Managing Risk Dormancy in MultiTeam Work Application of Time Dependent Success and Safety Assurance Methodology

Managing Risk Dormancy in Multi-Team Work: Application of
Time-Dependent Success-and-Safety Assurance Methodology
Farag Emad, Ingman Dov, Suhir Ephraim
E-mail:uj326@iec.co.il; Tel:972-52-3995774 ; Fax: 972-4-8183683
Faculty of Industrial Engineering and Management, Technion – Israel Institute of
Technology, Haifa 32000, Israel
Abstract
The success and safety of many to-day’s industrial activities, such as, e.g., constructing power
plants, transmission lines, and civil engineering objects, is often influenced by situations, when
successful and safe work completion is associated with the implementation of various more or less
complex multi-team layouts. Multi-team effort is characterized by the presence and interaction of
numerous, not necessarily concurrent time-and-space constrained interfering activities, as well as
by dormant risks. Such risks might threaten the fulfillment of the general task carried out by the
main team and/or by other teams on the construction site. This analysis addresses the role of the
dormant risks during the fulfillment of a non-simultaneous multi-team work. The objective of the
analysis is to suggest an effective and physically meaningful probabilistic predictive model. The
model is aimed at the understanding, quantification and effective managing the dynamics of the
system of interest. The emphasis is on the role of possible dormancies. The study is an extension
of the authors' earlier research on spatial and time dimensions in the addressed problem. The study
extends the risk management approach to a holistic level.
1.Introduction
Modern infrastructure and industrial activities are typically executed by several different on-site
teams: Sasoua, K., and Reason, J., (1999) "most human work is performed by teams rather than
by individuals". Each team performs distinct and designated activities over time. At the same
time the today’s technologies require better understanding and increasingly higher quality than
in the past. Sophisticated work methods, tools, and equipment have been developed for, and
became available to, the workers in multi-team systems. Such methods are crucial to achieve the
optimum and safe outcome of a particular project. Tight schedules are implemented today to
meet customers' requirements, and to ensure the demands for increased productivity. This
imposes additional pressure on workers. Handling of hazardous materials and energy sources,
such as electricity or radiation, requires the use of highly qualified labor, as well as customized

safety procedures and equipment. Dynamic physical conditions, such as noise, vibrations and
working at heights, contribute also to the likelihood of hazardous situations.
Teams often operate independently, with no explicit functional linkage between them. In other
cases, more or less close professional collaboration is required to perform a particular task. For
example, repair work in an electrical utility typically requires involvement of several teams.
One team of electricians disconnects the power, another team repairs the damage, and a third
team is deployed outside the secluded site, often on a standby basis, to assist, if necessary, the
workers inside the worksite. In this example three different teams perform various aspects of the
work aimed at a particular mutual goal. A mistake, error or a failure in one team's actions
affects other teams involved in sequential large-scale activity, and has a potential to create a
hazard. The hazard might remain dormant for some time, but eventually can become or generate
a risk. This risk might have an immediate impact on the team member who caused it, and/or
threaten another team continuing the job at the given site. This scenario can be identified as risk
dormancy (RD). When occurring in a multi-team situation, it becomes a multi-team risk
dormancy (MTRD). It is this type of risk dormancy that is the main concern and the main
subject of this analysis.
Several researchers have addressed the MTRD lately. Mitropoulos, P., Howell, G. A., and
Abdelhamid, S.T., (2005) stated that “errors by one crew may create unpredictable conditions
for a following crew” and suggested that "future research should focus on better understanding
the effect of task unpredictability and on developing error management strategies". Reason,
J.T., (2000) wrote: “Different actors’ decisions and actions can produce latent conditions or
pathogens in a system. These might lie dormant for a time until they combine with local
circumstances and active failure and penetrate the system's many layers of defenses, and an
accident occurs.” Despite the recognized importance of the MTRD, there are no studies that
suggest effective solutions to the MTRD problems.
The existing studies suggesting various safety models employ quite a few of diverse
approaches. Some models focus on actions or processes and examine the time and space of the
occurred accidents that led to personal injury or to damage to some assets. These are s.c. active
failures. Other models are system oriented, such, e.g., organization models related to the
management policy, actions and decision making Rasmussen, J., Pejtersen, A. M., and
Goodstein, L. P. p-149 (1994). Reason, J.T., (1997), Interdisciplinary models La Coze, J.,
(2005) "focus on cause-effect relationships close in time and space to the accident sequences".
Akinci, B., Fischen M., Levitt R.; and Carlson R., (2002) examined the time-space management

