Big Data Analytics System for Costing Power Transmission Projects

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 Modelling 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 is fit for 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.

Supply Chain Management and Big Data Concept Effects on Economic Sustainability of Building Design and Project Planning

“…This consideration assumes that the use of advanced technologies and the use of data analytics and the concept of big data can shorten the delivery of material, which can affect the shortening of construction time [6,16,31,32]. If the first hypothesis is assumed, a high degree of correlation with the second hypothesis can be expected in this relationship.…”

“…This is also a logical assumption when monitoring key performance indicators. In implementing this concept (several studies have confirmed that cost reduction may be a sufficient motivator for implementing BD and ICT [4,32]), it could be one of the most important motivators. If the dependence between the use of BD in SCM and cost reduction is also demonstrated empirically, this can lead to faster implementation of these technologies.…”

Expressway Project Cost Estimation With a Convolutional Neural Network Model

“…Yang et al [38] used a CNN to learn important features related to rice yields from low altitude remote-sensing images and established a model to predict grain yield. Delgado et al [39] proposed a prediction analysis and modeling system and used a 2.75-million-point power-transmission-project dataset for conducting a case study. Three of the most popular cost-estimation models (linear regression, support vector regression, and ANNs) based on big data have been brought to fruition.…”