art jang control room operation sherpa-1

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Nuclear Engineering and Design 257 (2013) 7987

Contents lists available at SciVerse ScienceDirect

Nuclear Engineering and Design

journal homepage: www.elsevier .com/ locate /nucengdes

An empirical study on the basic human error probabilities for NPP advanced main

control room operation using soft control

InseokJang a, Ar Ryum Kim a, Mohamed Ali Salem Al Harbi b, SeungJun Lee c ,Hyun Gook Kang a, Poong Hyun Seonga,

a Department of Nuclear andQuantum Engineering, Korea Advanced Institute of Science andTechnology, 373-1,Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Koreab Department of Nuclear Engineering, Khalifa University of Science, Technology andResearch,P.O. Box127788, AbuDhabi,UnitedArab Emiratesc Integrated SafetyAssessment Division, KoreaAtomic EnergyResearch Institute, 150-1, Dukjin-dong, Yuseong-gu,Daejeon 305-353, Republic of Korea

h i g h l i g h t s

The operation environment ofMCRs in NPPs has changed by adopting new HSIs. The operation action in NPP Advanced MCRs is performed by soft control. Different basic human error probabilities (BHEPs) should be considered. BHEPs in a soft control operation environment are investigated empirically. This work will be helpful to verify ifsoft control has positive or negative effects.

a r t i c l e i n f o

Article history:

Received 2 November 2012

Received in revised form 22 January 2013

Accepted 23 January 2013

a b s t r a c t

By adopting new humansysteminterfaces that are based on computer-basedtechnologies, the operation

environment ofmain control rooms (MCRs) in nuclear power plants (NPPs) has changed. The MCRs that

include these digital and computer technologies, such as large display panels, computerized procedures,

soft controls, and so on, are called Advanced MCRs. Among the many features in Advanced MCRs, soft

controls are an important feature because the operation action in NPP Advanced MCRs is performed bysoft control. Using soft controls such as mouse control, touch screens, and so on, operators can select a

specific screen, then choose the controller, and finally manipulate the devices.

However, because of the different interfaces between soft control and hardwired conventional type

control, different basic human error probabilities (BHEPs) should be considered in the Human Reli-

ability Analysis (HRA) for advanced MCRs. Although there are many HRA methods to assess human

reliabilities, such as Technique for Human Error Rate Prediction (THERP), Accident Sequence Evaluation

Program (ASEP), Human Error Assessment and Reduction Technique (HEART), Human Event Repository

and Analysis (HERA), Nuclear Computerized Library for Assessing Reactor Reliability (NUCLARR), Cog-

nitive Reliability and Error Analysis Method (CREAM), and so on, these methods have been applied to

conventional MCRs, and they do not consider the new features ofadvance MCRs such as soft controls. As

a result, there isan insufficient database for assessing human reliabilities in advanced MCRs.

In this paper, BHEPs in a soft control operation environment are investigated empirically for BHEPs to

apply advanced MCRs. A soft control operation environment is constructed by using a compact nuclear

simulator (CNS), which is a mockup for advanced MCRs. Before the experiments, all tasks that should

be performed by subjects are analyzed using one ofthe task analysis methods, Systematic Human Error

Reduction and Prediction Approach (SHERPA). Human errors are then checked to analyze BHEPs, humanerror mode, and the cause ofhuman error when using soft control.

2013 Elsevier B.V. All rights reserved.

Corresponding author. Tel.: +8242 3503820; fax: +8242 3503810.

E-mail addresses:[email protected](I. Jang), [email protected]

(A.R. Kim), [email protected] (M.A.S.A. Harbi), [email protected] (S.J. Lee),

[email protected](H.G. Kang), [email protected](P.H. Seong).

1. Introduction

The assessment of what can go wrong with large scale systems

such as nuclear power plants is of considerable current interest,

given the past decades record of accidents attributable to human

error. Such assessments are formal and technically complex evalu-

ations of the potential risks of systems, and are called probabilistic

safety assessments (PSAs). A PRA today consider not just hardware

0029-5493/$ see front matter 2013 Elsevier B.V. All rights reserved.

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80 I. Jang et al. / Nuclear Engineering and Design257 (2013) 7987

failures and environmental events that can impact upon risk but

also human error contributions (Kirwan, 1992).

