Table of Contents Author Guidelines Submit a Manuscript
Advances in Civil Engineering
Volume 2018, Article ID 5078906, 11 pages
Research Article

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

1Department of Construction Management, Tsinghua University, Beijing, China
2Department of Political Science, Tsinghua University, Beijing, China

Correspondence should be addressed to Yu-Sung Su; nc.ude.auhgnist@gnusuyus

Received 21 February 2018; Accepted 8 April 2018; Published 6 June 2018

Academic Editor: Xianbo Zhao

Copyright © 2018 Pin-Chao Liao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.