چكيده لاتين :
Background and aims: With the developing use of electricity in all aspects of human life, electricity
accidents have also increased. Electricity is the most important type of energy used in workplace and has
a direct relationship between economic development and its consumption. Every year, more than 5,000
deaths from electric shock occur in the US construction industry, accounting for about 20% of the deaths
from accidents. The incidence of electrical accidents is 25 times greater than the incidence of falls.
Therefore, it is essential to take the necessary measures to prevent such incidents. One of the main
components of the prevention policy, is the safety performance assessment of the organization's or
industries by using appropriate performance indicators with related operations. Safety performance
indicators are divided into two main categories: the leading and the lagging indicators. The leading
indicators show the organization's actions in predicting and preventing incidents before they occur, while
the lagging indicators show the organization's performance after the occurrence of the incidents. In many
activities, such as construction phase due to rapid changes in the nature of activities and subsequent
changes in the level of operational risk, it is essential that safety performance indicators become more
responsive and sensitive to changes in the safety level of operations. The aim of this study is to develop
and validate active indicators for assessing the safety performance of electricity in the phase of
construction of oil and gas refineries using Bow Tie and Network Bayesian techniques.
Methods: This is a descriptive-analytical study carried out in the phase of construction of oil and gas
refineries. In the first phase, the construction operations were identified by studying the WBS refinery
project. Work Breakdown Structure is a graphical network or graphical structure for showing a product
or service production method, including hardware, software, services, and other tasks of the organization
or company. At this stage, the main activities of the construction phase were identified using the WBS
project. In this study, hazardous construction phases were identified and classified by PHA. Considering
that determining the causal network and probability of a failure is one of the important factors of risk
assessment and determination of safety performance indicators, in the third stage, the relationship
between the causes of the incident and the occurrence of the underlying causeswere researched using
expert panel. The Bayesian network is an important tool for determining causal relationships and
calculating the probability of accidents. The Bayesian network is a graphical probability model that
shows a set of random variables and conditional dependencies between them. After determining the
probability of occurrence of the root, intermediate and direct causes of the electric shock, the Bow-tie
risk assessment technique used to determine the control measures related to each of the causes. By
surveying the relationship between the causes of the electrical incident, the active indicators related to
the field of electrical safety were identified and in the last step, validation of the indicators over a 6-
month period done by comparing the results of measuring the active indicators and the frequency of
incidents recorded through Regression correlation test was performed.
Results: In this study, six disciplines including electrical, instrumentation, piping, tanks,
communications and buildings, and 18 electrical hazardous activities identified. Based on the results of
the WBS review and the preliminary risk analysis of identified activities, the causal network electric
shock accounted for the expert’s opinion. The probability of occurrence of electric shock was 0.053
calculated using the method of BN. The electric shock caused by a collision with the under-ground cable
(A) and the contact of the scaffolding pipe with the power cable (E) the largest share of the potential for
electric shock. By using technique Bow tie, control measures including a RCD, earthing system, inspection and supervision, training, permit to work at drilling, scaffolding, work to height and
maintenance operations and Ricket Fire, are set to reduce the probability of occurrence of events. 11
active performance indicators include drilling performed with permission to do work, boards with RCD
and Earthing, earthing generators, generators with installed safety instructions, generators with fire
extinguisher, scaffolds authorized to do work, Number of electrical safety inspection, cranes with
electrical insulation cabin, operators trained and earthing Conex. In order to determine the correlation
between active indicators and incident frequency indicators, information on five contractor companies
collected and measured during six months. Then the linear regression model used to determine the
statistical relation. The R-square value of the incident frequency with the active performance indicators
of A, B, C, D and E companies is 0.996, 0.495, 0.998, 0.863 and 0.797, respectively, with the highest
linear regression, respectively C, E, A, B and D companies. The p-value in A, B and D companies is
greater than 0.05 and in C and E companies is less than 0.05. Therefore, these results indicate that the
correlation between incidents frequency as dependent variables and active performance indicators as
Independent variables in A, B and D companies are not significant, but the correlation between C and E
companies is significant. The R-square value of the incident frequency with the active indicators of the
total project was 0.819 and P-value was less than 0.05. Therefore, these results indicate that the
correlation between incident frequency as an associated variable and active indicators as independent
variables in the whole project is significant.
Conclusion: This study was performed to determine the safety performance active indicators of
electrical of construction phase of oil and gas refineries. In previous studies, such as the Podgorski
(2015) and Flahati (2017), the leading performance indicators developed according to ILO-OHS-2001
and OHSAS 18001 management system components. One of the main objectives of the development of
safety performance indicators in construction projects is to rank and compare the safety performance of
the contractors in the project. Therefore, it is necessary to consider all aspects of the performance
management system in order to make a proper judgment of the existing situation. The performance
indicators presented in the Podgorski study (2015) emphasize only the components of the safety and
health management system. While the model presented in the present study categorizes the indicators
according to the definition of Hinze (2013) into two types of active and passive indicator. Given the
rapid changes in operating conditions in the construction phase, leading performance indicators should
be able to detect rapid changes in the level of safety of activities. Abdelhamid TS et al. (2000) declared
the failure to identify unsafe conditions as one of the root causes of construction projects incidents, so
active performance indicators should be able to measure the safety of construction operations in short
term periods. The strength of this study is to use the Bayesian Network to determine the cause of the
incident. The superiority of Bayesian network in assessing the risk and determining the route of the
events is that nodes can considered dependent. Therefore, it is easier to determine the relationships
between the different levels of the causes of the incident and the estimation of the probability of
occurrence of accidents is more accurate. One of the main constraints of previous studies was the lack of
validation of leading performance indicators. In this study, were validated the developed indicators.
Correlation between accident frequency as dependent variable and active performance indicators as
independent variables is high in all five companies, but in firms A, B and D this correlation is not
significant. One of constraints of this study is the short duration of measurement of indicators in a sixmonth
period, so the results will be more accurate with increasing the measurement time of the
indicators. This study indicates that determining the leading indicators in addition to the components of
the safety management system should be based on the type of operations and identified workplace
hazards. The leading performance indicators of the safety management system components are mostly
passive and cannot show the changes in the level of safety of the workplace in a short time. Because
construction operations are inherently high-risk and safety-related changes are taking place rapidly, it is
therefore necessary to adopt active indicators as complementary passive indicators. Considering the
many variables that affect the occurrence of major industrial accidents, determining the causal
relationships between these variables is complex. Therefore, using high reliability methods such as
Bayesian network increases the reliability of the active performance indicators derived from the network
causing accidents.
