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, utilizing 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 and 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 R2 value for the five regression models was 0.92, with mean absolute error 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, minimize project costs (such as third-party insurance and equipment repairs), and offer effective strategies to mitigate H&S risk.
Keywords: Artificial intelligence; deep learning; health and safety risk.
© 2019 Society for Risk Analysis.