The importance of human reliability related problems in secur-

ing the safety of complex process systems has been clearly

demonstrated in recent decades. Unfortunately, although many

people have devoted efforts to clarifying why human performance

deviates from a certain expected level, there are several difficulties

in scrutinizing human reliability related problems. One critical dif-

ficulty is that the amount of available knowledge from operating

experiences is extremely small because of the infrequency of real

accidents (Park and Jung, 2005). Similarly, in a report on one of the

renowned HRA methods, Technique for Human Error Rate Predic-

tion (THERP), it is pointed out that The paucity of actual data on

human performance continues to be a major problem for estimat-

ingHEPs andperformancetimesin nuclear power plant (NPP) task

(Swain and Guttmann, 1983). Due to the lack of real accident data

in NPPs, data such as the results of experiments and fields studies,

experiments using artificial tasks, and simulation data have been

used as a database for Human Error Probability (HEP) estimation.

However, another critical difficulty is that most current HRA

databases deal with operation in conventional type of MCRs.

With the adoption of new humansystem interfaces that are

based on computer-based technologies, the operation environ-

ment of MCRs in NPPs has changed. The MCRs including these

digital and computer technologies, such as large display pan-

els, computerized procedures, soft controls, and so on, are called

advanced MCRs (Stubler et al., 2000). Because of thedifferent inter-

faces, different basic human error probabilities (BHEPs) should

be considered in human reliability analyses (HRAs) for advanced

MCRs. Although there are many HRA methods to assess human

reliabilities such as Technique for Human Error Rate Prediction

(THERP), Accident Sequence Evaluation Program (ASEP), Human

Error Assessmentand Reduction Technique (HEART), Human Event

Repository and Analysis (HERA), Nuclear Computerized Library for

Assessing Reactor Reliability (NUCLARR), and Cognitive Reliability

and Error Analysis Method (CREAM) (Swain and Guttmann, 1983;

Swain, 1987; Hallbert et al., 2006, 2007; Wendy and David, 1992;

Hollnagel, 1998), these methods have been applied to conventionalMCRs, and they do not consider the new features of advance MCRs

such as soft controls.

This study carries out an empirical analysis of human error

considering soft controls under an advanced MCRmockup called

compact nuclear simulator (CNS). The aim of this work is not only

to compile a database using the simulator for advanced MCRs but

also to compare BHEPs with those of a conventional MCRdatabase.

Moreover, human error modes and causes of human error when

using soft control are also identified.

2. Soft control

2.1. Definition and general characteristics of soft control

In NUREG-CR/6635, soft controls are defined as devices having

connections with control and display system that are mediated by

softwarerather thanphysicalconnections (Stubler et al.,2000). This

definition directly reflects the characteristics of advanced MCRs,

including that the operator does not need to provide control input

through hard-wired, spatially dedicated control devices that have

fixed functions. Because of this characteristic, the function of soft

control may be variable and context dependent rather than stat-

ically defined. Also, devices may be located virtually rather than

spatiallydedicated.That is, personnelmay beabletoaccessapartic-

ular soft control from multiple places within a display soft control

from multiple places within a display system (Stubler et al., 2000;

Lee et al., 2011).

General characteristics of soft controls are as follows: multiple

locations for access, serial access, present and available, physical

decoupling of input and display interfaces, interface management

control, multiple modes, software-defined functions, and interface

system(Stubler et al., 2000;Lee et al., 2011). Based on these charac-

teristics and functions of soft control, human error maybe reduced

orthesechanges mayconverselyincrease human errordue to inter-

face management complexity.

2.2. Task analysis for soft control

A task analysis helps the analyst to understand and represent

human and system performance in a particular task and sce-

nario. A task analysis involves identifying tasks, collecting task

data, analyzing the data so that tasks are understood, and pro-

ducing a documented representation of the analyzed tasks. Typical

task analysis methods are used for understanding the required

humanmachine and humanhuman interactions and breaking

down tasks or scenarios into component task steps or physical

operations. A task analysis can be defined as the study of what the

operator (or a team of operators) is required to do (i.e., the oper-

ators actions and cognitive processes) in order to achieve system

goals (Jang et al., 2012.).

In new operation environment, advanced MCR, the operation

actions of operators are divided into primary tasks (e.g., providing

control inputs to plant systems) or secondary tasks (e.g., manip-

ulating the user interface to access information or controls or to

change control modes). Interface management tasks are referred

to as secondary tasks because they are concerned with control-

ling the interface rather than the plant. Operators should perform

secondary tasks to find appropriate screens or devices by screen

navigations and screen selections before they perform the primary

task to control a device. Human errors of primary tasks may result

in the execution of inappropriate control actions. Human errors

involving secondary tasks are likely to cause delays in accessing

controls and displays, to disorient the operator within the display

system, or to select wrong controls and displays. While conven-

tionalMCRsdo nothavesecondarytasks,the secondarytasks ofsoftcontrol take a relatively large portion. Therefore, not only human

error for primary tasks but also that for secondary tasks should be

analyzed in soft controls (Lee et al., 2011).

