# Comparison of Methods for Dependency

Authors: Marko Čepin
Journal: Science and Technology of Nuclear Installations
 (2008)

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## Abstract

The human reliability analysis (HRA) is a highly subjective evaluation of human performance, which is an input for probabilistic safety assessment, which deals with many parameters of high uncertainty. The objective of this paper is to show that subjectivism can have a large impact on human reliability results and consequently on probabilistic safety assessment results and applications. The objective is to identify the key features, which may decrease subjectivity of human reliability analysis. Human reliability methods are compared with focus on dependency comparison between Institute Jožef Stefan human reliability analysis (IJS-HRA) and standardized plant analysis risk human reliability analysis (SPAR-H). Results show large differences in the calculated human error probabilities for the same events within the same probabilistic safety assessment, which are the consequence of subjectivity. The subjectivity can be reduced by development of more detailed guidelines for human reliability analysis with many practical examples for all steps of the process of evaluation of human performance.


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## Body

## 1. Introduction

The human reliability
analysis (HRA) is a systematic framework, which includes the process of
evaluation of human performance and associated impacts on structures, system, and components for a complex facility. The process and the results are highly
subjective, and they are the input for probabilistic safety assessment (PSA),
which deals with many parameters of high uncertainty [1–4].Many methods
connected with HRA were developed in the last decades: for example, technique
for human error rate prediction (THERP) [5], systematic human
action reliability procedure (SHARP) [6], accident sequence
evaluation program (ASEP) [7], a technique for
human event analysis (ATHEANA) [8, 9], cognitive reliability
and error analysis method (CREAM) [10], human cognitive
reliability (HCR) [11], standardized plant
analysis risk HRA (SPAR-H) [12], and Institute
Jožef Stefan human reliability
analysis (IJS-HRA) [13–15].Those methods have
some unique and some common features [16, 17]. It is difficult
to judge them or to compare them in sense, which method is better than others.
It is observed that in
the methods developed recently more attention was given to the cognitive portion
of human failure events (HFEs) [16, 17]. An important
feature is the dependency [13, 18], which is more
emphasized at more recent methods, although the standpoint was stated years ago
with THERP [5]. The mentioned
methods use the data, the human reliability databases. Well ago, less data was
available and many specific human error probabilities and human shaping
factors, which adjust those probabilities, were determined based on expert
judgement. Nowadays, much more data is available due to more experience in the
plant operation and due to more training in plant simulators. This may lead to
the conclusion that more recent methods are less subjective.The objective of
the paper is to show that subjectivism can largely impact the HRA results and
consequently the results and applications of PSA in a nuclear power plant (NPP)
with special emphasis on consideration of dependency. The objective is to
identify the key features, which may decrease subjectivity of HRA.Two methods from
the set mentioned above are selected for their detailed comparison in an
example case of real probabilistic safety assessment model, which include human
reliability analysis. Those two are: SPAR-H [12] and IJS-HRA [13, 14]. They are
selected as they are relatively new methods, which encompass the previous
knowledge in the field, which are relatively simple for their application, and which pay an
acceptable level of attention to the issue of dependency, which is the focus of
the work.


