Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects

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 averageR2 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.

Source: Risk Analysis - February 14, 2020 Category: International Medicine & Public Health Authors: Anuoluwapo Ajayi, Lukumon Oyedele, Hakeem Owolabi, Olugbenga Akinade, Muhammad Bilal, Juan Manuel Davila Delgado, Lukman Akanbi Tags: Original Research Article Source Type: research