aspect of accident prevention. He indicated that "lack of management of activity space
requirements during planning and scheduling results in time-space conflicts in which an
activity’s space requirements interfere with another activity’s space requirements or work-in-
place." Rosenfeld, Y., Rozenfeld, O., Sacks, R., and Baum, H., (2006). Rozenfeld, O., Sacks,
R., and Rosenfeld, Y., (2009) addressed this situation by expanding the safety model to MT
problems that involve mutual risk exposure of two or more teams sharing the same time–space
domain.
In the analysis that follows a rather general holistic approach is used to address dormant risks
encountered during consecutive MT work activities. This approach treats the problems of
interest from a rather general point of view, regardless of a specific nature of a particular risk or
a possible outcome. Our approach provides a probabilistic assessment of the management's
dynamic response at the organizational level. Here are several typical examples.
Example 1: A scaffold is required for certain tasks performed on a public utility construction
site by teams working at heights, such as, say, plasterers and painters Figure (1).
Scaffold builder team
Figure 1a Prior of lean scheduling
Figure 1(b) Lean Scheduling Time-Space Dependent Model (Rozenfeld, 2009)
Project Schedule
Project Schedule
Plumber
Bricklayer team,
Plasterer team
team
Plumber
Scaffold builder team
Bricklayer team,
Plasterer team
Figure 1 Illustration of the scaffold example
Painter team
Painter team

A scaffold building team erects the scaffold, while a plumbing team is scheduled to
subsequently lay sewer pipes. In the meantime the scaffold is being used by other teams. In
such a situation, the time-space sharing is implemented as illustrated in Figure (1a). In the
safety assessment of the project in question risk exposure in team activities performed with
time-space overlapping is not considered. All the teams—the plasterers, the painters and the
plumbers—worked on the site simultaneously. The Lean Scheduling Time and Space
Dependent Model Rozenfeld, O., Sacks, R., and Rosenfeld, Y., (2009) illustrate the time
segregation.
This means that the plumbing team's work is rescheduled to avoid the risk of dealing with
falling objects or tools from the plastering or bricklaying teams. In space segregation, on the
other hand, the plumbers would be assigned to work at the other side of the site, where no teams
would be working above them. The risk posed by the scaffolding team is eliminated, as
illustrated in Figure (1b). The planner's instructions indicate, however, that the plumbing team
must lay the pipes in trenches at the foot of the building, where the scaffold is assembled.
Although the plastering team could be temporarily repositioned and the excavation could be
rescheduled, this still might destabilize the scaffold and increase the probability of its collapse
at a later time Figure (2). Rozenfeld's time-space dependent model does not address this aspect
of scaffold destabilization risk. The risk leads to an additional risk, namely, to the risk
dormancy. In this example, other scaffold users, such as the painting team, are exposed to an
underestimated risk, which, however, has been identified beforehand. This example illustrates
the risk dormancy (RD) phenomenon, i.e., a risk that is dormant, while awaiting for other teams
Figure 2 RD Exposure in MT Example- Risk of Scaffold Collapse, threat to painter team
Project schedule
Excavation for sewer
Plastering team
Scaffold building Painting
team
Underestimation of the identified RD in MT work
or misidentification of RD

that might use the hazardous scaffold. Fig. 2 emphasizes the progress that could be achieved by
developing tools and methods to deal with such underestimated or misidentified risks that stem
from the dynamic changing of the site (in this case, the excavation).
Example 2: Power grid works are inherently created serially. A utility lineman team replaces an
insulator on a high-voltage power line after obtaining permission from the responsible
electrician. The electrician operates according to a written checklist issued by the engineering
department. The professional teams work in a serial manner with respect to both time and space:
first, the engineering department writes the instruction checklist; then the responsible electrician
shuts down the power at a circuit box mounted near the work site (along the power line) and
linemen replace the insulator further down the line (not even necessarily at the same site). In
such situations several teams are active at the site, and communication and coordination of their
interactions might be quite complex. Any error in these activities could create RD, thereby
increasing the likelihood of an electrical shock accident. Examples of this type of error are
failure to check for current, misidentification of the appropriate line connector, and/or the
specification of the wrong transformer or pole number.
The above examples (scaffold collapse or electrician's error) represent dormant risks caused by
MT activities that have no time or space overlapping. This means that accidents caused by the
MTRD activities regardless of their simultaneity and space have not been addressed. MT
activities create dynamic work sites (environments) with constant changes depending on the
needs for the complex system, in/for which the work is being performed. Such complex
systems require a tight control, i.e., appropriate risk management, which should be the main
component of a safety management system (SMS).
The term “risk management” includes the notion of mitigating risks to an adequate and
achievable level acceptable by the organization. This can be done by the application of two
major methods:
1) Appropriate and effective risk control and
2) The use of the most suitable accident causation models.
The obvious challenge in the assurance of the occupational safety is the development of the
ability to effectively control problematic situations, identify hazards and assess and control
risks. The actual down-to-earth and practical on-site work is more complicated, however, than
the models that attempt to predict and simulate the effort. Proactive approaches, such as risk
assessment and control, can never be entirely accurate, complete, and successful when applied