## 2. Methods

### 2.1. IJS-HRA

Figure1 shows the scheme of the IJS-HRA method [13]. The method for
evaluation of HFE is developed including consideration about dependencies
between HFE [5, 13]. Figure 1 shows that identification of HFE distinguishes
preinitiator events (i.e., preinitiators), initiator events (i.e., initiators)
and postinitiator events (i.e., postinitiators). Preinitiators are the events that may cause
the equipment to be unavailable before the initiating event has occurred.
Initiators are the events that may contribute to the occurrence of initiating
events. Postinitiators are the events, which are connected with human actions
to prevent accident or mitigate its consequences after initiating event has
occurred. Evaluation of HFE including evaluation of dependencies integrates
assessment of human error probabilities (HEPs) with plant information, operator
interview, simulator experience, and plant database.Figure 1
Scheme of
IJS-HRA method.The five levels of
dependency are determined according to THERP: zero dependency (ZD), low
dependency (LD), moderate dependency (MD), high dependency (HD), and complete
dependency (CD) [5]. Human error probability (HEPs) of dependent HFE
A and B is determined according to equation: PXD(PB∣PA)=PA∗(1+K⋅PB)/(K+1);
where K=0,1,6,19,∞, for dependency levels ZD, LD, MD, HD, and CD, where
X=Z,L,M,H, and C, respectively [5].Figures2 and 3 show how dependency between HFE is determined for
preinitiators and for postinitiators, respectively. Initiators are treated
similarly as postinitiators. For preinitiators, there is an additional
algorithm, which from independent HFE A and its dependent event HFE B
calculates their HEP as the geometry average of both [13].Figure 2
IJS-HRA dependency—preinitiator HFE.Figure 3
IJS-HRA dependency—postinitiator HFE.Figures2 and 3 show that based on the parameters, which are
connected with their representative HFE, the dependency evaluation code is
identified (e.g., LD12). Dependency evaluation code consists of first two
characters identifying the level of dependency (e.g., ZD, LD, MD, HD, and CD).
The next numbers in the code represent the scenario number of the corresponding
scenario from dependency method presented in its respective figure and identify
parameters that are important for determining the level of dependency: for
example, cue, time between, crew, stress, complexity, location, system, action description, procedure, timing,
person, and action similarity [13]. For example,
for 2 dependent postinitiators, a dependency level LD is determined on Figure 3 (LD12), which shows: different cue, 5–30 minutes between the events, low
stress, simple action, and no change of probability needed as joined HEP>1E-5.


### 2.2. SPAR-H

Standardized plant
analysis risk HRA (SPAR-H) is a method for estimating the human error
probabilities (HEPs) associated with operator actions and decisions in nuclear
power plants [12]. Table 1 shows how dependency between HFE is determined. Five
levels of dependency are determined, similarly to THERP and IJS-HRA. The
parameters for determining the level of dependency differ from THERP and from
IJS-HRA.Table 1
SPAR-H dependency.
Condition numberCrew (same or different)Time (close in time or not close in time)Location (same or
different)Cues (additional or no additional)Dependency1SCSNACOMPLETEWhen considering2ACOMPLETErecovery in a series, for3DNAHIGHexample, 2nd, 3rd, or4AHIGH4th checker:5NCSNAHIGHif this error is the6AMODERATE3rd error in the sequence,7DNAMODERATEthen the dependency is8ALOWat leastmoderate;9DCSNAMODERATEif this error is the10AMODERATE4th error in the sequence,11DNAMODERATEthen the dependency12AMODERATEis at leasthigh13NCSNALOW14ALOW15DNALOW16ALOW17ZERO


## 2.1. IJS-HRA

Figure1 shows the scheme of the IJS-HRA method [13]. The method for
evaluation of HFE is developed including consideration about dependencies
between HFE [5, 13]. Figure 1 shows that identification of HFE distinguishes
preinitiator events (i.e., preinitiators), initiator events (i.e., initiators)
and postinitiator events (i.e., postinitiators). Preinitiators are the events that may cause
the equipment to be unavailable before the initiating event has occurred.
Initiators are the events that may contribute to the occurrence of initiating
events. Postinitiators are the events, which are connected with human actions
to prevent accident or mitigate its consequences after initiating event has
occurred. Evaluation of HFE including evaluation of dependencies integrates
assessment of human error probabilities (HEPs) with plant information, operator
interview, simulator experience, and plant database.Figure 1
Scheme of
IJS-HRA method.The five levels of
dependency are determined according to THERP: zero dependency (ZD), low
dependency (LD), moderate dependency (MD), high dependency (HD), and complete
dependency (CD) [5]. Human error probability (HEPs) of dependent HFE
A and B is determined according to equation: PXD(PB∣PA)=PA∗(1+K⋅PB)/(K+1);
where K=0,1,6,19,∞, for dependency levels ZD, LD, MD, HD, and CD, where
X=Z,L,M,H, and C, respectively [5].Figures2 and 3 show how dependency between HFE is determined for
preinitiators and for postinitiators, respectively. Initiators are treated
similarly as postinitiators. For preinitiators, there is an additional
algorithm, which from independent HFE A and its dependent event HFE B
calculates their HEP as the geometry average of both [13].Figure 2
IJS-HRA dependency—preinitiator HFE.Figure 3
IJS-HRA dependency—postinitiator HFE.Figures2 and 3 show that based on the parameters, which are
connected with their representative HFE, the dependency evaluation code is
identified (e.g., LD12). Dependency evaluation code consists of first two
characters identifying the level of dependency (e.g., ZD, LD, MD, HD, and CD).
The next numbers in the code represent the scenario number of the corresponding
scenario from dependency method presented in its respective figure and identify
parameters that are important for determining the level of dependency: for
example, cue, time between, crew, stress, complexity, location, system, action description, procedure, timing,
person, and action similarity [13]. For example,
for 2 dependent postinitiators, a dependency level LD is determined on Figure 3 (LD12), which shows: different cue, 5–30 minutes between the events, low
stress, simple action, and no change of probability needed as joined HEP>1E-5.


