Big Data Analytics System for Costing Power Transmission Projects
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 1
Abstract
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.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request.
Acknowledgments
The authors would like to gratefully acknowledge the Engineering and Physical Sciences Research Council and Innovate UK for funding this research under Grant No. 102061, application number: 44746–322224.
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Journal of Construction Engineering and Management
Volume 146 • Issue 1 • January 2020
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©2019 American Society of Civil Engineers.
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Received: Sep 25, 2018
Accepted: May 30, 2019
Published online: Nov 15, 2019
Published in print: Jan 1, 2020
Discussion open until: Apr 15, 2020
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