Development and ranking of safety performance indicators using Bayesian network and Analysis Hierarchical Process Case study: Work at height of the Oil and Gas refinery construction phase

Background and aims: The safety and health performance measurement, designed to provide the necessary information on the concern to progress and the current state of the organizationchr('39')s strategies, processes and activities. The purpose of this study is to present a new model for the...

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Main Authors: mohsen falahati, ali karimi, mojtaba zokaei, ali dehghani
Format: Article
Language:fas
Published: Iran University of Medical Sciences 2018-08-01
Series:Salāmat-i kār-i Īrān
Subjects:
Online Access:http://ioh.iums.ac.ir/article-1-2279-en.html
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spelling doaj-a207b12baa3a4fceb5e6dea60f3dd7c42021-01-29T16:48:51ZfasIran University of Medical SciencesSalāmat-i kār-i Īrān1735-51332228-74932018-08-01153172185Development and ranking of safety performance indicators using Bayesian network and Analysis Hierarchical Process Case study: Work at height of the Oil and Gas refinery construction phasemohsen falahati0ali karimi1mojtaba zokaei2ali dehghani3 saveh universty, of medical scences, Social Determinats of Health Research Center ,Saveh University of Medical Sciences ,Saveh ,Iran Associate Professor of Occupational Health Engineering, Faculty of Public Health, Tehran University of Medical Sciences, Tehran, Iran saveh universty, of medical scences, Social Determinats of Health Research Center ,Saveh University of Medical Sciences ,Saveh ,Iran Anzali Trade-Industrial Freezone Organization Background and aims: The safety and health performance measurement, designed to provide the necessary information on the concern to progress and the current state of the organizationchr('39')s strategies, processes and activities. The purpose of this study is to present a new model for the development of safety performance indicators using the probability risk assessment model and applying expertschr('39') opinions. Methods: This descriptive-analytic study was carried out in 3 steps: categorize of the construction face activities and its related hazards identification; formation of the accident causal network occurred in the oil and gas refinery construction phase using the Bayesian network and the selection of key performance indicators using AHP method. Results: The statistical analysis of the recorded accidents showed that 21% of the incidence rate is related to the falling. Among all the construction phase activities, using the WBS analysis of the project, 27 activities with work at height risk of were identified.  18 active performance indicators were extracted the accident causal network  that using SMART criteria and occurrence probability rate  Calculated from the Bayesian network was selected as 5 active key performance indicators. Conclusion: Determining the leading performance indicators is influenced by various organizational, managerial, operational and other factors. As the project progresses, the nature and level of risk of the operation of construction projects is changing. Therefore, indicators of safety performance measurements in these projects should be sensitive to rapid changes. For this reason, active indicators with a short-term measurement period are more effective in measuring the safety performance of construction operations.http://ioh.iums.ac.ir/article-1-2279-en.htmlbayesian networkrefinery construction phasesafetykey performance indicators
collection DOAJ
language fas
format Article
sources DOAJ
author mohsen falahati
ali karimi
mojtaba zokaei
ali dehghani
spellingShingle mohsen falahati
ali karimi
mojtaba zokaei
ali dehghani
Development and ranking of safety performance indicators using Bayesian network and Analysis Hierarchical Process Case study: Work at height of the Oil and Gas refinery construction phase
Salāmat-i kār-i Īrān
bayesian network
refinery construction phase
safety
key performance indicators
author_facet mohsen falahati
ali karimi
mojtaba zokaei
ali dehghani
author_sort mohsen falahati
title Development and ranking of safety performance indicators using Bayesian network and Analysis Hierarchical Process Case study: Work at height of the Oil and Gas refinery construction phase
title_short Development and ranking of safety performance indicators using Bayesian network and Analysis Hierarchical Process Case study: Work at height of the Oil and Gas refinery construction phase
title_full Development and ranking of safety performance indicators using Bayesian network and Analysis Hierarchical Process Case study: Work at height of the Oil and Gas refinery construction phase
title_fullStr Development and ranking of safety performance indicators using Bayesian network and Analysis Hierarchical Process Case study: Work at height of the Oil and Gas refinery construction phase
title_full_unstemmed Development and ranking of safety performance indicators using Bayesian network and Analysis Hierarchical Process Case study: Work at height of the Oil and Gas refinery construction phase
title_sort development and ranking of safety performance indicators using bayesian network and analysis hierarchical process case study: work at height of the oil and gas refinery construction phase
publisher Iran University of Medical Sciences
series Salāmat-i kār-i Īrān
issn 1735-5133
2228-7493
publishDate 2018-08-01
description Background and aims: The safety and health performance measurement, designed to provide the necessary information on the concern to progress and the current state of the organizationchr('39')s strategies, processes and activities. The purpose of this study is to present a new model for the development of safety performance indicators using the probability risk assessment model and applying expertschr('39') opinions. Methods: This descriptive-analytic study was carried out in 3 steps: categorize of the construction face activities and its related hazards identification; formation of the accident causal network occurred in the oil and gas refinery construction phase using the Bayesian network and the selection of key performance indicators using AHP method. Results: The statistical analysis of the recorded accidents showed that 21% of the incidence rate is related to the falling. Among all the construction phase activities, using the WBS analysis of the project, 27 activities with work at height risk of were identified.  18 active performance indicators were extracted the accident causal network  that using SMART criteria and occurrence probability rate  Calculated from the Bayesian network was selected as 5 active key performance indicators. Conclusion: Determining the leading performance indicators is influenced by various organizational, managerial, operational and other factors. As the project progresses, the nature and level of risk of the operation of construction projects is changing. Therefore, indicators of safety performance measurements in these projects should be sensitive to rapid changes. For this reason, active indicators with a short-term measurement period are more effective in measuring the safety performance of construction operations.
topic bayesian network
refinery construction phase
safety
key performance indicators
url http://ioh.iums.ac.ir/article-1-2279-en.html
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