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S. Abrishami
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A data-based comparison of BN-HRA models in assessing human error probability
An offshore evacuation case study
Bayesian Network (BN) has been increasingly exploited to improve different aspects of Human Reliability Analysis (HRA), resulting in a new generation of HRA techniques, known as BN-HRA models. However, validating and evaluating the accuracy of BN-HRA models is still a challenging task. In this study, we have assessed and compared the performance of some of well-known BN-HRA techniques using human performance data obtained from an offshore evacuation simulation. Based on the role of data in quantifying the BN-HRA models, three categories of BN-HRA models have been considered: (i) BN-CREAM and BN-SPARH, which are based on predefined rules (rule-based methods), (ii) Bayesian Parameter Learning (BPL), which is entirely based on the available data (data-based method), and (iii) BN-SLIM model which is based on both the available data and the predefined rules (hybrid method). The results of the present study show that the data-based methods, i.e., BN-SLIM and BPL, in general outperform the rule-based methods. Cross-validation analysis further demonstrates the superiority of BN-SLIM over BPL, particularly in case of data scarcity.
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Bayesian Network (BN) has been increasingly exploited to improve different aspects of Human Reliability Analysis (HRA), resulting in a new generation of HRA techniques, known as BN-HRA models. However, validating and evaluating the accuracy of BN-HRA models is still a challenging task. In this study, we have assessed and compared the performance of some of well-known BN-HRA techniques using human performance data obtained from an offshore evacuation simulation. Based on the role of data in quantifying the BN-HRA models, three categories of BN-HRA models have been considered: (i) BN-CREAM and BN-SPARH, which are based on predefined rules (rule-based methods), (ii) Bayesian Parameter Learning (BPL), which is entirely based on the available data (data-based method), and (iii) BN-SLIM model which is based on both the available data and the predefined rules (hybrid method). The results of the present study show that the data-based methods, i.e., BN-SLIM and BPL, in general outperform the rule-based methods. Cross-validation analysis further demonstrates the superiority of BN-SLIM over BPL, particularly in case of data scarcity.
Success Likelihood Index Model (SLIM) is one of the widely-used deterministic techniques in human reliability assessment especially when data is insufficient. However, this method suffers from epistemic uncertainty as it extremely relies on expert judgment for determining the model parameters such as the rates and weights of the performance shaping factors (PSFs). Besides, given an operation consisting of several tasks, SLIM calculates the human error probability (HEP) by ignoring possible dependencies among the tasks.
The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We demonstrate how BN-SLIM can consider uncertainty associated with the rates of PSFs by using probability distributions. BN-SLIM is also able to provide a better estimation of human error probability by considering conditional dependencies resulting from common PSFs. The probability updating feature of BN-SLIM can be used to identify the PSFs contributing the most to human failure event. The outperformance of BN-SLIM over SLIM is demonstrated via an illustrative example. ...
The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We demonstrate how BN-SLIM can consider uncertainty associated with the rates of PSFs by using probability distributions. BN-SLIM is also able to provide a better estimation of human error probability by considering conditional dependencies resulting from common PSFs. The probability updating feature of BN-SLIM can be used to identify the PSFs contributing the most to human failure event. The outperformance of BN-SLIM over SLIM is demonstrated via an illustrative example. ...
Success Likelihood Index Model (SLIM) is one of the widely-used deterministic techniques in human reliability assessment especially when data is insufficient. However, this method suffers from epistemic uncertainty as it extremely relies on expert judgment for determining the model parameters such as the rates and weights of the performance shaping factors (PSFs). Besides, given an operation consisting of several tasks, SLIM calculates the human error probability (HEP) by ignoring possible dependencies among the tasks.
The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We demonstrate how BN-SLIM can consider uncertainty associated with the rates of PSFs by using probability distributions. BN-SLIM is also able to provide a better estimation of human error probability by considering conditional dependencies resulting from common PSFs. The probability updating feature of BN-SLIM can be used to identify the PSFs contributing the most to human failure event. The outperformance of BN-SLIM over SLIM is demonstrated via an illustrative example.
The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We demonstrate how BN-SLIM can consider uncertainty associated with the rates of PSFs by using probability distributions. BN-SLIM is also able to provide a better estimation of human error probability by considering conditional dependencies resulting from common PSFs. The probability updating feature of BN-SLIM can be used to identify the PSFs contributing the most to human failure event. The outperformance of BN-SLIM over SLIM is demonstrated via an illustrative example.
Conference paper
(2019)
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Shokoufeh Abrishami, N. Khakzad, Pieter van Gelder, Seyed Mahmoud Hosseini
Success Likelihood Index Model (SLIM) is one of the widely-used methods in human reliability assessment especially when data is insufficient. However, this method suffers from uncertainty as it heavily relies on expert judgment for determining the model parameters such as the rates and weights of the performance shaping factors. The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling the uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We applied both SLIM and BN-SLIM models to a hypothetical example and compared the results. It is shown that BN-SLIM is able to provide a better estimation of human error probability by considering dependencies. The probability updating feature of BN-SLIM in particular makes it possible to use new information to update the prior beliefs about the rates of the performance shaping factors, thus updating the resultant human error probabilities.
...
Success Likelihood Index Model (SLIM) is one of the widely-used methods in human reliability assessment especially when data is insufficient. However, this method suffers from uncertainty as it heavily relies on expert judgment for determining the model parameters such as the rates and weights of the performance shaping factors. The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling the uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We applied both SLIM and BN-SLIM models to a hypothetical example and compared the results. It is shown that BN-SLIM is able to provide a better estimation of human error probability by considering dependencies. The probability updating feature of BN-SLIM in particular makes it possible to use new information to update the prior beliefs about the rates of the performance shaping factors, thus updating the resultant human error probabilities.