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.
Ajayi, A., Oyedele, L., Davila Delgado, J. M., Akanbi, L., Bilal, M., Akinade, O., & Olawale, O. (2019). Big data platform for health and safety accident prediction. World Journal of Science, Technology and Sustainable Development, 16(1), 2-21. https://doi.org/10.1108/WJSTSD-05-2018-0042