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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Iran University of Medical Sciences
2018-08-01
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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 |
work_keys_str_mv |
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