Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Nov 10;9(12):e22167.
doi: 10.1016/j.heliyon.2023.e22167. eCollection 2023 Dec.

Comprehensive factor analysis and risk quantification study of fall from height accidents

Jun Long Peng  1 , Xiao Liu  1 , Chao Peng  1 , Yu Shao  1
Affiliations

Comprehensive factor analysis and risk quantification study of fall from height accidents

Jun Long Peng et al. Heliyon. .

Abstract

Working at heights poses frequent and significant risks, demanding scientific approaches for investigating fall-from-height (FFH) incidents and proposing preventive measures to enhance building safety. Nevertheless, ongoing research on analyzing the causal factors behind fall-from-height accidents lacks a comprehensive qualitative and quantitative assessment of the interplay between these factors. To bridge this gap, this study introduces an integrated risk analysis model. Utilizing incident reports and leveraging the multi-case rootedness theory, the model initially identifies influential elements. Subsequently, employing the Grey Decision Making Laboratory (Grey-DEMATEL) and Interpretive Structural Modeling (ISM) techniques, a hierarchical network is constructed, followed by the transformation of this hierarchical network model into a Bayesian Network (BN) model using GeNie2.0 software. Ultimately, the study was based on data from 420 accident cases and analyzed the causes and diagnosis of the accidents. The findings indicate that A5 (Low-security awareness) is the most significant factor contributing to falls from great heights and that the connection between the components is dynamic and non-linear rather than simply independent and linear. Furthermore, the study established a likelihood of occurrence of such incidents of up to 57 % and ranked the probability of occurrence of each contributing component in the case of a fall from height. This study presents a scientifically valid method for analyzing fall-from-height accidents. Experimental results confirm the model's applicability, empowering contractors to improve safety management by accessing precise risk information and prioritizing preventive measures against interrelated accidents. The model facilitates informed decision-making for contractors to effectively mitigate fall-from-height risks and establish a safer working environment.

Keywords: Causal analysis; Falls-from-heights accident (FFH); Grey-DEMATEL-ISM-BN; Risk analysis; Risk quantification.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Factor framework structure diagram.
Fig. 2
Fig. 2
Analysis method framework diagram.
Fig. 3
Fig. 3
Cause and effect diagram.
Fig. 4
Fig. 4
Hierarchical diagram of the causes of FFH.
Fig. 5
Fig. 5
Causal reasoning diagram for FFH.
Fig. 6
Fig. 6
Diagnostic reasoning diagram for FFH.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Chi C.-F., Chang T.-C., Ting H.-I. Accident patterns and prevention measures for fatal occupational falls in the construction industry. Appl. Ergon. 2005;36(4):391–400. - PubMed
    1. Sánchez F.A.S., Peláez G.I.C., Alís J.C. Occupational safety and health in construction: a review of applications and trends. Ind. Health. 2017;55(3):210–218. - PMC - PubMed
    1. Ofori G. Nature of the construction industry, its needs and its development: a review of four decades of research. J. Constr. Dev. Ctries. (JCDC) 2015;20(2):115.
    1. Zhou Z., Goh Y.M., Shi Q., Qi H., Liu S. Data-driven determination of collapse accident patterns for the mitigation of safety risks at metro construction sites. Tunn. Undergr. Space Technol. 2022;127
    1. Rafindadi A.D.u., et al. Analysis of the causes and preventive measures of fatal fall-related accidents in the construction industry. Ain Shams Eng. J. 2022;13(4)

LinkOut - more resources