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.

电伤影响是巨大的。电力投资将继续增加，导致建设项目的复杂性，这无疑会导致伤害和相关影响。机器学习 (ML) 算法用于量化和建模伤害原因；然而，传统的 ML 技术并不能产生最佳结果，因为它们需要精心设计才能将数据转换为合适的形式。在这项研究中，我们通过分析从一家领先的英国电力基础设施公司获得的事件案例，开发并比较了最先进的 ML 算法（深度学习和增强树）与其他传统方法来克服这个问题。使用观察值和预测值之间的统计比较来对开发模型的预测性能进行基准测试。$=$ 0.967 和 Cohen Kappa 测量 $=$0.964）。此外，我们进行了敏感性分析，以确定输入参数和数据大小对模式行为的影响。敏感性分析结果表明深度学习和提升树模型具有很强的泛化能力及其对安全风险管理的有效性。