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Big data analytics system for costing power transmission projects

Delgado, Juan Manuel Davila; Oyedele, Lukumon; Bilal, Muhammad; Ajayi, Anuoluwapo; Akanbi, Lukman; Akinade, Olugbenga

Authors

Manuel Davila Delgado Manuel.Daviladelgado@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence

Lukumon Oyedele L.Oyedele@uwe.ac.uk
Professor in Enterprise & Project Management

Muhammad Bilal Muhammad.Bilal@uwe.ac.uk
Associate Professor - Big Data Application

Anuoluwapo Ajayi Anuoluwapo.Ajayi@uwe.ac.uk
Associate Professor - Big Data Application

Dr Lukman Akanbi Lukman.Akanbi@uwe.ac.uk
Associate Professor - Big Data Application Developer

Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence



Abstract

© 2019 American Society of Civil Engineers. Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a big data architecture, to manage the large and diverse data required for predictive analytics. This paper presents a predictive analytics and modeling system (PAMS) that facilitates the use of different data-driven cost prediction methods. A 2.75-million-point dataset of power transmission projects has been used as a case study. The proposed big data architecture fits this purpose. It can handle the diverse datasets used in the construction sector. The three most prevalent cost estimation models were implemented (linear regression, support vector regression, and artificial neural networks). All models performed better than the estimated human-level performance. The primary contribution of this study to the body of knowledge is an empirical indication that data-driven methods analysed in this study are on average 13.5% better than manual methods for cost estimation of power transmission projects. Additionally, the paper presents a big data architecture that can manage and process large varied datasets and seamless scalability.

Citation

Delgado, J. M. D., Oyedele, L., Bilal, M., Ajayi, A., Akanbi, L., & Akinade, O. (2020). Big data analytics system for costing power transmission projects. Journal of Construction Engineering and Management, 146(1), https://doi.org/10.1061/%28ASCE%29CO.1943-7862.0001745

Journal Article Type Article
Acceptance Date May 30, 2019
Online Publication Date Nov 15, 2019
Publication Date Jan 1, 2020
Deposit Date May 31, 2019
Publicly Available Date Dec 16, 2019
Journal Journal of Construction Engineering and Management
Print ISSN 0733-9364
Publisher American Society of Civil Engineers
Peer Reviewed Peer Reviewed
Volume 146
Issue 1
DOI https://doi.org/10.1061/%28ASCE%29CO.1943-7862.0001745
Keywords predictive analytics, data-driven, Big Data, cost estimation, construction management, machine learning, deep learning
Public URL https://uwe-repository.worktribe.com/output/846518
Publisher URL https://ascelibrary.org/journal/jcemd4

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