During the HRA process, a task analysis should be implemented

in advance. In this study, a task analysis of soft control is performed

based on the Emergency Operating Procedure (EOP) considering

the features of soft control such as navigation tasks, interface man-

agement tasks, and so on. There are two kinds of tasks in EOP when

using soft control, control of non-safety related functions and con-

trol of safety related functions. All tasks including primary and

secondary tasks are analyzed using a task analysis method, Sys-

tematic Human Error Reductionand PredictionApproach (SHERPA)

(Lee et al., 2011; Embry, 1986).

As an example, Fig. 1 shows a task analysis using SHERPA. Thegoal of the task is to reset the safety injection and auxiliary feed-

water actuation signal. In order to achieve the goal, the operator

selects Reactivity system screen from the operator console (sec-

ondary task) and resets the safety injection signal (primary task).

For reset of the safety injection signal, there are other subtasks:

Press bypass button from the operator console (secondary task),

Press the acknowledgebutton (secondarytask), and finally Press

bypass button using the input device for the safety component

(primary task). Another subtask, Reset the auxiliary feedwater

actuation signal, performed to reset the safety injection signal, is

then analyzed.

The unit tasks can be rearranged as shown in Fig. 2. Each unit

task is included in one of four steps: operation selection, screen

selection, control device selection, and operation execution. While


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I. Jang et al. / Nuclear Engineering and Design257 (2013) 7987 81

Fig. 1. Task analysis using SHERPA.

some tasks are similar to those of conventional controls, there areunique features of soft control such as the screen selection step,

and some steps, such as control device selection and operation

execution steps, have different unit tasks from those of conven-

tional controls. The operation process of soft controls can vary

according to the interface features of advanced MCRs (Lee et al.,

2011).

Once the task analysis is completed, all of the unit tasks are cat-

egorized as diagnosis tasks and execution tasks. Based on a review

of first and second generation HRA methods, there are two general

task categories: execution and diagnosis. Examples of execution

tasks include operating equipment, performing line-ups, starting

pumps, conducting calibration or testing, and other activities per-

formed during the course of following plant procedures or work

orders. Diagnosis tasks consist of reliance on knowledge and expe-

rience to understand existing conditions, planning and prioritizing

activities, and determining appropriate courses of action (Gertman

et al., 2005). BHEPs for the classified two tasks are estimated by an

empirical simulation study in this work.

3. Experiment in simulation environment

3.1. Compact nuclear simulator (CNS)

As the name indicates, this simulator is compact and is not a

full scope simulator. The reference plant of this simulator is Kori 3

Nuclear Power Unit in Korea, which is a Westinghouse 3 Loop PWR

plant. As shown in Fig. 3, the interface of CNS is fully digitalized to

make the experimental environment similar to an advanced MCR.

The thermal hydraulic part is from SMABRE code, which was

developed in Finland, and one group diffusion equation is used for

flux calculations. This is linked to the PWRcode model and special

routines forthe steamflow, theturbine, thecondenser,and thecon-

densate and feed-water systems. The chemical and volume control

system and the protection system are also described. In addition,

there are some peripheral parts such as the electrical system, the

containment, and the pressure relief tank.

Fig. 4 shows 7 subsystems (Reactor Coolant System, Residual

Heat Removal System, Main Steam/Turbine System, Feedwater

Fig. 2. Sequence analysis of a soft control task.


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Fig. 3. Compact nuclear simulator.

System, Electrical System, Chemical and Volume Control System,

andCondenser System) of the CNS. Other smaller subsystems, suchas the SG Level control system, are not shown separately. Using

this simulator, 79 malfunctions, which are briefly summarized in

Table 1, can be provided.

3.2. Experiment

In order to measure human error rate in an emergency situa-

tion,experiments with21 students majoring in nuclear engineering

majors are performed under a Steam Generator Tube Rupture

(SGTR) accident scenario. The number of human errors is checked

in a prepared checklistcreated from thetaskanalysisand BHEPs are

calculated based on the number of errors divided by the number of

opportunities.

