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Deep learning models for health and safety risk prediction in power infrastructure projects

Delgado, Juan Manuel Davila; Oyedele, Lukumon; Ajayi, Anuoluwapo; Owolabi, Hakeem; Akinade, Olugbenga; Bilal, Muhammad; Akanbi, Lukman


Juan Manuel Davila Delgado

Hakeem Owolabi
Associate Professor - Project Analytics and Digital Enterprise

Olugbenga Akinade
Associate Professor - AR/VR Development with Artificial Intelligence

Muhammad Bilal
Associate Professor - Big Data Application

Dr Lukman Akanbi
Associate Professor - Big Data Application Developer


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, utilising 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 & 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-squared value for the five regression models was 0.92, with Mean Absolute Error (MAE) 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, minimise project costs (such as third-party insurance and equipment repairs), and offer effective strategies to mitigate H&S risk.


Delgado, J. M. D., Oyedele, L., Ajayi, A., Owolabi, H., Akinade, O., Bilal, M., …Akanbi, L. (2020). Deep learning models for health and safety risk prediction in power infrastructure projects. Risk Analysis, 40(10), 2019-2039.

Journal Article Type Article
Acceptance Date Sep 16, 2019
Online Publication Date Nov 22, 2019
Publication Date Oct 1, 2020
Deposit Date Dec 2, 2019
Publicly Available Date Nov 23, 2021
Journal Risk Analysis
Print ISSN 0272-4332
Electronic ISSN 1539-6924
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 40
Issue 10
Pages 2019-2039
Public URL