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Estimating the Influence of Improper Workplace Environment on Human Error: Posterior Predictive Analysis

Autor(en):




Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Advances in Civil Engineering, , v. 2018
Seite(n): 1-11
DOI: 10.1155/2018/5078906
Abstrakt:

A model for identifying, analyzing, and quantifying the mechanisms for the influence of improper workplace environment on human error in elevator installation is proposed in this study. By combining a modification of a human error model with real-world inspection data collected by an elevator installation company, the influence paths of improper workplace environment on the conditional probability of human error were quantified using a Bayesian network parameter-learning estimation method and posterior predictive simulation. Under the condition of an improper workplace environment, the probability of human error increased by 80% of its original value, a factor much higher than that resulting from improper management. The most probable influence was found to be improper workmanship and changes in the information required by the worker, thus triggering cognitive failure and consequent unsafe actions by workers. The proposed methodology (posterior predictive simulation) provides a new approach in construction studies for quantifying the probabilistic levels of various causal paths, and the results show the key mechanism for the influence of improper workplace environment on human error using real-world mechanical installation data.

Copyright: © 2018 Pin-Chao Liao et al.
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Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

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  • Veröffentlicht am:
    30.11.2018
  • Geändert am:
    02.06.2021