To read the full version of this content please select one of the options below:

Big data platform for health and safety accident prediction

Anuoluwapo Ajayi (Faculty of Business and Law, University of the West of England, Bristol, UK)
Lukumon Oyedele (Bristol Enterprise and Innovation Centre, Bristol Business School, University of the West of England, Bristol, UK)
Juan Manuel Davila Delgado (Big Data Analytics Lab, University of the West of England Bristol, Bristol, UK)
Lukman Akanbi (Big Data Analytics Lab, University of the West of England Bristol, Bristol, UK) (Department of Computer Science and Engineering, Faculty of Technology, Obafemi Awolowo University, Ile-Ife, Nigeria)
Muhammad Bilal (Big Data Analytics Lab, University of the West of England Bristol, Bristol, UK)
Olugbenga Akinade (Big Data Analytics Lab, University of the West of England Bristol, Bristol, UK)
Oladimeji Olawale (Faculty of Business and Law, University of the West of England, Bristol, UK)

World Journal of Science, Technology and Sustainable Development

ISSN: 2042-5945

Article publication date: 30 October 2018

Issue publication date: 7 January 2019

1848

Abstract

Purpose

The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.

Design/methodology/approach

The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.

Findings

The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.

Originality/value

This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.

Keywords

Citation

Ajayi, A., Oyedele, L., Davila Delgado, J.M., Akanbi, L., Bilal, M., Akinade, O. and Olawale, O. (2019), "Big data platform for health and safety accident prediction", World Journal of Science, Technology and Sustainable Development, Vol. 16 No. 1, pp. 2-21. https://doi.org/10.1108/WJSTSD-05-2018-0042

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

Related articles