## 2.2. SPAR-H

Standardized plant
analysis risk HRA (SPAR-H) is a method for estimating the human error
probabilities (HEPs) associated with operator actions and decisions in nuclear
power plants [12]. Table 1 shows how dependency between HFE is determined. Five
levels of dependency are determined, similarly to THERP and IJS-HRA. The
parameters for determining the level of dependency differ from THERP and from
IJS-HRA.Table 1
SPAR-H dependency.
Condition numberCrew (same or different)Time (close in time or not close in time)Location (same or
different)Cues (additional or no additional)Dependency1SCSNACOMPLETEWhen considering2ACOMPLETErecovery in a series, for3DNAHIGHexample, 2nd, 3rd, or4AHIGH4th checker:5NCSNAHIGHif this error is the6AMODERATE3rd error in the sequence,7DNAMODERATEthen the dependency is8ALOWat leastmoderate;9DCSNAMODERATEif this error is the10AMODERATE4th error in the sequence,11DNAMODERATEthen the dependency12AMODERATEis at leasthigh13NCSNALOW14ALOW15DNALOW16ALOW17ZERO


## 3. Analysis and Results

### 3.1. Qualitative Comparison

Table2 shows how dependency determined in IJS-HRA method
suits the dependency determined in SPAR-H method (theoretical comparison of
both dependency methods).Table 2
Comparison of dependency levels—all scenarios are presented.
PostinitiatorsPreinitiatorsIJS-HRASPAR-HIJS-HRASPAR-HCD1, HD3, HD5, MD7,CD1MD9, MD11, LD13, LD15CD1CD1, HD3HD2CD1, HD3, MD9, MD11HD2CD2, HD4HD4MD6, LD8, LD14, LD16MD4HD5, MD7HD6MD6, LD8, LD14, LD16LD6MD6, LD8MD8MD6, LD8, LD14, LD16HD8CD1, HD3MD10MD6, LD8, LD14, LD16MD10CD2, HD4LD12MD6, LD8, LD14, LD16LD12HD5, MD7HD14MD6, LD8ZD14MD6, LD8HD16MD6, LD8CD16CD1, CD2, HD3, HD4MD18MD6, LD8HD17HD5, MD7MD20MD6, LD8MD19MD6, LD8LD22MD6, LD8LD21CD1, CD2, HD3, HD4ZD24LD14, LD16ZD23HD5, MD6, MD7, LD8ZD25MD9, MD10, MD11, MD12, LD13, LD14, LD15, LD16CD1, CD2, HD3, HD4, HD5, MD6, MD7, LD8, MD9,ZD27MD10, MD11, MD12, LD13, LD14, LD15, LD16Table3 is the subset of Table 2. Table 3 focuses only to those scenarios (specific scenario
suits specific set of parameters), which suit real HFE considered in the
specific HRA (practical comparison of both dependency methods based on specific
PSA model). Both tables show that for specific HFE their respective HEP is
evaluated as a different value, if it is determined with one or the other
method.Table 3
Comparison of dependency levels—only scenarios, which are applicable for HFE of the specific PSA model.
PreinitiatorsPostinitiatorsIJS-HRASPAR-HIJS-HRASPAR-HLD12HD5CD1CD1ZD27MD7, LD13,
LD16MD8HD4, LD8HD17HD5ZD24LD14, LD16LD12LD8HD2CD2, MD12LD22MD6, LD8MD20LD8


