Delgado, J M D; Oyedele, L; Bilal, M; Ajayi, A; Akanbi, L and Akinade, O (2020) Big data analytics system for costing power transmission projects. Journal of Construction Engineering and Management, 146(1): 05019017, ISSN 0733-9364
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | big data; cost estimation; data-driven; predictive analytics |
| Index terms: | cost estimating, big data, artificial neural network, profit, body of knowledge, dataset, construction sector, cost prediction, case study, modelling, cost estimate, costing |
| Subjects: | data collection methods, modelling and simulation, knowledge management, financial and cost management, analytical methods, economic analysis, accounting and finance, data management, industry analysis, information systems |
| Topics: | Research Practice, Information Management, Engineering Principles, Cost Management, Business Strategy, Digital Applications |
| Descriptive scope: | 4 PCTE |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here