alone. Reason, J.T., (2000) "Effective risk management depends crucially on establishing a
reporting culture. Without a detailed analysis of mishaps, incidents, near misses, and 'free
lessons,' we have no way of uncovering recurrent error traps or of knowing where the edge is
until we fall over it". Risk management should be therefore implemented both proactively and
reactively, and its complete success necessitates reactive approaches such as accident
investigation/causation. A description of the process of investigating accidents in the context of
the concept presented in this paper, namely, the underlying cause - risk dormancy (RD), is set
forth below.
The general strategy of pursuing risk management approach in the problem in question includes
the following major items:
Hazard identification: Before quantifying the probability of failure, hazards related to the
system operation must be identified. Several identification techniques are available Carter, G.,
and Smith, S., some of which are based on brainstorming among people familiar with the
installations at a work site, while other techniques are of a more systematic nature. According to
Cuny, X., and Lejeune, M. (2003), recognizing hazards can be a rather complicated task. Many
different kinds of uncertainty factors contribute to the challenge of recognizing hazards and a
clear-cut determination of the source and level of the risk for each hazard is next-to-impossible.
Furthermore, many observations are needed to accurately estimate the likelihood of an accident,
particularly, if it is of rare occurrence. One of the paradigms in the Accident Root Causes
Tracing Model Abdelhamid, S. T., and Everett, J. G. (2000) reveals that workers, more often
than not, fail to identify hazardous situations. This leads to the conclusion that the hazard
identification process only partially covers the hazards that should be identified on site. Multi-
team hazards (MTH) are even more difficult to identify.
Risk assessment: Hazards become a problem only when they could possibly result in an
accident whose occurrence is preceded by a sequence of events that may cause a hazardous
situation. After a hazard is identified, all possible sequences of events that can be triggered by
that hazard must be studied and checked to determine whether or not they might lead to an
accident. With the relevant scenarios in hand, it is possible to calculate the two elements of a
risk: the probability of the events occurring, and their consequences. Reason, J.T., (1997)
presents three models for safety management: the Pearson model, the Engineering model, and
the Organizational model. Each of these models has a different perspective on human error.
“Workplaces and organizations are easier to manage than the minds of individual workers. You
cannot change the human condition, but you can change the conditions under which people

work. In short, the solutions to most human performance problems are technical rather than
psychological.” This concept can be better understood by considering the work of Papadopoulos
et al., who concluded that "risk assessment must be conducted for each task and for each
worker. This risk assessment must consider all hazards and their interactions and must be
revised when changes occur". In addition they wrote: "However, frequent changes regarding
workforce, working hours and working conditions, as well as time pressure, result in
insufficient time for conducting a complete and effective risk assessment, determining training
needs, setting up, applying and monitoring the corresponding OSH measures. Furthermore, the
methodological tools used in risk assessment up to now are not sufficient for this complex
situation". In an earlier paper on this subject, Drivas and Papadopoulos (2004) pointed that risk
assessment need to be considering all hazards and their collaborations and must be reviewed
when changes occur. Risk assessment will be even more difficult to identify for risks arising
from MT work, which is characterized by difficulty in identifying or the lack of the researcher
capacity to evaluate risks.
Risk control: Risk management is basically a control problem Rasmussen, J., and Svedung, I.,
(2000). The review focuses on the most suitable methods of risk control:
)a) Control Hierarchy is achieved by applying four main levels of action: 1) the hazard is
eliminated; 2) a physical barrier is erected between the hazard and the performer (worker); 3)
personal protective equipment is used; and 4) workers comply with written safety instructions.
)b) Root Cause Analysis addresses accident causation according to four basic categories:
management factors (safety and risk control), intermediate factors (procedures, work design,
training), performance (behavioral and technical) factors, and external (environmental) factors.
)c) Griffel, A. (1999), Comparative Analysis consists of measurements for risk prevention
using the following four dimensions: effectiveness, applicability, efficiency, and influence.
Moreover, since most serious accidents are apparently caused by the operation of hazardous
systems outside the design envelope, the basic challenge in the development of improved risk
management strategies is essentially to ensure improved interaction between the decision
making and planning strategies at the various levels of the organization Rasmussen, J., and
Svedung, I., (2000).
Thus, despite the currently implemented risk management strategy, the control of MTRD
appears to be unsolved yet.