3.2.1. Procedure

The specific experimental procedure of the simulation is based

on the SGTR scenario. The CNS provides an accident that requires

cognitive action in order to be solved. The subjects then try to find

the causes of the accident by responding symptoms of the accident

and they know that the accident is SGTR and finally attempt to sta-

bilize the plant. During the accident, the subjects respond to the

alarm signals, plant parameters, and so on. In order to familiarize

the subjects with the CNS and how to control the devices, train-

ing of each subject is performed. Training procedures are divided

into two steps. First, the subjects are trained academically by being

educated about Emergency Operating Procedures (EOPs) such as

EOP development after TMI accidents, general structure of EOP,event andsymptom-basedapproach, useof EOP, andso on.Second,

practical training is performed by using compact nuclear simulator

(CNS) used in this experiment. After both academic and practice

Fig. 4. Seven subsystems of the CNS.


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EOP Operators action/task Detailed operators action/task Error check CauseE-3 Check RCP whether or not it should be stopped

Charging Pump : at least 1 of them is operating

RCS pressure : lower than 97 kg/cm2

E-3 Inspect ruptured S/GIs thereunexpected increase of narrowrange level of certainS/G

E-3 Isolate let-down flow from ruptured S/G

Regulate controller set point ofruptured S/G PORV to 79.1kg/cm2

Check whether rupturedS/G PORV is closed or not HV-108HV-208HV-308

Close steam distribution valve which supply flow to turbine

driven

HV-313

HV-314

HV-315Check blow-down isolation valve of ruptured S/G whether or

not it should be closedHV-304

Close MSIV and bypass valve of ruptured S/G

HV-108

HV-208

HV-308

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

E-3 Reset SI and AUX FW Actuation Signal

Reset SI Signal

SelectReactivitysystem from the operator

console

PressBypassfromthe operator consolePress the Acknowledge button

PressBypassbuttonusing the input device forthe safety component

Reset AUX FW Actuation Signal

PressResetsafeguardfrom the operator console

Press the Acknowledge button

PressResetbuttonusing the input device for thesafety component

.

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.

Fig. 5. The error checklist from task analysis.

However, if he/she does not recognize that the selection is wrong

and continues executing the operation on the wrong object, then

a wrong operation will be performed. Wrong operation: even though an operator performed appropri-

atenavigations of screens andright selection of the target device,

an operator can perform a wrong operation such as pressing

OPEN button instead of CLOSE button. Mode confusion: if a control window includes multimode, an

operator could perform an operation on the wrong mode. An

operator can make a mistake such as increasing the level of pres-

surizer instead of its pressure in case that the level and pressure

of the pressurizer are controlled in the same control panel with

mode switch. Inadequate operation: when an operator executes the right oper-

ation after appropriate navigations of screens and right selection

of the target device, the operation could be executed insuf-

ficiently, too early or too long/short. All operations that are

performed incompletely Delayed operation: due to the wrong selections of screens or

devices and recovery of them, an operation could not be per-

formed at the righttime. Additional timefor reselection of screens

or devices could be oneof the reasons forsuch delayed operation.

If human errormodesexcluding thedefined human error modes

were found in theexperiment results, thehumanerrormodeswere

modified by adding new modes.

4.2. Basic human error probabilities

4.2.1. Statistical results

According to the experiment procedure, the number of errors

made by subjects was recorded in the prepared checklists. Fig. 6

showsthe total number of errors regardingdiagnosisand execution

by each subject. While there were several subjects who did not

make anyerrors, oneof thesubjectsmade 10 errors. This difference

might be caused by personal Performance Shaping Factors (PSFs).

Fig. 6. Number of human errors.

However, in this study, PSFs were not investigated, because BHEPs

according to human error modes should be determined in advance

in the HRA process and then PSFs should be applied to the BHEPs

for the final modified HEP.

Human errors that occurred were classified using the human

error modes defined in Section 4.1. Fig. 7 shows the number of

human errors according to human error modes for the execution

error, and the diagnosis error mode is added independently. Also,

BHEPs for each error mode were calculated, as shown in Fig. 8.

Fig. 7. Number of humanerrors accordingto error modes.


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Table 3

Number of humanerrors and probabilities of human errorsaccording to error modes.

