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Predicting completion risk in PPP projects using big data analytics

Owolabi, Hakeem; Bilal, Muhammad; Oyedele, Lukumon; Alaka, Hafiz A; Ajayi, Saheed O; Akinade, Olugbenga

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Hakeem Owolabi
Associate Professor - Project Analytics and Digital Enterprise

Muhammad Bilal
Associate Professor - Big Data Application

Lukumon Oyedele
Professor in Enterprise & Project Management

Hafiz A Alaka

Saheed O Ajayi

Olugbenga Akinade
Associate Professor - AR/VR Development with Artificial Intelligence


Accurate prediction of potential delays in public private partnerships (PPP) projects could provide valuable information relevant for planning and mitigating completion risk in future PPP projects. However, existing techniques for evaluating completion risk remain incapable of identifying hidden patterns in risk behavior within large samples of projects, which are increasingly relevant for accurate prediction. To effectively tackle this problem in PPP projects, this study proposes a Big Data Analytics predictive modeling technique for completion risk prediction. With data from 4294 PPP project samples delivered across Europe between 1992 and 2015, a series of predictive models have been devised and evaluated using linear regression, regression trees, random forest, support vector machine, and deep neural network for completion risk prediction. Results and findings from this study reveal that random forest is an effective technique for predicting delays in PPP projects, with lower average test predicting error than other legacy regression techniques. Research issues relating to model selection, training, and validation are also presented in the study.

Journal Article Type Article
Acceptance Date Sep 19, 2018
Online Publication Date Nov 21, 2018
Publication Date May 1, 2020
Deposit Date Dec 7, 2018
Publicly Available Date Dec 10, 2018
Journal IEEE Transactions on Engineering Management
Print ISSN 0018-9391
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 67
Issue 2
Pages 430-453
Keywords completion risk, big data analytics, PPP projects
Public URL
Publisher URL
Additional Information Additional Information : © 2018 IEEE
Contract Date Dec 7, 2018


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