Accident causation: Accident investigation/causation analyses enable one to better understand
the factors and processes leading to accidents. Our analysis that follows explores further
accident causation or aggravating factors, i.e., risk dormancy in multi-team work.
This paper's primary objective is to develop a holistic safety model, in which both management
and labor respond to various dormant MT risk situations. Management is responsible for
making decisions concerning the handling of such risks and accident causation in accordance
with a SMS. Disorder might result in a potential for unsafe actions. Such actions are
characterized by the following major attributes:
 Deviation of a process from its planned time schedule, requiring corrective action to
remove the source of nonconformity in order to prevent recurrence. The corrective
action is aimed at ensuring that the existing potentially hazardous situations do not
lead to accidents.
 Time lags in the sequenced work of the various teams that require communication and
mutual reporting.
 Continuing random changes to the physical sites, thereby necessitating continuous risk
assessment.
 Uncontrolled multiple degrees of freedom, instead of a tight and narrow path to a
successful outcome.
 The lack of quality methodology principles being applied to monitor safe performance,
so as to reduce or even prevent accidents, especially those with casualties.
 These attributes can create hazards that might be difficult to identify properly.
Two major problems with MT risk management have been identified:
(1) RD identified in MT work is often underestimated. For example, after excavating
trenches intended for power or communication lines or for drainage pits, the installation of the
cables of pipes is often delayed and the trenches and pits are marked using yellow caution tape
only. Despite the identification of an open trench or a pit as a hazard, such trenches and pits are
sometimes left open for days and even weeks, constituting a threat to other teams working at the
site.
(2) The potential threat of risk situations in an MT work is often misidentified. This leads
to a dynamic risk situation. For example, a maintenance technician places a rag on the floor to

absorb the condensation from a faulty office air conditioner and leaves to get his tools. A
secretary inadvertently steps on the wet rag, slips and falls, and injures her ankle.
The analysis that follows addresses the occupational accident data of the integrated electrical
public utility (PU) in the State of Israel between 2004 and 2011. As of 2011, the PU company
employed 12,687 workers and maintained and operated several power station sites with an
aggregate installed generating capacity of 13,133 MW, supplied to customers via a national grid
transmission and distribution (T&D) system. The following segmentation of the employee
roster into five operational divisions reflects the company’s main areas of activity relevant to
this study.
The works and expertise of the first and the second divisions, the North and South District ones,
are the T&D of electrical power. The third division is engaged in building power plants and
substations. The fourth division is in charge of logistics, and provides transportation services,
cranes, heavy vehicles and workshop works to the other divisions. The fifth division is the
Generation Division, which operates the power stations. The number of the employees in these
five divisions is shown in Table 1.
Table 1 Number of employees by division
Division
North
District
South
District Logistic Generation Construction
Number of
employees 800 1300 700 2100 1700

The reported accidents of five PU’s divisions during 2004-2011 were compiled by its safety
department. The data presented in this paper were collected in accordance with the Israel
Institute for Occupational Safety and Hygiene (IIOSH) classification system. Each of the
accidents was examined to determine its causation in the context of the RD in MT work. The
results were subdivided into categories using two main factors: risk dormancy (RD) and non-
risk dormancy (NRD). The criteria described in section 1.3 were applied. The results, as they
N.MTRD.A - Non MTRD Accidents
MTRD.A - MTRD Accidents
Table 2 RD and Non- RD accidents in MT work
relate to the above five PU divisions, are shown in Table 2. These results reveal as much as
9.85% RD related accident rate for all the accidents.
2. Analyses
2.1 Risk dormancy analysis
2.1.1 Multi-team risk dormancy
Risk dormancy is the time delay between the occurrence of a failure (hazard event) in the
action of one team (Team A) that affects another team (Team B) involved in the process (Fig.3).
Such a failure has the potential to produce a hazard that is underestimated or undetected by
Team B and eventually becomes or generates a risk that lies dormant for a period of time (risk
dormancy). This risk has no immediate effect on the Team A member who caused it, but might
be a threat to another team (referred to as Team B) that will later continue the same job or will
Division
North
District
South
District Logistic Generation Construction
N.MTRD.A 783 860 474 1391 715
MTRD.A. 45 81 64 134 133
TOTAL 828 941 538 1525 848
%
MTRD.A 5.4 % 8.6 % 11.9 % 8.78 %
9.85 %

