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Conventional approaches based on manual analysis and expert judgment are slow, inconsistent, and subjective. This study presents a data-driven NLP and machine learning framework that automates challenge detection and classification from project descriptions using the Pacificon dataset. A TfidfVectorizer + Logistic Regression pipeline reaches 74.6% mean cross-validation accuracy, improving consistency and enabling earlier interventions.","Manuscript 1793  \nProactive Risk Identification in New Zealand Infrastructure Projects: A Machine Learning Approach to Classifying Project Risk  \nDr Tirth Patel  \nAssoc. Prof. Eric Scheepbouwer Dr. Jacobus Daniel van der Walt  \nFollow this and additional works at: [https://docs.lib.purdue.edu/cib-conferences](https://docs.lib.purdue.edu/cib-conferences)  \nThis document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. [Please contact epubs@purdue.edu](Please contact epubs@purdue.edu) for additional information.  \nProactive Risk Identification in New Zealand Infrastructure Projects: A Machine Learning Approach to Classifying Project Risk  \nDr. Tirth Patel, [tirth.patel@canterbury.ac.nz](tirth.patel@canterbury.ac.nz)  \nPostdoctoral Fellow, Department of Civil and Natural Resources Engineering, University of Canterbury, New Zealand  \nDr. Eric Scheepbouwer,[eric.scheepbouwer@canterbury.ac.nz](eric.scheepbouwer@canterbury.ac.nz)  \nAssociate Professor, Department of Civil and Natural Resources Engineering, University of Canterbury, New Zealand  \nDr. Jacobus Daniel van der Walt, [daniel.vanderwalt@canterbury.ac.nz](daniel.vanderwalt@canterbury.ac.nz)  \nSenior Lecturer, Department of Civil and Natural Resources Engineering, University of Canterbury, New Zealand  \nAbstract  \nIn the construction industry, proactive identification and management of challenges such as delays, budget constraints, and regulatory hurdles are essential for effective risk management and successful project delivery. Traditional methods, relying on manual analysis and expert judgment, are often timeconsuming, inconsistent, and subjective. This study proposes a data-driven framework to automate the identification and classification of challenges within project descriptions using Natural Language Processing (NLP) and machine learning (ML) techniques. Utilizing the Pacificon dataset, comprising 254,923 infrastructure projects across New Zealand, the study defines challenge categories through regular expressions and implements a machine learning pipeline with TfidfVectorizer and Logistic Regression. The framework achieves a cross-validation mean accuracy of 74.6%, demonstrating robustness and reliability. Experimental results highlight strong performance in categories like regulatory hurdles and budget constraints while identifying areas for improvement, such as environmental factors. By automating challenge identification, the framework reduces reliance on manual risk assessments, enhances consistency, and enables early intervention for improved project management outcomes. This research contributes to the digital transformation of construction risk management, offering scalable, data-driven solutions aligned with industry demands for efficiency and precision in managing project complexities.  \nKeywords  \nMachine learning, Natural Language Processing, Risk identification.  \n1 Introduction  \nInfrastructure projects are critical to economic stability, urban development, and sustainable growth. However, these projects often encounter various challenges, such as budget overruns, regulatory hurdles, resource shortages, and technical setbacks that can disrupt timelines, inflate costs, and compromise quality (Taylan et al. 2014, Patel et al. 2020) . In New Zealand, as in other regions, infrastructure project delivery requires a robust understanding of these recurring risks to achieve efficient and resilient project outcomes. Consequently, the proactive identification and mitigation of  \nClassification: In-Confidence  \nsuch challenges have become critical to enhancing project planning, optimizing resources, capacity planning and promoting stakeholder confidence in infrastructure investments (Lu et al. 2013) .  \nTraditionally, insights into project challenges have been derived retrospectively through manual postproject evaluations (Zou et al. 2017) . While informative, these approaches are inherently reactive, offering limited fore","cbCain9gQ2nSJV7j","https://ap.wps.com/l/cbCain9gQ2nSJV7j","pdf",888637,1,9,"English","en",105,"# Introduction\n## Problem background and limitations of manual approaches\n## Objective and research gap\n# Abstract\n## Proposed data-driven NLP/ML framework\n## Dataset and modeling approach\n# Methodology\n## Challenge categorization with regular expressions\n## ML pipeline with TfidfVectorizer and Logistic Regression\n# Results and discussion\n## Cross-validation performance\n## Key category strengths and improvement areas\n# Conclusion\n## Benefits for proactive risk management","[{\"question\":\"What problem does the study address in New Zealand infrastructure projects?\",\"answer\":\"It targets the need for proactive identification and classification of recurring project challenges such as delays, budget constraints, and regulatory hurdles, which are difficult to manage using slow and subjective manual methods.\"},{\"question\":\"How does the proposed framework identify and classify risks from project descriptions?\",\"answer\":\"It uses Natural Language Processing and machine learning to process historical project descriptions, defining challenge categories with regular expressions and training a pipeline based on TfidfVectorizer and Logistic Regression.\"},{\"question\":\"What performance did the framework achieve, and what categories showed strong results?\",\"answer\":\"The model achieved a mean cross-validation accuracy of 74.6%. Experimental findings indicate strong performance for categories including regulatory hurdles and budget constraints, while environmental factors were highlighted as an area for improvement.\"}]","Proactive Risk Identification in New Zealand Infrastructure Projects - A Machine Learning Approach to Classifying Project Risk | PDF",1785821583,23,{"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},"proactive-risk-identification-in-new-zealand-infrastructure-projects-a-machine-learning-approach-to-classifying-project-risk","",{"@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/proactive-risk-identification-in-new-zealand-infrastructure-projects-a-machine-learning-approach-to-classifying-project-risk/124318/",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","2026-08-04",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},"What problem does the study address in New Zealand infrastructure projects?","Question",{"text":75,"@type":76},"It targets the need for proactive identification and classification of recurring project challenges such as delays, budget constraints, and regulatory hurdles, which are difficult to manage using slow and subjective manual methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework identify and classify risks from project descriptions?",{"text":80,"@type":76},"It uses Natural Language Processing and machine learning to process historical project descriptions, defining challenge categories with regular expressions and training a pipeline based on TfidfVectorizer and Logistic Regression.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance did the framework achieve, and what categories showed strong results?",{"text":84,"@type":76},"The model achieved a mean cross-validation accuracy of 74.6%. 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