Predicting the Level of Safety Performance Using an Artificial Neural Network

  • Emmanuel Bannor Boateng
  • Manikam Pillay
  • Peter Davis
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 876)


In this study, an artificial neural network model is developed to predict the level of safety performance on construction sites. Adopting an experimental research design, the model employs safety behaviour, near misses, incidents, fatalities, and the safety risk levels as the inputs, while the safety performance level acted as the output. 339 datasets were generated based on expert intuition and professional experiences. A 5-4-1 Multi-Layer Perceptron with back-propagation was sufficient in building the model that has been trained and validated. The results are promising and show good predictive ability. The developed model could help construction and consultancy firms to assess, forecast, and monitor the level of safety performance of construction projects.


Artificial neural network Construction industry Experimental study Safety performance Safety management Prediction 



We would like to give a special thanks to Associate Professor Stephan Chalup for his comments on an earlier version of the manuscript concerning the application of ANN.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Emmanuel Bannor Boateng
    • 1
  • Manikam Pillay
    • 1
  • Peter Davis
    • 2
  1. 1.School of Health SciencesUniversity of NewcastleCallaghanAustralia
  2. 2.School of Architecture and Built EnvironmentUniversity of NewcastleCallaghanAustralia

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