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Guidance on the Design of Ships for
Enhanced Escape and Evacuation
Project EGO
EPSRC funded project 2005-2007
Project Partners:
Academic Partners: FSEG University of Greenwich: GR/T22100/01
Academic Partners:
DRC University College London: GR/T22117/01
Industrial Partner:
Directorate of Sea Systems, Defence Equipment
and Support organisation of the UK Ministry of Defence (DES-SESea)
Traditionally, when designing a ship the driving issues are seen to
be powering, stability, strength and seakeeping. When the broad form
of the layout has been finalised, human factors issues related to
crew numbers, ship operations and evolutions, such as evacuation,
are either ignored, considered as an after thought or incorporated
through a set of prescriptive rules. This can result in significant
operational inefficiencies and potentially hazardous environments
onboard. The overall objective of this multidisciplinary research
project is to show the advantages of integrating the cutting edge
technologies of Escape Simulation (i.e. maritimeEXODUS developed by
the FSEG of the University of Greenwich) and Ship Configurational
Design (i.e. SURFCON developed by the Marine Research Group of UCL).
This will enhance the guidance for all parties in the design,
regulation, construction and operation of ships with regard to the
main aspects of personnel movement onboard. To achieve this, the
project draws on the well-established expertise of the FSEG of the
University of Greenwich in the area of fire and evacuation modelling
and the Design Research Centre, Marine Research Group of UCL in the
area of ship architecture design. While this project addresses the
design of naval vessels, the principle behind the proposed
methodology and the tool set produced have direct application to the
design of commercial and passenger vessels.
|
PROJECT OBJECTIVES |
1) To explore the impact on naval ship configurational
design of issues associated with crew manning numbers, function and
movement.
2) To identify key performance measures for successful crew performance
in normal and extreme conditions.
3) To extend the ship evacuation software maritimeEXODUS to include
additional non-emergency simulation capabilities.
4) To extend the ship design software so that it can provide a modelling
environment that interactively accepts maritimeEXODUS simulation output
for a range of crew evolutions.
5) To demonstrate a methodology for ship design that integrates ship
configuration design with modelling of a range of crewing issues through
PARAMARINE-SURFCON.
FSEG were responsible for objectives (2) and (3), UCL were responsible
for objective (4) and both organisations were jointly responsible for
objectives (1) and (5). By clicking on this
link you will be connected to the UCL web pages associated with this project. |
PROJECT SUMMARY |
The partnership successfully achieved
all objectives, demonstrating the viability of the methodology and the
benefits that can be derived through its use. Key significantly novel
advances made in the project include:
a) The use of simulations to evaluate personnel movement applied in the
earliest stages of ship design.
b) The evaluation of personnel movement extended to include
non-emergency scenarios.
c) A transparent and reproducible methodology developed to determine
overall HF performance of a given ship design and shown to be both
discriminating and diagnostic.
d) Guidance provided on the level of design definition necessary to
carry out personnel movement simulations in preliminary ship design.
e) The project provided insights to ship designers on specific features
that enhance and restrict personnel movement onboard heavily populated
vessels.
f) The project indicated those research areas that remain to be
addressed for the comprehensive simulation and assessment of personnel
movement in non-evacuation scenarios onboard ships.
The figure below illustrates the overall relationship between the expertise
domains and software tools on which each research partner led. This
shows, in flowchart form, the design and analysis activities that were
carried out in each of the tools. The interface tools developed in this
project were separate from the PARAMARINE-SURFCON and maritimeEXODUS
software and permitted the required information to be transferred
between them.
 |
PROJECT
REPORT |
The full project report presented to the EPSRC may be downloaded as a
pdf file. Download EPSRC project report
here.
NOTE: This document will be made available after the project has been
assessed by the EPSRC.
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THE HPM
METHODOLOGY |
A novel new discriminating and
diagnostic methodology for assessing the HF performance of ship
design has been developed which is systematic, transparent
and repeatable. The method, known as the Human
Performance Metric (HPM) allows the accurate and rapid assessment of HFs
issues associated with vessel layout and crew operating procedures. The
HPM works by systematically evaluating one layout design against
another, whether two variants of the same design or two completely
different ship designs. The analysis is performed using the
maritimeEXODUS evacuation simulation software. The HPM involves
specifying and defining a range of: Evaluation Scenarios, Performance
Measures, Functional Groups and Weights. Each of these terms
will be described.