### 3.2. Quantitative Comparison

64 HFEs exist in
the PSA model, which HEP is changed if HRA dependency method changes. Table4 shows a part of those HFE with identified dependency
levels and respective HEP for both methods IJS-HRA and SPAR-H. Terms CALC and
IND marked at preinitiators represent the calculation of final HEP as the
geometry average between the independent value of HEP for action at one train
and the respective dependent HEP assessed as low dependency (LD12) for similar
action at the other train.Table 4
Selected HFE with quantified HEP (for IJS-HRA and for SPAR-H).
Basic event IDDependency level IJS-HRAFinal HEP IJS-HRADependency level SPAR-HFinal HEP SPAR-HPRE_INI_01CALC, IND, LD121,91E-03HD55,00E-01PRE_INI_02CALC, IND, LD121,91E-03HD55,00E-01POST_INI_34ZD244,52E-03LD165,43E-02POST_INI_42MD81,71E-01LD88,08E-02POST_INI_53ZD241,58E-02LD146,50E-02POST_INI_63LD225,07E-02HD-4th-in-seq5,00E-01POST_INI_66HD25,16E-01MD121,70E-01POST_INI_69ZD241,04E-03LD145,10E-02POST_INI_79ZD241,96E-04MD-3th-in-seq1,43E-01POST_INI_83MD181,45E-01LD85,28E-02POST_INI_88MD201,43E-01LD85,06E-02POST_INI_102ZD242,91E-04HD-5th-in-seq5,00E-01Table5 shows the results of risk increase factor and risk decrease
factor of selected HFE calculated based on analysis runs with PSA model based
on IJS-HRA dependency and based on SPAR-H dependency considered. Selected HFE
in the table are those with RDF>1,05 and RIF>2,
which are a criteria for identification of risk significant events. The
differences between both cases are very large.Table 5
Results of importance of HFE.
IJS-HRAHFE identificationRDFHFE identificationRIFPOST_INI_421,13E+00POST_INI_042,26E+02POST_INI_631,09E+00POST_INI_127,46E+01POST_INI_881,09E+00POST_INI_1004,49E+01POST_INI_953,66E+01INI_012,34E+01INI_022,34E+01POST_INI_1022,23E+01POST_INI_021,75E+01POST_INI_346,73E+00POST_INI_353,19E+00POST_INI_692,68E+00POST_INI_632,62E+00POST_INI_602,01E+00SPAR-HHFE identificationRDFHFE identificationRIFPRE_INI_061,01E+01POST_INI_535,76E+00PRE_INI_058,18E+00POST_INI_045,63E+00POST_INI_1022,07E+00POST_INI_531,55E+00PRE_INI_091,51E+00PRE_INI_101,51E+00PRE_INI_041,40E+00PRE_INI_011,40E+00PRE_INI_021,38E+00PRE_INI_031,38E+00POST_INI_791,06E+00Table5 shows that identification of important HFE shows only
one HFE, which is identified as important in both analyses (POST_INI_04, which
deals with operator establishing auxiliary feedwater pumps). The difference
between both cases about the core damage frequency is very large, too. It differs
for more than one order of magnitude.Figure4 shows a comparison of fractional contribution of HFE
for both analyses. The figure shows that there are no comparable results:
events, which contribute significantly, if IJS-HRA dependency is considered,
can be insignificant, if SPAR-H dependency is considered and vice versa.Figure 4
Comparison of fractional contribution of HFE.Similarly, large
differences exist if instead of five levels of dependency less dependency
levels are determined with different equations for evaluation of dependency.