be engaged in a different job at the site. This situation is referred to as risk dormancy in MT
work.
An analysis of the RD time path reveals the following stages leading to an accident (Fig.3):
T0 - Beginning of Team A activity that could possibly generate a hazard event that
becomes a risk and could threaten the Team B work
TR- Hazard event time
Td- Risk dormancy time, which is equal to the time interval from the hazard event caused
by the Team A activity until the accident occurs, injuring the next team. The time is estimated
by the professional safety officer teams, based on their experience and knowledge.
Tacc - The time the accident occurred.
This paper addresses the time aspects of RD in MT work and proposes a model for risk
evaluation and management based on time-dependent probability (TDP) methodology. In this
context, classification criteria for RD are related to risks generated by MT activities.
2.1.2 Probabilistic analysis of risk dormancy time
The analysis of RD in MT work requires collecting information regarding an accident and
establishing the sequence of events that led to the accident. This includes identifying the team
affected by the accident and the accident occurrence time, as well as the team that most
probably generated the risk that ultimately materialized (risk-causing team) and the time when
Beginning of activity that most
likely generated a hazard event that
constitutes a risk for Team B
Risk dormancy time
Figure 3 MT risk dormancy pathway
Team A: hazard
Team activities
Team B: underestimates/fails to detect
hazard created by Team A
Team's "A" Hazard
Hazard event
Accident
Time

the risk was actually generated. The affected team and the time, Tacc, of accident occurrence are
easily determined in most cases since accidents are usually investigated and documented. It is,
however, often quite difficult to identify the risk-causing team and determine the time at which
the risk was generated Figure (3).
Still, identifying the team that generated the risk is quite complicated and requires efforts
of a team of professional analysts, such as, e.g., the safety officer's team, which is supposed to
be familiar with the stages and layout of the work. The accident investigation determines not
only the active causes leading to the accident, but also includes the attributes of the schedule
itself, as well as tools, places, personnel, etc. involved in the risk creation. As a result, the safety
officers can determine with high confidence the commencing time T0 of the Team A activity
that most likely created a hazard event TR, which became a risky one and threatened the Team
B. Finally, a team of safety experts analyzes all actions that preceded the occurrence of the
accident, since the range of the risk dormancy time uncertainties is basically the risk creation
time until its materialization.
One can therefore calculate the statistics of the risk dormancy duration from its creation
TR until accident occurrence Tacc regardless of the teams involved in the hazard generation. RD
time is a random variable; hence a probabilistic analysis should apply. Actually, Tacc could be
established rather accurately due to the time recording of an accident’s occurrence, while T0 and
TR should be considered as are best estimates made by the professional safety officers.
RD is clearly a positive value, and its range is between the RD generating point TR on one
side and the time of the accident occurrence Tacc where RD terminates at the other.
The risk dormancy time Td is a random variable extending between the beginning of
Team A activity T0, which could possibly generate a hazard event TR, and the accident time
Tacc. When TR is close to T0, then the time Td reaches its maximum value. Similarly, when the
time TR is close to Tacc, then Td approaches zero. Thus, the entire range of RD time uncertainty
can be expressed as
(1)

In accordance with the maximum entropy principle, we choose a uniform distribution for
RD time Figure (4) regardless of which particular team caused it.
The probability density function (pdf) is therefore a constant, as expressed by the
equation:
(2)
Here is the Heaviside step function:
(3)
f (Td) - Normalized pdf of RD time (Normalization of pdf by Tacc to achieve an integral equal
to 1) and Td is the random RD time uniformly distributed
Our observations are based on the distribution of RD time generated by a number of
accident occurrences and not only on a single event. Thus the distribution should be averaged
for all events. The average is the arithmetic mean of all the RD times:
Figure 4 Risk dormancy time distribution

Here - Average of risk dormancy time of each division, i - Index of time to accident
on each division, n - RD accidents number on each division, k - Division index.
The RD time probability of all accidents at the PU (in the five divisions, to be precise)
expressed in equation (5):
Here - Average probability of RD time of all divisions
Fexp – experiment cdf of RD time of all divisions
3. Results
As shown in equation (7), the CDF fit function is specified the five divisions of the PU. This fit
function positively predicts the experimental CDF function Fexp equation (6) of RD time for
MTRD accidents data:
(7)
F (Td) – CDF fit function
θ1 - Scale parameter, Short-term expected time
θ2 - Scale parameter, Long-term expected time
β1 - Shape parameter, Short-term expected time
β2 - Shape parameter, Long-term expected time
α – Partition parameter, dividing the data into short α part and long (1-α) content
The RD time distribution parameters for the five PU divisions are shown in Table (3).
Each one is also subdivided by two distinctive populations. Sub-data are well described by
Weibull distribution. Accordingly, each data subset is characterized by a vector of three
parameters as shown in the following table:

Table 3 Distribution characteristic parameters
Table (3) distinguishes between two different groups. First, the three divisions: North,
South and Logistics, showing a clear distinction between short and long term. Expectedly, the
fit function of this group shows a good fit to RD time data see Figures (5a) (5b) (5c). However,
the second group of the two other divisions, Generation and Construction, has a majority of RD
time appearances of short-term character, about 0.9 and 0.8 respectively, compared to a smaller
long-term RD appearance. Because of that we ignore the long term for this group, whose sub-
data are well described by Weibull distribution. Each subset data are characterized by a vector
of two parameters (Table 4).
Table 4 Distribution characteristic parameters
Division/
Parameter
α
θ1 θ2 β1 β2
North
District
0.47 3.98 570 0.92 1.4
South
District
0.68 4 539 0.92 0.96
Logistics 0.69 19 622 0.67 1.32
*
Generation
0.9 20 660 0.66 1.55
*Construct
ion
0.8 51 750 0.49 2.32
Division / Parameter θ1 β1
Generation 26 0.4
Construction 121 0.45

Accordingly, a modified CDF fit function of the Weibull type is specified:

The fit function shows a sufficient fitness to RD time data see Figures (5d) (5e). Monte-Carlo
simulation is used to generate random points from the domain RD time distribution data to
determine the validity of the five divisions' parameters - a kind of bootstrap simulation.
The simulation data show rather poor correlation between α, β and θ parameters. Consequently
we are considering them as independent parameters.
Hazard function: To confirm the results of the effect of RD one could examine the
impact of these parameters by employing the hazard function for each division:
The hazard function has resulted in the same groups of CDF functions with respect to RD time:
Group One: North, South and Logistics divisions characterized by two shape parameter β1 and
β2 as follows: 0.92, 0.92, 0.67 and 1.4, 0.96, 1.32 respectively (see Tables (3) (4). The results
for the β value were found to be close to 1, demonstrating almost constant failure rate in time
0 200 400 600 800 1 10
3

0
0.01
0.02
0.03
0.04
trace 1
Figure 7-a Hazard function of North district divisions
Dormancy time
Hazardfunction

see Figures (7a) (7b) (7c).
However, we are unable to explain the volatility behavior of the two models presented in
equation 7, which brings one to the three choices of Weibull. Similarly, one could question
why these parameter values were obtained. These questions will be addressed in the future
work.
0 200 400 600 800
0
0.01
0.02
0.03
0.04
trace 1
Figure 7-b Hazard function of South district divisions
Dormancy time
Hazardfunction
0 200 400 600 800
0
0.01
0.02
0.03
0.04
trace 1
Figure 7-c Hazard function of Logistic division
Dormancy time
Hazardfunction

Group Two: Generation and Construction Divisions characterized by single-shape
parameter β1 of 0.4 and 0.45 respectively see Table 4. The results of β were found to be less
than 1 demonstrating a decreasing failure rate in time as shown in Figures (7d) (7e).
4. Discussion
The type of organizations considered in this study are quite complicated, as they are
characterized by significant and strong interdependence between the management and MT
professional labor, in addition to the effect of interaction among themselves, regardless of
0 200 400 600 800 1 10
3

0
0.01
0.02
0.03
0.04
trace 1
Figure 7-d Hazard function of Generation division
Dormancy time
Hazardfunction
0 200 400 600 800 1 10
3

0
0.01
0.02
0.03
0.04
trace 1
Figure 7-e Hazard function of Construction division
Dormancy time
Hazardfunction

simultaneity. This complexity could lead to problematic aspects in internal company behavior,
sometimes causing safety problems.
The scope of this paper extends the risk management approach from the well-known
management models, with the addition of the important aspect of the MT safety perspective
beyond simultaneous situations, i.e., RD - risk dormancy. The research provided here extends
the existing approaches to a holistic level.
The obtained data on RD accidents indicate that the risk dormancy time distribution is
characterized by Weibull parameters: θ1, β1 - short term, θ2, β2 -long term respectively and
partition parameter α as shown in Tables (3) (4) above, for situations, in which the very nature
of the work dictates the dormancy time, as supported in the following data discussion:
First, θ1, β1 short term expected RD time and α partition parameters:
(1) North and South divisions - T&D districts
Scale parameter θ1 , whose RD time is 3.9 and 4 hours, respectively. An examination of
the accident investigation data shows that tasks in the T&D districts have the following
characteristics:
i. Most tasks are scheduled and completed in one day, because the nature of T&D work
requires power supply resumption to customers as quickly as possible. As a result, the short
term of risk dormancy time lasts a few hours.
ii. Tasks are performed sequentially by a professional MT.
iii. Subtasks are performed sequentially.
iv. Similar field of activity. E.g., lineman-teams of differing proficiency levels are
necessary for executing complementary parts of power line work due to the complexity of the
work and the existence of risk factors such as electricity, and, as a consequence of that, have
high safety level requirements. For example, erecting a transformer requires at least two
different teams: an electricians’ team to de-energize transformer connections to the power lines
and install grounds and an overhead line-work team to install the transformer. Thus, the work
teams require the necessary expertise for each phase of the work.
Shape parameter β1 of 0.92 for both districts:
These β1 values are close to 1, indicating an almost constant accident rate regarding RD
time. This happens if there is maximum entropy, characterized by exponential distribution