The HPM requires the specification of a range of
relevant Evaluation Scenarios (ES) against which the vessel will
be tested. These scenarios are intended to define the scope of the
challenges the vessel will be subjected to. In order to gauge vessel
performance across a range of criteria, the ES are made up of both
evacuation and Normal Operations (NOP) scenarios. The nature of the
scenarios are dependent on the type of vessel. Relevant evacuation
scenarios may include those required by MSC.1/Circ 1238 and include the
IMO night and day scenarios or their naval equivalent. The NOP
scenarios are dependent on the nature and class of vessel. For example,
a cruise ship application may require the time to empty the cinema is
minimised while a naval vessel may require watch changes to be completed
within set period of time. Each ES is then simulated using the
maritimeEXODUS software.
In addition to defining the ES, a range of
Performance Measures (PM) must be defined that measure various
aspects of personnel performance in undertaking the tasks associated
with the ES. PM for passenger ship evacuation scenarios may include the
time required to complete the assembly process while for a naval vessel
NOP scenario, the total number of water tight doors (WTD) opened and
closed may be relevant. The suitability of the vessel layout will be
evaluated for fitness of purpose through some combination of the PM
resulting from the execution of the ES. The PM values are determined
from output for the ES produced by maritimeEXODUS.
As members of the ships complement may be involved
in undertaking different tasks during a particular ES, the ships
complement is divided into subgroups. Membership of each subgroup is
determined by the nature of the tasks undertaken by the individuals in
the particular ES, with each subgroup being made up of people
undertaking a common set of tasks. These subgroups are labelled
Functional Groups (FG). The introduction of FGs allows the
analysis to focus on the performance of important subgroups of the crew
whose contribution may swamp that of other FGs or be swamped by other
FGs when considering the overall performance of the vessel. An example
of a FG is the ‘damage control and fire fighting’ group which is a prime
example of a FG used in circulation ES. In addition to the FGs defined
by specific sub-populations, a special FG, identified as Ships Company,
is included in all ES.
To complete the HPM, performance scores ai,j(PMk),
for a particular design variant X associated with each PMk
must be determined. The PM score (ai,j(PMk)) is
simply its value derived from the execution of the maritimeEXODUS
simulation software for each FGj within each ESi.
To allow a meaningful comparison between
performance scores, each score is normalised, producing
āi,j(PMk). The
normalisation is performed using the largest performance score from the
competing design variants X. Using this approach, all the HPM entries
will be less than or equal to 1.0. The larger the performance score,
the worse the performance of the FG for that particular PM.
An overall score can be determined for each FG
representing the performance of the FG in the particular ES. This is
calculated by taking a weighted sum of the normalised PM scores
achieved by the FG across all the PMs. As not all PMs are considered of
equal importance, a weighting is introduced to differentiate between
various PMs. Thus each normalised score āi,j(PMk)
will have a weight associated with it Ai,j,k, where subscript
i refers to evaluation scenario ESi, the j subscript refers
to the functional group FGj and the k subscript refers to the
performance measure PMk. Thus the functional group score άi,j
is given by
άi,j = (Ai,j,1
x āi,j(PM1)) + (Ai,j,2 x
āi,j(PM2)) + - - - + (Ai,j,n x āi,j(PMn))
+ - - -
An overall scenario score (SSi) can also
be determined for each ESi representing the performance of
all the FGs in the particular ES. This is calculated by introducing a
FG weight Bi,j and taking a weighted sum of the FGj
scores achieved in the ESi. Finally, an overall vessel performance
can be determined for the design iteration X representing its
performance across all the ESs. This is calculated by introducing an ES
weight Ci and taking a weighted sum of the ES scores.
The HPM with scenario and design score along with
the all the associated individual scores and weights are presented in
the table below. The overall Vessel Performance VP, for design X can
then be compared against the VP score for all other designs to determine
which design produced the best overall performance. The matrix is also
diagnostic in that it allows the identification of which measures
contributed to the poor performance of a failed vessel design, or which
PM could be improved in a winning design.
