[ 9th September 2022 by Jiayu Zhang 0 Comments ]

Big data platform for health and safety accident prediction, Anuoluwapo Ajayi, Prof. Lukumon Oyedele, Juan Delgado, Lukman Akanbi, Muhammad Bilal, Olugbenga Akinade and Oladimeji Olawale

Anuoluwapo Ajayi, Oladimeji Olawale
Faculty of Business and Law
University of the West of England, Bristol
UK
Email: anuajayi@yahoo.com
Prof. Lukumon Oyedele
Bristol Enterprise and Innovation Centre, Bristol Business School
University of the West of England, Bristol
UK
Juan Manuel Davila Delgado, Muhammad BilalOlugbenga Akinade
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

DOI: 10.1108/WJSTSD-05-2018-0042

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: Big data analytics; Health and safety; Machine learning; Health hazards analytics.
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

 

WJSTSD V16 N1 2019 Ajayi et al.pdf
WJSTSD V16 N1 2019 Ajayi et al.pdf
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