process. Remarkably, hazards and risky situations analyzed in this paper, and which are more
likely to cause accidents at a constant rate, are related to electrical supply divisions (South and
North divisions as shown in Figures (7a) and (7b).
Partition parameter α - divides the South and North divisions’ risk dormancy time
appearances into two separate categories in which the short term is 0.47, 0.68 respectively.
Case study - 1: A lines work team of PU North division performed an underground
cable connection to an overhead line. According to the safety instructions, the cable to be
worked on must be positively identified by tags and must be isolated from the electric supply
sources. Furthermore, tests must be performed to verify that the cable is de-energized, and
grounds of an approved type must be applied to protect workers from all the energy sources.
Earlier in the morning of the same day, an authorized clearance team should be assigned to
identify and de-energize the underground cable, in accordance with an authorization provided
and documented by a system operator. The clearance team is supposed to install the protective
short-circuiting and grounding equipment required for the protection of the team working on
cable connection. The permission to start working was given at the work site. While the cable
cutting work was in progress, an explosion occurred. The team cut an energized cable. The
authorized clearance team misidentified the correct cable and gave the work authorization for
the wrong cable. Workers were injured due to electrical arc flash.
In this case the above-mentioned scale parameter characteristics apply about 2 hours of
short-term dormancy time.
(2) Logistic division
Scale parameter θ1, whose risk dormancy time is 19 hours. The present results revealed a
prominent attribute related to work course duration. The large majority of appearances of these
RD accident occurrences are at the end of a working day or a shift. The following theme is the
main characteristic of the risk dormancy causation: in the Logistic division the main MT
activities occurring at the beginning or at the end of the working day/shift were the loading and
unloading of trucks.
Shape parameter β1 - a shape parameter value of β1 = 0.67  1 indicates that the accident
rates decrease over time. This happens if significant hazards or risky situations are generated
result in an accident at a decreasing rate over time.

Partition parameter α, which is dividing the Logistic division risk dormancy time
appearances into two substantial populations' 0.69, 0.31 of short and long term, respectively.
Case study - 2: A workshop employee was on his way to repair a metal processing
machine. A stack of iron bars, delivered the previous day, was still on the workshop floor,
protruding into the employee’s pathway. He stumbled as he passed the stack and injured his leg.
Material deliveries are usually made in the morning and materials are unloaded from trucks at
the workshop yard close to where the machines are placed, pending transfer to storerooms.
These irons bars were unloaded a day before the accident. A 24 dormancy time was estimated
by the safety officer.
Case study - 3: The PU owns a rather big truck fleet, used for truck-mounted work
platforms and truck-mounted cranes, which are used for loading/unloading and uplifting
workers to heights. In this case one of the trucks was sent back to duty from in-house periodic
maintenance service on the morning of that day, the truck driver opened the engine hood during
a routine cleaning and checking procedure at the end of the shift and was injured while trying to
remove a "piece of rubber" that was inadvertently left there by a garage worker. An eight-hour
RD time was estimated by safety department.
(3) Generation and Construction divisions with significant short term expected
RD time
It's obvious from Table (3) that partition parameter α of the magnitude of 0.9 and 0.8,
respectively, indicates the dominant short term RD time appearances in these divisions.
Therefore, we neglected/ignored the long term appearance in those two divisions as shown in
Table (4).
Scale parameter θ1, whose risk dormancy time is 26 and 121 hours, respectively. Task
substitute time, i.e., first task completion and transition to the next task by MT at generation,
construction and logistic divisions are longer than in T&D districts.
Shape parameter β1 of 0.4 and 0.45 again a value of β1  1 indicates that the accidents
rate decreases over time. This happens if there are significant hazards or risky situations
generated early and leading to an accident in a decreasing rate over time.
Case study - 4: To carry out a maintenance job in a turbine building of a power station, a
scaffold was erected and placed on the route of an overhead bridge crane. The crane consists of
parallel runways with a traveling bridge spanning the gap and equipped with hoist that travels