Design X |
|
Evaluation
Scenario |
Functional Groups |
|
FG1 |
FG2 |
- - - |
FGn |
- - |
Scenario
Score |
Scenario
Weight |
ES1 |
B11 |
ά1,1 |
B12 |
ά1,2 |
- |
- - |
B1n |
ά1,n |
- |
- - |
SS1 |
C1 |
ES2 |
B21 |
ά2,1 |
B22 |
ά2,2 |
- |
- - |
B2n |
ά2,n |
- |
- - |
SS2 |
C2 |
: |
: |
: |
: |
: |
- |
- - |
: |
: |
- |
- - |
- - - |
- - - |
ESn |
Bn1 |
άn,1 |
Bn2 |
άn,2 |
- |
- - |
Bnn |
άn,n |
- |
- - |
SSn |
Cn |
: |
: |
: |
: |
: |
- |
- - |
: |
: |
- |
- - |
- - - |
- - - |
Overall Functional Group Scores |
SFG1 |
SFG2 |
- - - |
SFGn |
- - - |
|
|
Overall design performance |
VPDESIGN(X) |
|
MODIFICATIONS TO
maritimeEXODUS |
The
maritimeEXODUS software forms the core of the HF evaluation component of
the system. This software was specifically designed to simulate
evacuation scenarios and, as part of this project, its capabilities were
extended to include the simulation of the NOP scenarios. This
capability built on an existing feature known as the Itinerary List
(IL). Using the IL it is possible to assign crew (and passengers)
a list of tasks to perform. To accommodate the range of new
scenarios that needed to be simulated to complete the HPM a range of new
tasks were developed including:
-
‘Terminate’
command, used in the NOPs scenarios allowing crew to stay at their
last location once a task has been completed;
-
‘Repeat’
command, used to allow crew to repeat predefined set of tasks a
number of times as is required in the patrol task;
-
‘Search
Compartment’ command
which instructs crew to enter a list of assigned compartments to
undertake a search as part of the blanket search scenario;
-
‘Close
Door’ command which instructs a crew member to check that a door
has the correct status for the current ship state and if not, change
the status of the door;
-
‘Give’
and ‘Receive’ command allowing a senior member of the ship’s
staff to issue tasks to lower ranked members, who ‘Receive’ the task
.
In
addition to these capabilities, a range of other modifications and
additional software have been developed including:
- A separate utility program was developed (the Human
Performance Metric Analyser) which automatically constructs the HPM
matrix of human performance scores from maritimeEXODUS output that are
used in the evaluation of the vessel design.
- The process of building
vessel geometries ready for analysis was automated. Previously, the
process of preparing a geometry could take as much as two weeks to
complete, with the automation the majority of this process can now be
completed within half an hour (based on the Type 22 Batch III Frigate).
This is achieved through linking maritimeEXODUS to Paramarine-Surfcon
through appropriate filter software.
- A new scripting language (SSF) was
developed which enables third parties to easily set up a population and
their itineraries within maritimeEXODUS without the need to navigate
through a complex user interface, reducing a considerable amount of
tedious time consuming effort.
- Additional output files were required
from maritimeEXODUS in order for it to interact with the ship
configuration software SURFCON. These include images of contour maps
displayed within maritimeEXODUS showing the locations of severe
congestion and footfall maps. Animated output files were also
implemented in order to illustrate individuals moving around the design.
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DEMONSTRATION
APPLICATION OF HPM |
The
following demonstration of the HPM concept involves evaluating the
relative performance of two designs of a hypothetical naval vessel.
Vessel Geometries
The baseline vessel design (Variant 1) consists of 453 compartments
spread over eight decks. Decks No1 and No 2 (deck 4 and 5 respectively)
have a single central passageway connecting the aft to forward section
of the deck. The second variant design (Variant 2) consists of the 445
compartments spread over eight decks as in variant 1. The key difference
between the two designs is that Variant 2 has two passageways running in
parallel from the aft to the forward end of the vessel on both decks.

The Scenarios
Each vessel has a complement of 262. The crew are initially located in
the location they would be expected to be at the start of each scenario
as determined by the “state” of the vessel. Crew members not on watch
are located in their cabin. In this example each variant is assessed
using seven ESs. These are:
Naval evacuation scenarios;
-
‘normal day cruising A’ ES1,
-
‘normal day cruising B’ ES2,
-
‘Action Stations’ES3
NOPs scenarios;
-
‘State 1 Preps’ ES4,
-
‘Blanket Search’ ES5,
-
‘Family Day A’ ES6,
-
‘Family Day B’ ES7.
