A systematic approach towards accident analysis and prevention

Koo, Joonhoi; Kim, Seunghyok; Kim, Hyo‐Suk; Kim, Younghun; Yoon, En Sup

doi:10.1007/s11814-009-0262-z

Cited by 10 publications

(3 citation statements)

References 6 publications

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“…The accidents precursors data collection is proposed in different industrial fields, as: process industry [8], [9], construction field [10] and in manufacturing industry [11].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Occupational accident-precursors data collection and analysis according to Human Factors Analysis and Classification System (HFACS) taxonomy

et al. 2019

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Data were collected in an automotive production plant during a campaign of observations performed by safety experts. A period of one week of observations was done during which safety experts monitored the working activity of an assembly line. All accident-precursors identified were reported in a format and immediately analysed and classified according to HFACS. Each collected element was classified in 3 categories as: unsafe acts (related to human behaviour), unsafe condition (related to the working condition and working organisation) and near miss (a situation that involved workers without physical consequence for them). Then each element was classified according to the four levels of HFACS: individual factor (violation or error), environmental factor, supervision and organisational factor. This step was supported by short interview with workers and/or supervisors involved to better identify the characterising factors of the event. This survey allowed the identification and classification of 100 accident-precursors that could be used in the company where they have been collected and, more in general, in manufacturing companies, to identify behaviours and areas of improvement for health and safety based on more recurrent factors that characterised the observed events, according to the methodology described in Baldissone et al. [1].

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“…The accidents precursors data collection is proposed in different industrial fields, as: process industry [8], [9], construction field [10] and in manufacturing industry [11].…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Occupational accident-precursors data collection and analysis according to Human Factors Analysis and Classification System (HFACS) taxonomy

et al. 2019

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“…The procedure includes the following steps: 1-1. Domino Risk Analysis (1) Perform hazard identification on the existing facilities and additional facilities using the past accident DB [2,11,12] or selection of pertinent process units based on DOW Fire&Explosion index [13].…”

Section: March 2011mentioning

confidence: 99%

“…Objective function (12) Subject to I=E+A={1,…,m}+{1,…,n}={1,…,m+n}, i∈I, set of all facilities J={i+1,i+2,....,n} j∈I, set of all facilities r i =(x i , y i ), 0≤x i ≤X, 0≤y i ≤Y ((X, Y) is the coordinate, corresponding to the site size) r ij =[(x i −x j ) 2 +(y i −y j ) 2 ] 1/2 , rectilinear distance between i and j f(αF ij , βP ij , γM ij )=select (αF ij , βP ij , γM ij ) based on max(r max of DF, r max of DP, r max of DM) α: relative impact of flame parameter β: relative impact of pressure parameter γ: relative impact of missile parameter r ij : satisfy the regulation about concerning facilities (e.g., minimal safety distance between facilities, safety distance from the boundary of the site etc.) r ij ≤r max : r ij =r max means that above distance of r max don't generate domino effect.…”

Section: March 2011mentioning

confidence: 99%

Optimal layout of additional facilities for minimization of domino effects based on worst-case scenarios

Kim

Lee

et al. 2011

Korean J. Chem. Eng.

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Accidents involving domino effects are more serious than other type of accidents. Although there have been studies on such accidents, it is still difficult to examine the actual factors and causes since the domino effect is influenced nonlinearly by factors involving flame, overpressure, and flying objects. We considered the case of adding new facilities to an existing system in a given site. The layout of new facilities suggests positions that minimize the domino effects, based on nonlinear optimization taking domino factors into account. We quantitatively calculated the domino risk of each facility through the concept of combined domino factors (flame, overpressure, and missile). Also, we identified variations of domino damage extent of the target system through comparison of the impacts of domino effect when additional facilities were installed. Simulated annealing was adopted for searching optimal positions. As a case study, we applied the proposed method to the case of adding DME storage tanks in the existing LPG charging facilities. The presented framework of the quantitative assessment of domino risk and safety standard for the layout of additional facilities would be useful for proper layout design for improved accident prevention.

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