Deep learning models for health and safety risk prediction in power infrastructure projects

Ajayi, Anuoluwapo; Oyedele, Lukumon; Owolabi, Hakeem; Akinade, Olugbenga; Bilal, Muhammad; Davila Delgado, Juan Manuel; Akanbi, Lukman

Abstract

Inappropriate management of Health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the availability of H&S incident data, utilising them to mitigate accident occurrence effectively is challenging due to inherent limitations of existing data logging methods. In this study, we used a text mining approach for retrieving meaningful terms from data and develop six deep learning (DL) models for H&S risks management in power infrastructure. The DL models include DNNclassify (risk or no risk), DNNreg1 (loss time), DNNreg2 (body injury), DNNreg3 (plant & fleet), DNNreg4 (equipment), and DNNreg5 (environment). An H&S risk database obtained from a leading UK power infrastructure construction company was used in developing the models using the H2O framework of the R language. Performances of DL models were assessed and benchmarked with existing models using test data and appropriate performance metrics. The overall accuracy of the classification model was 0.93. The average R-squared value for the five regression models was 0.92, with Mean Absolute Error (MAE) between 0.91 and 0.94. The presented results, in addition to the developed user-interface module, will help practitioners obtain a better understanding of H&S challenges, minimise project costs (such as third-party insurance and equipment repairs), and offer effective strategies to mitigate H&S risk.

• Journal Article Type: Article
• Acceptance Date: Sep 16, 2019
• Online Publication Date: Nov 22, 2019
• Publication Date: Oct 1, 2020
• Deposit Date: Dec 2, 2019
• Publicly Available Date: Nov 23, 2021
• Journal: Risk Analysis
• Print ISSN: 0272-4332
• Electronic ISSN: 1539-6924
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 40
• Issue: 10
• Pages: 2019-2039
• DOI: https://doi.org/10.1111/risa.13425
• Keywords: Artificial intelligence; deep learning; health and safety risk
• Public URL: https://uwe-repository.worktribe.com/output/4353084