The Analysis
The seven evaluation scenarios (i.e. ES1 - ES7) were each run 50 times
using the maritimeEXODUS software and representative simulation result
files were selected for each scenario to construct the HPM for each
variant. An example animation produced by maritimeEXODUS for the
Action Stations ES for Variant 1 is presented below.

Click on the image to play the animation
The
PMs for each variant were then determined and the final HPM constructed
for each variant as shown below.
Evaluative scenario |
Scenario
Weight |
Variant 1 |
Variant 2 |
% difference between
Variant 1 and Variant 2 |
Normal
Day Cruising A |
1 |
46.14 |
44.33 |
3.93% |
Normal
Day Cruising B |
1 |
50.81 |
46.79 |
7.92% |
Action
Stations Evacuation |
1 |
51.45 |
46.70 |
9.23% |
State
1 Preps |
1.5 |
67.46 |
75.47 |
-11.87% |
Blanket Search |
1.5 |
78.04 |
84.29 |
-8.01% |
Family
Day A |
1.5 |
48.65 |
47.20 |
2.99% |
Family
Day B |
1.5 |
56.03 |
55.32 |
1.26% |
|
Overall Performance of design |
523.7 |
531.2 |
|
Variant 1 produces an overall Vessel Performance (VP) score of 523.7
while Variant 2 produces a VP score of 531.2. Thus we note that the
overall performance of both variants is broadly similar, with Variant 1
producing a marginally better (1.4%) overall human factors performance
according to the identified ES, PM and weights. Furthermore, we note
that Variant 2 outperformed Variant 1 in most of the scenarios, however
Variant 1 significantly outperformed Variant 2 in two NOPs scenarios.
The worst performing scenario for Variant 1 is ‘Action Stations
Evacuation’. As Variant 1 produces the better overall performance and
produces significantly better NOPs performance it is considered the
superior design. However, its performance may be improved by
investigating why it did poorly in its worst performing ES.
To
better understand why Variant 2 outperformed Variant 1 in the ‘Action
Stations Evacuation’ scenario (ES3) and to identify potential areas in
which Variant 1 can be further improved it is necessary to delve into
the sub-components of the HPM (see below). Presented below are the PM
scores for Variant 1 and 2 for ES3. We note that Variant 1 performed
better than Variant 2 in five of the 18 PMs (G2, G5, M1, M14 and M16).
Of these five PMs, four show at least 10% better performance than the
respective Variant 2 PM, with M14 (most times a WT door was operated)
and M16 (average number of doors used per person) returning 18% better
performance. However, 12 of the PMs for Variant 1 returned poorer
performance. Of these PMs nine returned values which were at least 10%
worse than those in Variant 2. The poorest performance was achieved by
G4 (average time spent in congestion) which returned 50% worse
performance.
FG1
– Entire Population |
Variant 1 |
Variant 2 |
|
Weight |
raw |
norm |
raw |
norm |
CONGESTION
CRITERIA |
|
|
|
|
|
C1 – the
number of locations in which the population
density exceeds 4 p/m2 for more
than 10% of the overall scenario time’ |
8 |
4 |
1 |
4 |
1 |
C2 – the
maximum time that the population density
exceeded the regulatory maximum of 4 p/m2
for 10% of the simulation time |
3 |
75.40 |
1 |
42.14 |
0.56 |
GENERAL
CRITERIA |
|
|
|
|
|
G1 – average
time required to complete all operations; |
4 |
256.7 |
1 |
193.54 |
0.75 |
G2 – average
time spent in transition
|
3 |
36.61 |
0.80 |
45.76 |
1 |
G3 – time to
reach final state |
8 |
666.7 |
0.22 |
594.50 |
0.20 |
G4 – Average
time spent in congestion |
3 |
150.6 |
1 |
74.93 |
0.50 |
G5 – average
distance travelled |
4 |
47.11 |
0.94 |
50.11 |
1 |
GEOMETRIC
CRITERIA: |
|
|
|
|
|
M1 – the
number of WTD used during the scenario. |
2 |
24 |
0.89 |
27 |
1 |
M2 – the
number of Hatches used during the scenario. |
2 |
31 |
1 |
25 |
0.81 |
M3 – the
number of ladders used during the scenario. |
2 |
31 |
1 |
25 |
0.81 |
M5 – the
number of doors used during the scenario. |
1 |
78 |
1 |
76 |
0.97 |
M8 – the
number of times the FG moved between decks |
2 |
373 |
1 |
322 |
0.86 |
M13 –
Average number of components used per member
of FG during the scenario |
2 |
4.47 |
1 |
4.36 |
0.98 |
M14 – Most
times a WT door was operated |
4 |
9 |
0.82 |
11 |
1 |
M15 – Most
times a hatch was operated |
3 |
10 |
1 |
7 |
0.70 |
M16 -
Average number of doors used per person |
3 |
1.59 |
0.82 |
1.94 |
1 |
M17 -
Average number of WT doors per person |
3 |
1.46 |
1 |
1.19 |
0.82 |
M18 -
Average number of hatches used |
3 |
0.27 |
1 |
0.23 |
0.83 |
We
note that for Variant 2, the overall average time spent in congestion
(as measured by G4) was some 50% less than in Variant 1. This
significant reduction in congestion results in Variant 2 being able to
complete the scenario 11% quicker than Variant 1 (as measured by G3).
