Human reliability is the probability that a person correctly performs some required activity by the system in a

required time period and performs no extraneous activity that can degrade the system. Human reliability

analysis (HRA) is the analysis, prediction and evaluation of work-oriented human performance using some

indices as human error likelihood and probability of task accomplishment. The human error concept must not

have connotation of guilt and punishment, having to be treated as a natural consequence, that emerges due to the not continuity between the human capacity and the system demand. The majority of the human error is a

consequence of the work situation and not of the responsibility lack of the worker. The anticipation and the

control of potentially adverse impacts of human action or interactions between the humans and the system are

integral parts of the process safety, where the factors that influence the human performance must be recognized and managed. The aim of this paper is to propose a methodology to evaluate the emergency evacuation process on industrial installations including SLIM-MAUD, a HRA first-generation method, and using virtual reality and simulation software to build and to simulate the chosen emergency scenes

Botelho, Felipe Mury

Carvalho, Paulo Victor Rodrigues de

Grecco, Cláudio Henrique dos Santos

Mól, Antônio Carlos Abreu

Oliveira, Mauro Vitor de

Santos, Issac José Antonio Luquetti

Carpe dIEN

2007 International Nuclear Atlantic Conference - INAC 2007
Santos, SP, Brazil, September 30  to October 5, 2007
ASSOCIAÇÃO BRASILEIRA DE ENERGIA NUCLEAR - ABEN
ISBN: 978-85-99141-02-1
HUMAN RELIABILITY ANALYSIS AS AN EVALUATION TOOL OF
THE EMERGENCY EVACUATION PROCESS ON INDUSTRIAL
INSTALLATION
 Isaac J. A. L. dos Santos1, Cláudio H. S. Grecco1, Antônio C. A. Mól1, Paulo V. R.
Carvalho1, Mauro V. Oliveira1, Felipe Mury Botelho1
1Instituto de Engenharia Nuclear (IEN / CNEN - RJ)
Caixa Postal 68550
21945-970 Rio de Janeiro, RJ
luquetti@ien.gov.br
grecco@ien.gov.br
mol@ien.gov.br
paulov@ien.gov.br
mvitor@ien.gov.br
felipemury@superig.com.br
ABSTRACT
Human reliability is the probability that a person correctly performs some required activity by the system in a
required time period and performs no extraneous activity that can degrade the system. Human reliability
analysis (HRA) is the analysis, prediction and evaluation of work-oriented human performance using some
indices as human error likelihood and probability of task accomplishment. The human error concept must not
have connotation of guilt and punishment, having to be treated as a natural consequence, that emerges due to the
not continuity between the human capacity and the system demand. The majority of the human error is a
consequence of the work situation and not of the responsibility lack of the worker. The anticipation and the
control of potentially adverse impacts of human action or interactions between the humans and the system are
integral parts of the process safety, where the factors that influence the human performance must be recognized
and managed. The aim of this paper is to propose a methodology to evaluate the emergency evacuation process
on industrial installations including SLIM-MAUD, a HRA first-generation method, and using virtual reality and
simulation software to build and to simulate the chosen emergency scenes.
1. INTRODUCTION
Human error is any member of a set of human actions that exceed some limit of acceptability.
It is an out-of-tolerance action, where the limits of acceptable performance are defined by the
system [1]. Human reliability is the analysis, prediction and evaluation of work-oriented
human performance in quantitative terms using some indices as human error likelihood and
probability of task accomplishment. HRA has three basic functions, namely the identification
of human errors, the prediction of their likelihood and reduction of their likelihood, if
required [2]. Human reliability can be useful in diagnosing the factors that lead to less than
desired human performance and can also be used to determine the increase in performance.
Quantitative HRA can be used in probabilistic risk assessments (PRA), where the resulting
probabilities of failure for task sequence are combined with probabilities of failure for the
equipment in a system fault tree or a system event tree. Human reliability considerations
occupy an important place in the engineering of complex systems. During the last decade it
has become self-evidence that to minimize the probability of failures, the human factor must
be taken into account. The dependability of people to make the correct decision and then take
INAC 2007, Santos, SP, Brazil.
the correct action at the correct time cannot be overlooked. The purpose of HRA is to render a
complete description of the human contribution to risk and to identify ways to reduce the risk.
HRA has its roots in the study of human performance.  The assessment of the human error
becomes a tool for identifying the factors that influence the human performance. These
factors are known by risk analysts as performance shaping factors (PSFs). PSFs include the
quality of the operations, inservice tests, procedures, the quality of the interfaces, training,
workload, stress, environment, etc [3]. When these factors adversely affect performance of a
