Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects
Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects
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DOI：
10.1111/risa.13425
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发表时间：
2019-11-22
期刊：
影响因子：
3.8
通讯作者：
Akanbi, Lukman
中科院分区：
文献类型：
--
作者：
Ajayi, Anuoluwapo;Oyedele, Lukumon;Akanbi, Lukman
电力基础设施项目中健康与安全 (H&S) 风险管理不当可能会导致职业事故和设备损坏。工作中的事故会对工人、公司和公众产生不利影响。尽管可以获得健康与安全事故数据，但由于现有数据记录方法的固有局限性，利用它们有效地减少事故发生仍然具有挑战性。在这项研究中，我们使用文本挖掘方法从数据中检索有意义的术语，并开发了六个用于电力基础设施中 H&S 风险管理的深度学习 (DL) 模型。深度学习模型包括 DNNclassify（风险或无风险）、DNNreg1（损失时间）、DNNreg2（身体伤害）、DNNreg3（工厂和车队）、DNNreg4（设备）和 DNNreg5（环境）。使用 R 语言的 H2O 框架开发模型时使用了从英国一家领先的电力基础设施建设公司获得的 H&S 风险数据库。使用测试数据和适当的性能指标，对深度学习模型的性能进行评估并与现有模型进行基准测试。分类模型的总体准确率为0.93。五个回归模型的平均 R-2 值为 0.92，平均绝对误差在 0.91 到 0.94 之间。除了开发的用户界面模块之外，所提出的结果将帮助从业者更好地了解健康与安全挑战，最大限度地降低项目成本（例如第三方保险和设备维修），并提供有效的策略来减轻健康与安全风险。
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 R-2 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.