along the bridge. These cranes are electrically operated from ground level by a control pendant.
Three days later a team from another department used the crane for lifting heavy valves as part
of a job. The crane hit the scaffold, causing extensive damage. In this case the above-mentioned
generation scale parameter characteristics apply a risk dormancy time of about 72 hours.
Second, θ2, β2 long term expected RD time and α partition parameters:
(1) North, South and Logistic divisions
Scale parameter θ2 whose RD time is 570, 539 and 622 hours, respectively, the long
term RD accidents are likely to affect teams with no professional linkage between them. There
is no significant difference observed in the results for all the divisions as seen in Table (3) and
(4).
Case study – 5: A working team of the Southern PU district was sent for carrying out
maintenance work on electric supply line of low voltage network.An employee whose job
included activities installing, constructing, adjusting, repairing, etc. climbed on a metal pole and
started repair work. An electrical current flow as a result of contact between transformer cables
and the pole causing electrical shake to worker. Investigation found that a month before another
working team of the same district performed different work on the same transformer, causing
faulty cables connection of the transformer. As a result, loose connection of the cable made
contact with the conductive metal pole and electrical flash of short circuit injured the team.
In this case, different teams, namely maintenance and operation teams of the same
district, performed different tasks without professional affiliation or linkage between them. The
scale parameter θ2 characteristic applied a risk dormancy time of about 720 hours.
(2) Generation and Construction divisions
Negligible long term expected RD time
5. Conclusions
A novel probabilistic risk management model has been introduced to characterize the risk
dormancy phenomenon to be considered as a proper way of taking in to account MT aspects in
the occupational safety research. Furthermore, a holistic perception of MT functional
complexity allows for a generalized view of MT mutual interaction instead of focusing in the
single team behavior.
The following conclusions can be drawn from the carried out analysis.

The model is innovative in two major ways: Firstly, the identification of risk dormancy in
sequential multi-team work, i.e., Multi-team Risk Dormancy – MTRD. Though, unidentified
dormant risks or the underestimation of identified dormant risks are a "ticking bomb": each such
risk represents an unsafe/hazardous event that is certain to happen in the foreseeable future and
which threatens other teams continuing the same or a different job on site. Indeed, the current
time-space approach does not address or offer solutions to such risks. Therefore, we have
developed an RD approach that offers a solution for predicting TDP. Secondly, the PU accident
database enables us to evaluate and determine the above-mentioned significant risk aspect
tendencies. Accordingly, the proposed model defines risk dormancy, a new facet of risks
generated by multi-team work in modern industrial and infrastructure organizations, regardless
of the time frame involved.
Acknowledgments
True friendship is the willingness to sacrifice one's self for the other. This study is
dedicated to my friend, the late Dr. Majdi Latif, who inspired and encouraged me.
References
Abdelhamid, S. T., and Everett, J. G. (2000): Identifying Root Causes of Construction Accidents,
Journal of Construction Engineering and Management, ASCE, 126, 52.
Akinci, B., Fischen M., Levitt R.; and Carlson R., (2002): Formalization and Automation of Time-
Space Conflict Analysis Journal of Computing in Civil Engineering.
Carter, G., and Smith, S., (2006): Safety Hazard Identification on Construction Projects, Journal
of Construction Engineering and Management, Vol. 132, No. 2.
Cuny, X., and Lejeune, M. (2003). "Statistical Modelling and Risk Assessment", Safety Science,
Volume 41, Issue 1, February 2003, Pages 29-51.
Drivas and Papadopoulos (2004) Occupational Risk Assessment. In: ELINYAE – EKA
Griffel, A. (1999). "Countermeasure analyses: A strategy for evaluating and ranking safety
recommendations. Unpublished manuscript.
La Coze, J., (2005). "Are organisations too complex to be integrated in technical risk assessment
and current safety auditing?", Safety Science.
Mitropoulos, P., Howell, G. A., and Abdelhamid, S.T., (2005). "Accident Prevention Strategies:
Causation Model and Research Directions", ASCE 2005.

Papadopoulos, G., et al. Occupational and public health and safety in a changing work
environment: An integrated approach for risk assessment and prevention. Safety Sci. (2009),
Rasmussen, J., Pejtersen, A. M., and Goodstein, L. P. p-149 (1994). Cognitive system
engineering, Wiley, New York.
Rasmussen, J., and Svedung, I., (2000). "Proactive Risk Management in a Dynamic Society" Risk
& Environmental Department, Swedish Rescue Services Agency, Karlstad.
Reason, J.T., (1997). Managing the Risks of Organizational Accidents. Ashgate Publishing Ltd.,
Aldershot.
Reason, J.T., (2000). "Human error: models and management" British Medical Journal, 320, 768-
770.
Rosenfeld, Y., Rozenfeld, O., Sacks, R., and Baum, H., (2006)" Efficient and timely use of safety
resources in construction" CIW99, International Conference on Global unity for Safety &
Health, Beijing, Chain.
Rozenfeld, O., Sacks, R., and Rosenfeld, Y., (2009). ‘CHASTE - Construction Hazard Analysis
with Spatial and Temporal Exposure’, Construction Management & Economics (accepted
for publication)
Sasoua, K., and Reason, J., (1999). "Team errors: definition and taxonomy" Reliability
Engineering and System Safety 24

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