most 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