TY - JOUR

T1 - Robust data-driven human reliability analysis using credal networks

AU - Morais, Caroline

AU - Estrada-Lugo, Hector Diego

AU - Tolo, Silvia

AU - Jacques, Tiago

AU - Moura, Raphael

AU - Beer, Michael

AU - Patelli, Edoardo

N1 - Funding Information: Caroline Morais gratefully acknowledges the Brazilian Oil & Gas regulator ANP (Agencia Nacional do Petroleo, Gas Natural e Biocombustiveis) for the support for her research. Hector Diego Estrada-Lugo gratefully acknowledges the Consejo Nacional de Ciencia y Tecnologıa (CONACyT) for the scholarship awarded by the Mexican government for graduate studies. Edoardo Patelli was partially supported by the EPSRC grant EP/R020558/2 Resilience Modelling Framework for Improved Nuclear Safety (NuRes). Funding Information: Caroline Morais gratefully acknowledges the Brazilian Oil & Gas regulator ANP (Agencia Nacional do Petroleo, Gas Natural e Biocombustiveis) for the support for her research. Hector Diego Estrada-Lugo gratefully acknowledges the Consejo Nacional de Ciencia y Tecnolog?a (CONACyT) for the scholarship awarded by the Mexican government for graduate studies. Edoardo Patelli was partially supported by the EPSRC grant EP/R020558/2 Resilience Modelling Framework for Improved Nuclear Safety (NuRes).

PY - 2022/2

Y1 - 2022/2

N2 - Despite increasing collection efforts of empirical human reliability data, the available databases are still insufficient for understanding the relationships between human errors and their influencing factors. Currently, probabilistic tools such as Bayesian network are used to model data uncertainty requiring the estimation of conditional probability tables from data that is often not available. The most common solution relies on the adoption of assumptions and expert elicitation to fill the gaps. This gives an unjustified sense of confidence on the analysis. This paper proposes a novel methodology for dealing with missing data using intervals comprising the lowest and highest possible probability values. Its implementation requires a shift from Bayesian to credal networks. This allows to keep track of the associated uncertainty on the available data. The methodology has been applied to the quantification of the risks associated to a storage tank depressurisation of offshore oil & gas installations known as FPSOs and FSOs. The critical task analysis is converted to a cause-consequence structure and used to build a credal network, which extracts human factors combinations from major accidents database defined with CREAM classification scheme. Prediction analysis shows results with interval probabilities rather than point values measuring the effect of missing-data variables. Novel decision-making strategies for diagnostic analysis are suggested to unveil the most relevant variables for risk reduction in presence of imprecision. Realistic uncertainty depiction implies less conservative human reliability analysis and improve risk communication between assessors and decision-makers.

AB - Despite increasing collection efforts of empirical human reliability data, the available databases are still insufficient for understanding the relationships between human errors and their influencing factors. Currently, probabilistic tools such as Bayesian network are used to model data uncertainty requiring the estimation of conditional probability tables from data that is often not available. The most common solution relies on the adoption of assumptions and expert elicitation to fill the gaps. This gives an unjustified sense of confidence on the analysis. This paper proposes a novel methodology for dealing with missing data using intervals comprising the lowest and highest possible probability values. Its implementation requires a shift from Bayesian to credal networks. This allows to keep track of the associated uncertainty on the available data. The methodology has been applied to the quantification of the risks associated to a storage tank depressurisation of offshore oil & gas installations known as FPSOs and FSOs. The critical task analysis is converted to a cause-consequence structure and used to build a credal network, which extracts human factors combinations from major accidents database defined with CREAM classification scheme. Prediction analysis shows results with interval probabilities rather than point values measuring the effect of missing-data variables. Novel decision-making strategies for diagnostic analysis are suggested to unveil the most relevant variables for risk reduction in presence of imprecision. Realistic uncertainty depiction implies less conservative human reliability analysis and improve risk communication between assessors and decision-makers.

KW - CREAM

KW - Credal network

KW - FPSO/FSO

KW - Human reliability analysis (HRA)

KW - Missing data

KW - Quantified bow-tie

UR - http://www.scopus.com/inward/record.url?scp=85117831662&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2021.107990

DO - 10.1016/j.ress.2021.107990

M3 - Article

AN - SCOPUS:85117831662

VL - 218

JO - Reliability engineering & system safety

JF - Reliability engineering & system safety

SN - 0951-8320

IS - Part A

M1 - 107990

ER -