Errors/opportunities Probabilities of human errors 95% confidence limits Error factors

E1 (operation omission) 5/1281 0.0039 0.00050.0073 3.87

E2 (wrong object) 11/756 0.01455 0.00600.0231 1.96

E3 (wrong operation) 5/441 0.01134 0.00150.0213 3.82

E4 (mode confusion) 1/42 0.02381

E5 (inadequate operation) 12/504 0.02381 0.01050.0371 1.88

E6 (delayed operation) 71/504 0.01389 0.00370.0241 2.56

Diagnosis error 9/360 0.0143 0.00500.0236 2.17

The number of human errors/opportunities, probabilities of human

errors, 95%confidence limits, and error factors according to human

error modes are tabulated in Table 3. In case of mode confusion

(E4), statistical analysis wasnot carried outbecausea small number

of opportunities do not guarantee statistical results such as con-

fidence limits, and error factors calculation. Among the BHEPs in

execution error mode, inadequate operation (E5) and mode confu-

sion showed the highest error probability but because the number

of tasks that related to mode confusion (E4) was relatively smaller

than other tasks it was hard to conclude that probability of E5 and

E4 were the same due to different sample and calculated error

factors.

Several BHEPs were compared with established HEP data in theform of THERP tables. In the case of omission error in THERP Table

20-7, shownin Table4, HEPis0.01withanerrorfactorof3whilethe

probabilityof operationomissionin thisstudy is 0.0039 witha simi-

larerrorfactor of 3.87. This comparison implies that thesoft control

environment reduces human error regarding operation omission.

In thecase of selectionerrors in THERP Table 20-9 shown in Table 5,

HEP is either 0.001 or 0.003 with an error factor of 3 while the

probability of a wrong object in this study is 0.01455 with an error

factor of 1.96. After the Error Factor (EF) in the THERP table is con-

sidered, comparing calculated error factorin this study, HEPs in the

results of this study is similar to HEP from THERP table. In the case

of failure of administrative control in THERP Table 20-6, shown in

Table 6, HEPis 0.005 with an error factorof 10 while theprobability

of wrong operation in this study is 0.01134 with an error factor of3.82. Because two values of uncertainty bounds (error factors) are

considerably different, it was difficult to compare the result in this

study with THERP table when soft control was used for probability

of wrong operation.

4.2.2. Human errors observed in this experiment

After the calculation of BHEPs, the performance checklists and

the recorded operators screens were analyzed again to verify the

detailed reasons for human errors. Examples of human errors in

this study are given below.

As an example of the operation omission (E1), there was one

step in the SGTR procedure where subjects have to line up the

Fig. 8. Basic human error probabilities (BHEPs) according to error modes.

following valves: LV-161 open, LV-615 close, LV-459 open, and

HV-1, 2, 3 open. However, several subjects failed to line up one

of the valves in the procedures. Regarding the human error mode

of wrong object (E2), one subject operated the wrong controller.

The subject should have performed the following step in the pro-

cedure: Turn on pressurizer heaterto saturate cooling water at the

pressure of ruptured S/G. In order to perform this step, the sub-

ject had to select the BACKUP HEATER on the operators console,

as shown in Fig. 9a. However, the subject selected the wrong con-

troller, as shown in Fig. 9b. In the case of the human error mode

of wrong operation (E3), the subject stopped all Reactor Coolant

Pumps(RCPs), asshownin Fig.10, althoughRCP1and3shouldhave

been stopped as explainedin theinstructions. Also, there wasa case

Fig. 9. (a) Screenshot regarding right operation on right object. (b) Screenshot

regardingright operation on wrong object.


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Table 4

THERPtable 20-7: estimatedprobabilitiesof errorsof omission per item of instruction when useof written procedures is specified.

Omission of item: HEP Error factor

When procedures with checkoff

provisions are correctly used

Short list 10 items 0.003 3

When procedures without checkoff provisions are

used, or when checkoff provisions are incorrectly used

Short list 10 items 0.01 3

When written procedures are available and should be used but are not used 0.05 5

Table 5

THERP table 20-9: estimated probabilities of errors in selecting annunciated displays for quantitative or qualitative readings.

Selection of wrong display HEP EF

When it is dissimilar to adjacent displays Negligible

From similar-appearing displays when they areon a panelwith clearly drawn mimic lines that include the displays 0.0005 10

From similar-appearing displays that are part of well-delineated functional group on a panel 0.001 3

From an array of similar-appearing displays identified by labels only 0.003 3

Table 6

THERP table 20-6: estimated HEP related to failure of administrative control.