## 3.1. Qualitative Comparison

Table2 shows how dependency determined in IJS-HRA method
suits the dependency determined in SPAR-H method (theoretical comparison of
both dependency methods).Table 2
Comparison of dependency levels—all scenarios are presented.
PostinitiatorsPreinitiatorsIJS-HRASPAR-HIJS-HRASPAR-HCD1, HD3, HD5, MD7,CD1MD9, MD11, LD13, LD15CD1CD1, HD3HD2CD1, HD3, MD9, MD11HD2CD2, HD4HD4MD6, LD8, LD14, LD16MD4HD5, MD7HD6MD6, LD8, LD14, LD16LD6MD6, LD8MD8MD6, LD8, LD14, LD16HD8CD1, HD3MD10MD6, LD8, LD14, LD16MD10CD2, HD4LD12MD6, LD8, LD14, LD16LD12HD5, MD7HD14MD6, LD8ZD14MD6, LD8HD16MD6, LD8CD16CD1, CD2, HD3, HD4MD18MD6, LD8HD17HD5, MD7MD20MD6, LD8MD19MD6, LD8LD22MD6, LD8LD21CD1, CD2, HD3, HD4ZD24LD14, LD16ZD23HD5, MD6, MD7, LD8ZD25MD9, MD10, MD11, MD12, LD13, LD14, LD15, LD16CD1, CD2, HD3, HD4, HD5, MD6, MD7, LD8, MD9,ZD27MD10, MD11, MD12, LD13, LD14, LD15, LD16Table3 is the subset of Table 2. Table 3 focuses only to those scenarios (specific scenario
suits specific set of parameters), which suit real HFE considered in the
specific HRA (practical comparison of both dependency methods based on specific
PSA model). Both tables show that for specific HFE their respective HEP is
evaluated as a different value, if it is determined with one or the other
method.Table 3
Comparison of dependency levels—only scenarios, which are applicable for HFE of the specific PSA model.
PreinitiatorsPostinitiatorsIJS-HRASPAR-HIJS-HRASPAR-HLD12HD5CD1CD1ZD27MD7, LD13,
LD16MD8HD4, LD8HD17HD5ZD24LD14, LD16LD12LD8HD2CD2, MD12LD22MD6, LD8MD20LD8