given task, there is opportunity for improving performance by making the PSFs more
favorable.  Human performance is also influenced by whether personnel have to rely on
intuition, past experience, personal knowledge versus well-defined knowledge.
The major need in HRA is for quality data. Data from data base is available [1]. If properly
collected, data from experiments in simulators, from plant operations are very useful. The
general objective of HRA methods is to produce numerical data for use in probabilistic risk
assessment (PRA), bring about improvements in plant safety and improvements in plant
performance and availability. Some HRA methods are used in the quantification of
probability of failures for the human actions, such as THERP (technique for human error rate
prediction), SLIM-MAUD (success likelihood index method multiattribute utility
decomposition), HEART (Human error assessment and reduction technique) and HCR
(human cognitive reliability).
The aim of this paper is to propose a methodology to evaluate the human performance during
the emergency evacuation process of the workers on industrial installations. This
methodology includes a HRA first-generation method, SLIM-MAUD, and computational
tools, such as virtual reality and simulation software, to build and to simulate the chosen
emergency scenes.
2. THE SLIM-MAUD METHOD (SUCCESS LIKELIHOOD INDEX METHOD
MULTIATTRIBUTE UTILITY DECOMPOSITION)
The study of the human factors is a scientific discipline that involves the systematic
application of information relating to human characteristics and behavior to enhance the
performance of man-machine systems. The initial of studies about human error prediction has
come from the nuclear industry through the development of expert judgment techniques such
as SLIM-MAUD. The need for expert judgment techniques lies in the lack of human error
data. The SLIM-MUD technique is intended to be applied to tasks at any level of detail and
its development was prompted by lack of HEP (human error probability) data. Errors can be
quantified at various stage including tasks and sub-tasks. The premise of SLIM-MAUD is
that the probability of a human error associated with the task is a function of the PSFs
associated with the task. It assumes that experts can rate the PSFS and estimate the failure
rates. The SLIM uses ratings of the relative goodness of PSFs in a particular situation
weighed by their relative impact as determined by a panel of experts. The analysts provide
values employed as upper and lower bounds on failures rates for a particular task. The
method assumes that the log of success probability is equal to the experts judgment and two
empirically derived constants. Two tasks for which the probability of success are known are
required, and in order to estimate a HEP, success probabilities must be subtracted from 1.0.
INAC 2007, Santos, SP, Brazil.
Variants of the core SLIM procedure lie in the treatment of the rating and weighting data, and
how the elicitation of those data is performed. An extension of SLIM presents the use of
multi-attribute utility decomposition (SLIM-MAUD). Multi-attribute decision problems are
decision making situations in which the alternatives are described by their attributes (PSFs)
that cannot be optimized simultaneously. This technique provides a method for quantifying
how important each PSF is in comparison to the other PSFs [3].
3. THE COMPUTATIONAL TOOLS
Some computational tools will be used, such as s virtual reality and simulation software, to
build and to simulate the chosen emergency scenes.
3.1 Virtual Reality
CAD software have been developed for design of machines, systems, workplaces, etc,
utilizing design parameters derived from a real model of the product, such as, shape, size,
distance and position. 3D visualization technology was originally developed for viewing and
design CAD files. It uses 3D polygons so that the file size becomes small enough for its
transmission and manipulation. Although this simplification process discards some high level
design features, important geometric data are retained. It demonstrates the potential of 3D
visualization as an interface technology for human factors evaluation. Virtual prototype is a
simulation of an object with an adequate degree of realism that is comparable with the
physical functionality and the appearance of the real object [4]. Virtual design is a process
applied to the development of a product that uses virtual prototypes in selected process
phases. Typical simulated features are includes, such as visual appearance, shape, texture,
surface materials, colors, displays, lights and mechanical controls. Stereo vision is often
included in a virtual reality system. This is accomplished by creating two different images of
the world, one for each eye. The images are offset by the equivalent distance between the
eyes. The images can be projected through differently polarized filters, with corresponding
filters placed in front of the eyes and displayed sequentially on a conventional monitor or on
projection display.
3.2 Simulation Software
Simulex: It is a software tool capable of modeling the evacuation of large populations
through multi-storey buildings and the physical presence of each person as the person moves
through complex spaces. It produces more comprehensive analysis of emergency evacuation
