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Application of Stochastic Gradient Boosting Approach to Early Prediction of Safety Accidents at Construction Site

Auteur(s):
Médium: article de revue
Langue(s): en 
Publié dans: Advances in Civil Engineering, , v. 2019
Page(s): 1-9
DOI: 10.1155/2019/1574297
Abstrait:

The construction industry is one of the deadliest industries in the United States and Korea. The number of accidents at a construction site has been recently increasing despite institutional supports and managerial efforts. A proactive prediction of safety accidents is the best way to prevent them, but a dynamic change in particular conditions of a construction project makes the prediction very tricky and complicated. Moreover, preventive work for any safety accident at a construction site mainly depends on the intuitive and subjective opinions of practitioners with limited experience. The stochastic gradient boosting (SGB) approach may be an attractive alternative to conventional methods for predicting safety accidents because of its superior predictive performance. Therefore, SGB is applied to an early prediction of safety accidents at a construction site in order to examine its applicability to the construction safety domain. The prediction result of the proposed model is compared to an artificial neural network model and a decision tree model. The proposed model shows a slightly better result compared to the ANN and DT models. Moreover, the result of the proposed model also demonstrates the advantages of a simple parameter set in constructing a model and a comprehensible decision-making procedure for safety management.

Copyright: © 2019 Yoonseok Shin et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

Publicité

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    sur cette fiche
  • Reference-ID
    10403247
  • Publié(e) le:
    28.12.2019
  • Modifié(e) le:
    28.12.2019