Task HEP EF

Carry out a plant policy of scheduled tasks such as periodic tests or

maintenance performed weekly, monthly, of at longer intervals

0.01 5

Initiated a scheduled shiftly checking or inspection function 0.001 3Use written operation procedures under normal operation procedure 0.01 3

Use written operation procedures under abnormal operation procedure 0.005 10

Use a valve change or restoration list 0.01 3

Use written test or calibration procedures 0.5 5

Use written maintenance procedures 0.3 5

Use checklist properly 0.5 5

where the subject operated a controller in automatic mode when

the controller should have been operated in manual mode; this is

mode confusion (E4). As a human error mode of inadequate oper-

ation, a subject performed a task incompletely. There was a task

where the subject had to reset the Safety Injection (SI) and actu-

atedauxiliary feedwatersignal in theprocedure. For thecompletion

of this task, the subject should follow these steps: 1. Select Reac-

tivity Control System from the operator console, 2. Press bypassfrom the operator console, 3. Press acknowledge button, 4. Press

bypass button using the input device for the safety component, 5.

Pressreset safeguard fromthe operator console, 6. Pressacknowl-

edge button, 7. Press reset button using the input device for the

safety component. In this task, several subjects did not perform

steps 24, which resulted in incomplete operation. Fig. 11 shows

Fig. 10. Screenshot regarding wrong operation.

complete and incompleteoperation procedures, respectively. Next,

several diagnosis errors were found.

5. Discussion

The results from this research show BHEPs according to defined

human error modes when soft control is used in advanced MCRs.

In order to investigate BHEPs, a mockup facility, CNS, was set up tosimulate an advanced MCRwith a focus on soft control. Using CNS,

21 subjects participated in an experiment dealing with a SGTR.

Recently, although numerous studies have proven the effects

of PSFs on final HEPs, this research did not consider either exter-

nal (e.g. darkness, high temperature, excessive humidity, and high

work requirement) or internal PSFs (e.g. high stress, excessive

fatigue, deficiencies in knowledge, skills and experience, etc.). In

order to overcome this problem, the instructor tried to ensure a

consistent and unbiased environment during the experiments and

educated the subjects before the experiments in an effort to min-

imize the effects of the external and internal PSFs. By maintaining

consistent conditions, BHEPs not considering PSFs were extracted.

However, as shown in Fig. 6, it was thought that internal PSFs may

have affected subjects 18 and 20. These two subjects caused theBHEP to be higher. Because the database of BHEPs are values where

PSFs are not applied, PSFs in advanced MCRs should be studied and

final HEPs based on BHEPs (the results of this study) and PSFs (the

results of further studies) should be calculated together in future

work.

Another aspect that should be discussed is the number of sub-

jects and the number of opportunities for E4 in this experiment.

This study focused on the effects of soft control on human perfor-

mance as a starting point and pilot test. More experiments by the

authors and other researchers should be performed with a large

number of subjects, various scenarios that could extract or induce

various error modes (especially E4 in this study), and even normal

conditions so that BHEPs based on experimentalstudywill be more

concrete and reliable.


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Fig. 11. Screenshot regarding inadequate operation.

6. Conclusion

This paper investigated basic human errorprobabilities (BHEPs)

according to human error modes in a soft control operation envi-

ronment empirically, because soft control is one of the most

important features in advanced MCRs and there is no BHEP

database related to the use of soft control in the HRA method. As

a mockup facility, a compact nuclear simulator (CNS) that includes

features of soft control and advanced MCRs such as large display

panels and computer technologies was used to simulate advanced

MCRs.

In order to extract the BHEPs, a task analysis for soft control,

which yielded an error checklist, was completed. From the soft

control task analysis it was revealed that secondary tasks that arerelated to manipulating the user interface to access information or

controls or to change control modes are new kinds of tasks that

do not exist in conventional MCRs. The errors made by 21 subjects

were then checked on error check lists, classifying human error

modes during the accident scenario. Using the results of the error

checklists, several statistical and graphical analyses were imple-

mented, such as the number of human errorsaccording to subjects,

the number of human errors according to human error modes, and

BHEP according to human error modes. Moreover,BHEPsusing soft

control were compared with various THERP tables to investigate

the level of human error reduction when using soft control. These

comparisons implied that the soft control environment reduces

human error related to operationomission, buttherewas no signif-

icant effect on error regarding wrong operation and wrong object.

Also, human errors observed in this experiment were investigated,

and this might provide insight to modify soft control design or

to prepare efficient operator training methods dealing with soft

control.

This empirical study will be helpful to verify whether soft con-

trol haspositiveor negative effects on human performance andwill

be able to provide modified BHEPs for advanced MCRs to various

HRA methods if more data are collected continuously.

Acknowledgement

This research was supported by a Nuclear Research & Devel-

opment Program of the National Research Foundation (NRF) grant

funded by the Korean government (MEST) (grant code: 2012-

011506).

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