All Outputs (5)

Predicting completion risk in PPP projects using big data analytics (2018)
Journal Article
Owolabi, H., Bilal, M., Oyedele, L., Alaka, H. A., Ajayi, S. O., & Akinade, O. (2020). Predicting completion risk in PPP projects using big data analytics. IEEE Transactions on Engineering Management, 67(2), 430-453. https://doi.org/10.1109/TEM.2018.2876321

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 completi... Read More about Predicting completion risk in PPP projects using big data analytics.

Public private partnerships (PPP) in the developing world: Mitigating financiers’ risks (2018)
Journal Article
Owolabi, H. A., Oyedele, L., Alaka, H., Ebohon, O. J., Ajayi, S., Akinade, O., …Olawale, O. (2019). Public private partnerships (PPP) in the developing world: Mitigating financiers’ risks. World Journal of Science, Technology and Sustainable Development, 16(3), 121-141. https://doi.org/10.1108/WJSTSD-05-2018-0043

Purpose – A major challenge for foreign lenders in financing public private partnerships (PPP) infrastructure projects in an emerging market (EM) is the bankability of country-related risks. Despite existing studies on country risks in international... Read More about Public private partnerships (PPP) in the developing world: Mitigating financiers’ risks.

A Big Data analytics approach for construction firms failure prediction models (2018)
Journal Article
Alaka, H., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M., & Ajayi, S. (2019). A Big Data analytics approach for construction firms failure prediction models. IEEE Transactions on Engineering Management, 66(4), 689-698. https://doi.org/10.1109/TEM.2018.2856376

Using 693,000 datacells from 33,000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine (SVM), multiple discriminant analysis... Read More about A Big Data analytics approach for construction firms failure prediction models.

Designing out construction waste using BIM technology: Stakeholders’ expectations for industry deployment (2018)
Journal Article
Akinade, O., Oyedele, L., Ajayi, S., Bilal, M., Alaka, H. A., Owolabi, H., & Arawomo, O. (2018). Designing out construction waste using BIM technology: Stakeholders’ expectations for industry deployment. Journal of Cleaner Production, 180, 375-385. https://doi.org/10.1016/j.jclepro.2018.01.022

© 2018 The Authors The need to use Building Information Modelling (BIM) for Construction and Demolition Waste (CDW) minimisation is well documented but most of the existing CDW management tools still lack BIM functionality. This study therefore asses... Read More about Designing out construction waste using BIM technology: Stakeholders’ expectations for industry deployment.

A framework for big data analytics approach to failure prediction of construction firms (2018)
Journal Article
Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Bilal, M., Ajayi, S. O., & Akinade, O. O. (2020). A framework for big data analytics approach to failure prediction of construction firms. Applied Computing and Informatics, 16(1/2), 207-222. https://doi.org/10.1016/j.aci.2018.04.003

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best for... Read More about A framework for big data analytics approach to failure prediction of construction firms.