## 3.2. Quantitative Comparison

64 HFEs exist in
the PSA model, which HEP is changed if HRA dependency method changes. Table4 shows a part of those HFE with identified dependency
levels and respective HEP for both methods IJS-HRA and SPAR-H. Terms CALC and
IND marked at preinitiators represent the calculation of final HEP as the
geometry average between the independent value of HEP for action at one train
and the respective dependent HEP assessed as low dependency (LD12) for similar
action at the other train.Table 4
Selected HFE with quantified HEP (for IJS-HRA and for SPAR-H).
Basic event IDDependency level IJS-HRAFinal HEP IJS-HRADependency level SPAR-HFinal HEP SPAR-HPRE_INI_01CALC, IND, LD121,91E-03HD55,00E-01PRE_INI_02CALC, IND, LD121,91E-03HD55,00E-01POST_INI_34ZD244,52E-03LD165,43E-02POST_INI_42MD81,71E-01LD88,08E-02POST_INI_53ZD241,58E-02LD146,50E-02POST_INI_63LD225,07E-02HD-4th-in-seq5,00E-01POST_INI_66HD25,16E-01MD121,70E-01POST_INI_69ZD241,04E-03LD145,10E-02POST_INI_79ZD241,96E-04MD-3th-in-seq1,43E-01POST_INI_83MD181,45E-01LD85,28E-02POST_INI_88MD201,43E-01LD85,06E-02POST_INI_102ZD242,91E-04HD-5th-in-seq5,00E-01Table5 shows the results of risk increase factor and risk decrease
factor of selected HFE calculated based on analysis runs with PSA model based
on IJS-HRA dependency and based on SPAR-H dependency considered. Selected HFE
in the table are those with RDF>1,05 and RIF>2,
which are a criteria for identification of risk significant events. The
differences between both cases are very large.Table 5
Results of importance of HFE.
IJS-HRAHFE identificationRDFHFE identificationRIFPOST_INI_421,13E+00POST_INI_042,26E+02POST_INI_631,09E+00POST_INI_127,46E+01POST_INI_881,09E+00POST_INI_1004,49E+01POST_INI_953,66E+01INI_012,34E+01INI_022,34E+01POST_INI_1022,23E+01POST_INI_021,75E+01POST_INI_346,73E+00POST_INI_353,19E+00POST_INI_692,68E+00POST_INI_632,62E+00POST_INI_602,01E+00SPAR-HHFE identificationRDFHFE identificationRIFPRE_INI_061,01E+01POST_INI_535,76E+00PRE_INI_058,18E+00POST_INI_045,63E+00POST_INI_1022,07E+00POST_INI_531,55E+00PRE_INI_091,51E+00PRE_INI_101,51E+00PRE_INI_041,40E+00PRE_INI_011,40E+00PRE_INI_021,38E+00PRE_INI_031,38E+00POST_INI_791,06E+00Table5 shows that identification of important HFE shows only
one HFE, which is identified as important in both analyses (POST_INI_04, which
deals with operator establishing auxiliary feedwater pumps). The difference
between both cases about the core damage frequency is very large, too. It differs
for more than one order of magnitude.Figure4 shows a comparison of fractional contribution of HFE
for both analyses. The figure shows that there are no comparable results:
events, which contribute significantly, if IJS-HRA dependency is considered,
can be insignificant, if SPAR-H dependency is considered and vice versa.Figure 4
Comparison of fractional contribution of HFE.Similarly, large
differences exist if instead of five levels of dependency less dependency
levels are determined with different equations for evaluation of dependency.


## 4. Conclusions

The methods for
dependency determination between human failure events within human reliability
analysis have been examined.Consideration of
human error probability of the first human failure event in a sequence as it is
and an increase of independent human error probability of the next human
failure event in a sequence  common to most of the HRA methods, except
IJS-HRA, which for relatively similar actions determines identical failure
probability based on geometry average.The methods for
determination of dependency between human failure events differ mostly in
definition of parameters, which impact the dependency, in their application and
in the determination of dependency level, which applies to a specific set of
parameters. All those distinctions are subjective. This subjectivism can lead
to a difference of several orders of magnitude in the results of HRA and in the
PSA, which includes HRA. This means significant differences in all PSA results
and their applications, for example,(i)
identification of key human failure events, which is an input
for prioritization of simulator training,(ii)
calculation of core damage frequency and its sensitivity to
changes, which is an input for risk-informed decision-making,(iii)
identification of different key tasks within human failure
event in order to identify the key parameters from HRA database. The subjectivism
could be minimized with integration and standardization of(i)
selection of parameters, which affects the dependency between
human actions, for example,
(a)
persons (e.g., one or more persons involved, e.g., same or
different people are performing the actions),(b)
similarity of actions (e.g., similar or not similar action),(c)
similarity of implementation of procedures (e.g., filling the
forms without signing the steps of the form or with signing the steps, e.g.,
same or different procedure for),(d)
similarity of locations (e.g., same or different location),(e)
timing (e.g., sequential performance or a larger time interval
between the actions),(f)
stress level (e.g., low, high, optional: moderate),(g)
complexity of actions (e.g., simple or complex actions, where
specific definition of simplicity or complexity are important),(ii)
the number of levels of dependency and the formulas for their
evaluation (e.g., five levels of dependency as in THERP, SPAR-H, and IJS-HRA
with their corresponding formulas).In addition, the
detailed guidelines are needed which would guide the application and which
would be highlighted with many practical examples. Database on the examples of
quantified human error probabilities for independent tasks, for dependent
tasks, and for complete human actions and their dependencies should become a
part of nuclear power plant probabilistic safety assessment database.



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*Source: 987165-2008-07-06.xml*