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A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study

机译：基于数据的BN-HRA模型评估人为错误概率的基于数据的比较：海上疏散案例研究

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摘要

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.
机译：贝叶斯网络（BN）越来越探讨改善人类可靠性分析（HRA）的不同方面，导致新一代HRA技术，称为BN-HRA模型。然而，验证和评估BN-HRA模型的准确性仍然是一个具有挑战性的任务。在这项研究中，我们已经评估并比较了使用从海上疏散模拟中获得的人力性能数据的一些众所周知的BN-HRA技​​术的性能。根据数据在量化BN-HRA模型中的作用，已经考虑了三类BN-HRA模型：（i）BN-CREAD和BN-SPARH，基于预定规则（基于规则的方法），（ ii）贝叶斯参数学习（BPL）完全基于可用数据（基于数据的方法）和（iii）BN-Slim模型，该模型基于可用数据和预定义规则（混合方法）。本研究的结果表明，基于数据的方法，即BN-Slim和BPL，一般优于基于规则的方法。交叉验证分析进一步证明了BPL上BPR的优越性，特别是在数据稀缺的情况下。

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