times and highlights potential problem areas of the evacuation strategy. Human movement
parameters such as rates of body twist, acceleration/deceleration and speed have all been
collated during extensive tests. The tests demonstrated that Simulex accurately models
individual movement, and hence produces realistic results when the performance of group is
analyzed. The output of the model tracks the individual position throughout the evacuation.
Also, the occupants have an individual view of the building because the route choice can
consist of either the shortest route calculated by the default distance map or a user-defined
route obtained by assigning an alternate distance map to an individual or group of occupants.
INAC 2007, Santos, SP, Brazil.
Exodus: The purpose of this model is to simulate the evacuation of a large number of person
from a variety of enclosures. The model consists of six sub-models that interact with one
another to pass information about the evacuation simulation. They are the Occupant,
Movement, Behavior, Toxicity, Hazard and Geometry sub-models. The model views the
occupants as individuals by giving each occupant certain characteristics. The occupant sub-
model’s purpose is to describe information such as gender, age, maximum running speed,
maximum walking speed, response time, agility, patience, drive, etc. The sub-model also
maintains such information as the distance traveled by the occupant throughout the
simulation, the person’s locations, and exposure to toxic gases. Some of these attributes are
static, and some of these change with the conditions in the building.
Egress: The purpose of this model is to determine the evacuation of crowds in a variety of
situations, such as theaters, office buildings, railway stations, and ships. The floor plan of a
building is covered in cells that are equivalent in size to the minimum area occupied by an
occupant. Instead of being square, like most grid cells, the cell is hexagonal in shape. The
model views the occupants as individuals. The movements of each occupant are carefully
monitored throughout the simulation. Each individual also has certain goals and a specified
time period to complete that goal. The occupants’ perspective of the floor plan is also
individual. EGRESS contains a route finding algorithm that defines the shortest distance from
each cell on the floor plan to each specified region or exit. Then, the behavioral modeling
aids in choosing which objective the occupants moves toward.
Simwalk: It is a multi-purpose pedestrian simulator that allows to evaluate and to ensure the
security and comfort of walking in different environments. It models every pedestrian - up to
thousands of pedestrians - as single persons with their behavior and goals. Typical
applications of Simwalk are: security testing of egress routes of complex buildings, design
and walk-ability validation of public buildings and integration of pedestrian scenarios in
normal traffic simulation.
The figure 1 shows a simulation of an emergency scene using a computational tool.
INAC 2007, Santos, SP, Brazil.
Figure 1: The simulation of an emergency scene
4. THE EMERGENCY EVACUATION PROCESS
A key element of emergency planning is the evacuation planning. Protective action
recommendations (PAR) are recommendations made to decision makers in an emergency.
These recommendations generally involve three choices for workers action: do nothing,
shelter in place, or evacuate the area. The mobilization time represents the time required by
evacuees to perform all their necessary preparatory activities prior to starting the trip. Good
evacuation planning methodology involves an iterative process to identify the best evacuation
routes and to estimate the time required to evacuate the area at risk [5].
An industrial installation presents many risks in the form of process hazards, gas release,
radiation, fire and explosion. These events can occur at any time. The evacuation alarm
indicates to all personnel to start the evacuation sequence and gather at the temporary safe
refuge. The workers are required to stop the work and to return the process to a safe state.
The next step is to make their workplace safe so that the others workers will not be impeded
by obstructions. Once the temporary safe refuge is reached, all workers are required to
register their name on a tag. After, the tags and workers count are performed and all
personnel are required to remain in the temporary safe refuge. The key aspects to a successful
evacuation are influenced by the type of the human error and the severity of the event
initiator. The factors that define a successful evacuation are: early recognition, accurate
evaluation, quick access to egress route and efficient travel to temporary safe refuge [6]. The
emergency evacuation process concerns with the actions beginning at the time of event
initiation (Ti) and ending with the tasks performed in the temporary safe refuge (TSR), before
moving on to the abandonment phase. Each phase of the evacuation process has an associated
elapsed time, such as Ta, Tev, Teg and Tr. These times make up the total time of evacuation
(Tevac).
Tevac = Ta + Tev + Teg + Tr
INAC 2007, Santos, SP, Brazil.
The figure 2 shows the graphical representation of the phases of the evacuation sequence.
The first three phases (awareness, evaluation and egress) are the phases where workers have
