Deep learning and Boosted trees for injuries prediction in power infrastructure projects

Abstract

Electrical injury impacts are substantial and massive. Investments in electricity will continue to increase, leading to construction project complexities, which undoubtedly contribute to injuries and associated effects. Machine learning (ML) algorithms are used to quantify and model causes of injuries; however, conventional ML techniques do not produce optimal results since they require careful engineering to transform data into suitable forms. In this study, we develop and compare state-of-the-art ML algorithms (deep learning and boosted trees) with other conventional methods to overcome this problem by analyzing incident cases obtained from a leading UK power infrastructure company. The predictive performance of the developed models was benchmarked using a statistical comparison between observations and predicted values. Results showed that the implementation of deep feedforward neural networks achieved the best predictions (accuracy = 0.967 and Cohen Kappa measure = 0.964). Furthermore, we conduct a sensitivity analysis to determine the effect of input parameters and data sizes on the modes’ behavior. The sensitivity analysis results showed strong generalization abilities of the deep learning and boosted tree models and their effectiveness for safety risks management.