Comprehensive factor analysis and risk quantification study of fall from height accidents
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 l...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023093751 |
| _version_ | 1797384033255030784 |
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| author | Jun long Peng Xiao Liu Chao Peng Yu Shao |
| author_facet | Jun long Peng Xiao Liu Chao Peng Yu Shao |
| author_sort | Jun long Peng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T21:29:37Z |
| format | Article |
| id | doaj.art-de288eb58b714a48bb48dcc01a57b71c |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T21:29:37Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-de288eb58b714a48bb48dcc01a57b71c2023-12-21T07:33:15ZengElsevierHeliyon2405-84402023-12-01912e22167Comprehensive factor analysis and risk quantification study of fall from height accidentsJun long Peng0Xiao Liu1Chao Peng2Yu Shao3Changsha University of Science & Technology, Chang Sha City, ChinaCorresponding author.; Changsha University of Science & Technology, Chang Sha City, ChinaChangsha University of Science & Technology, Chang Sha City, ChinaChangsha University of Science & Technology, Chang Sha City, ChinaWorking 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.http://www.sciencedirect.com/science/article/pii/S2405844023093751Falls-from-heights accident (FFH)Grey-DEMATEL-ISM-BNRisk analysisRisk quantificationCausal analysis |
| spellingShingle | Jun long Peng Xiao Liu Chao Peng Yu Shao Comprehensive factor analysis and risk quantification study of fall from height accidents Heliyon Falls-from-heights accident (FFH) Grey-DEMATEL-ISM-BN Risk analysis Risk quantification Causal analysis |
| title | Comprehensive factor analysis and risk quantification study of fall from height accidents |
| title_full | Comprehensive factor analysis and risk quantification study of fall from height accidents |
| title_fullStr | Comprehensive factor analysis and risk quantification study of fall from height accidents |
| title_full_unstemmed | Comprehensive factor analysis and risk quantification study of fall from height accidents |
| title_short | Comprehensive factor analysis and risk quantification study of fall from height accidents |
| title_sort | comprehensive factor analysis and risk quantification study of fall from height accidents |
| topic | Falls-from-heights accident (FFH) Grey-DEMATEL-ISM-BN Risk analysis Risk quantification Causal analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023093751 |
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