the greatest exposure to the effects of the initiator event (e.g. heat, smoke, pressure) and to
high levels of physiological and psychological stress.
Figure 2. Graphical Representation of the Phases of a Evacuation Sequence (Adapted from
Dimattia [7] )
5. THE METHODOLOGICAL FRAMEWORK
The proposed methodological framework to evaluate the human reliability during the
emergency evacuation process on industrial installation, is shown in figure 2. The phases are:
• Phase 1: Establish criteria to choose and to rank the following judges: the principal team
(PT) and the elicitation team (ET).
Abandon
Initiating
Event
(Ti )
Time to evacuate (Tevac)
Awareness
Phase
(Ta)
Evaluation
Phase
(Tev)
Egress Phase
(Teg)
Recovery
Phase
(Tr)
Elevated Exposure
Phases (EEPs)
Risk increasing with time to evacuate
Evacuation
actions
INAC 2007, Santos, SP, Brazil.
• Phase 2: The PT defines the emergency evacuation scenarios.
• Phase 3: The PT develops the hierarchical task analysis for each evacuation scenario.
• Phase 4:  The PT develops a list of the most relevant PSFs through a pairwise
comparison.
• Phase 5: The PT develops the questionnaire for determining the PSFs weights and ratings.
• Phase 6: The ET determines the importance weights for each of the PSFs.
• Phase 7: The ET rates each action on the hierarchical task analysis through a numerical
scale.
• Phase 8: Perform statistical checks on the data from phase 6 and 7.
• Phase 9: Determination of human error probabilities through the SLIM-MAUD
procedure.
• Phase 10: Develop the risk consequence and risk mitigation for each muster scenario
The phases 6 and 7 are supported by the following resources: administrative and operating
procedures, video tapes of emergency training, analysis of workers performance during
emergency training, simulation using computational tools, such as virtual reality, simulex,
exodus, simwalk or egress software. Through these aid tools, the elicitation team (ET) can get
useful information and data that will help them to determine the importance weights for each
of the chosen PSF and to assign a value on the numerical scale.
4. CONCLUSIONS
In this paper we have presented a methodological framework to analyze the human reliability
during the emergency evacuation process of the workers of an industrial installation. Due to a
lack of human error databases for the emergency evacuation of the industrial installation, an
expert judgment technique, the SLIM-MAUD, will be used to predict human error
probabilities. The chosen of the simulated scenarios is based on the expert judgment, on the
expert experience, on the analysis of evacuation procedures, and on the observations of the
evacuations drills. The elicitation team (ET) determines the importance weights for each of
the PSFs and rates each action on the hierarchical task analysis. Computational tools may be
used by ET as an aid support to the elicitation of these data. The computational tools, such as
virtual reality, simwalk, exodus and simulex,  can be used to build and to simulate the chosen
scenarios. The data taken from the simulations may be used by the experts to improve the
elicitation of more PSF data.
The results of this research may be used in the identification of the areas where traditional
evacuation training does not adequately prepare workers to deal with high stress and difficult
INAC 2007, Santos, SP, Brazil.
environments. The results will provide a clear indication of situations where the risk is
highest, which actions can end in failure, which PSFs affects the performance of the workers
and increases the times related to  awareness, evaluation, egress and recovery phases.
REFERENCES
1. Swain, A. D.  and H. E. Guttmann, H.E. Handbook of Human Reliability Analysis with
Emphasis on Nuclear Power Plant applications, NUREG/CR-1278, U. S. Nuclear
Regulatory Commission (1983).
2.  Reason, J. Human Error. Cambridge, University Press (1990).
3. Embrey, D. E., Humphreys, P. C., Rosa, E. A., Kirwan, B. and Rea, K. SLIM-MAUD: an
Approach to Assessing Human Error Probabilities Using Structured Expert Judgment.
Report No. NUREG/CR-3518 (BNL-NUREG-51716). Department of Nuclear Energy,
Brookhaven National Laboratory, Upton, NY (1984).
4. Stanney, K. M. Handbook of Virtual Environments: Design, Implementation, and
Applications. Human Factors and Ergonomics, February 1 (2002).
5. Bellamy, L. J. The Influence of Human Factors Science on Safety in the Offshore Industry.
Journal of Loss Prevention in the Process Industries, 7(4), 370-375 (1994).
6. Lorenzo, D. K. A Guide to Reducing Human Errors, Improving Human Performance in
the Chemical Industry. The Chemical Manufacturers’ Association, Inc., Washington, DC,
(1990).
7. DiMattia, D.G. Human error probability index for offshore platform musters. Ph.D.
Thesis, Department of Chemical Engineering, Dalhousie University, Halifax, NS, Canada
(2004).


Human Reliability analysis as an evaluation tool of the emergency evacuation process on industrial installation

http://carpedien.ien.gov.br:8080/bitstream/ien/2483/1/HUMAN%20RELIABILITY%20ANALYSIS%20AS%20AN%20EVALUATION%20TOOL%20OF%20THE%20EMERGENCY%20EVACUATION%20PROCESS%20ON%20INDUSTRIAL%20INSTALLATION.pdf

Human Reliability analysis as an evaluation tool of the emergency evacuation process on industrial installation

Abstract

Similar works

Full text

Available Versions

Carpe dIEN