Abstract
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.
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08 January 2019
The original version of the book was inadvertently published with incorrect copyright names in Chapters “Measuring collaborative emergent behavior in multi-agent reinforcement learning”.
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Acknowledgements
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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Boateng, E.B., Pillay, M., Davis, P. (2019). Predicting the Level of Safety Performance Using an Artificial Neural Network. In: Ahram, T., Karwowski, W., Taiar, R. (eds) Human Systems Engineering and Design. IHSED 2018. Advances in Intelligent Systems and Computing, vol 876. Springer, Cham. https://doi.org/10.1007/978-3-030-02053-8_107
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DOI: https://doi.org/10.1007/978-3-030-02053-8_107
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