Indeed, we note that while both vessels easily satisfy the international
set evacuation time requirements (as measured by G3) the levels of
congestion experienced exceed the international set limits in four
locations (as measured by C1) and Variant 1 experiences the most severe
congestion (as measured by C2). As the values for C1 and C2 are higher
than the regulatory limits, neither vessel would be deemed to be
acceptable. To address this issue and to improve the overall
performance of Variant 1, further investigation is required to uncover
the causes of the severe congestion.
Variant 1: Three Primary Congestion Regions in Actions
Stations Evacuation
Exploring the areas of congestion in Variant 1 (using the population
density graphical function available in maritimeEXODUS) suggested that a
single additional ladder connecting 01 Deck with No 1 Deck between two
of the severe congestion regions may alleviate some of the congestion by
providing an additional means of vertical movement.

Suggested solution place additional ladder between
01_Deck and No_01_Deck.
Further analysis using maritimeEXODUS revealed that this simple
modification eliminated two of the four congestion regions in the
‘Action Stations Evacuation’ scenario. With this modification in place
the HPM was re-evaluated for the Modified Variant 1. The Modified
Variant 1 now outperforms the original Variant 1 in each scenario and
produces an overall VP which is 6% more efficient than the original
Variant 1 design (see Table 7)
and 8% more efficient than the Variant 2 design. We also find that the
Modified Variant 1 design outperforms the Variant 2 design in all but
the ‘Normal Day Cruising A’ evacuation scenario.
Evaluative scenario |
Scenario Weight |
Variant 1 |
Modified Variant 1 |
% difference between Variant 1 and
Modified Variant 1 |
Normal Day Cruising
A |
1 |
47.81 |
46.59 |
-2.61% |
Normal Day Cruising
B |
1 |
51.62 |
44.98 |
-14.76% |
Action Stations Evac |
1 |
52.78 |
44.68 |
-18.11% |
State 1 Preps |
1.5 |
75.95 |
73.43 |
-3.43% |
Blanket Search |
1.5 |
86.25 |
85.45 |
-0.94% |
Family Day A |
1.5 |
52.28 |
49.55 |
-5.52% |
Family Day B |
1.5 |
57.57 |
53.57 |
-7.48% |
|
Overall Performance
of design |
560.29 |
529.24 |
|
In
summary, the HPM identified that the single passage variant produced
marginally superior HF performance than the dual passageway variant.
This demonstrates that the HPM methodology is discriminating.
The HPM was also used to identify areas in which the HF performance of
the single passageway variant could be further improved. Through adding
a single ladder to the single passageway variant we improved its overall
performance by 6% making the single passageway variant almost 10% better
than the dual passageway variant. This demonstrates that the HPM
methodology is diagnostic. Furthermore, the HPM
methodology can routinely be applied to any given design and so is
systematic, the logic process by which the decisions are made
are transparent and another engineer using the methodology
would arrive at the same conclusions and so the methodology is
repeatable. |
PROJECT
PUBLICATIONS AND PRESENTATIONS |
The following list
of papers were produced by the project partners. Some of the
publications and presentations are available in pdf format. Click on
the link provided to download the publication where available or to
view the powerpoint presentation.
- Andrews, D J, “The Use of Simulation in Preliminary Ship
Design, Proceedings of 12th International Conference on Computer
Applications in Shipbuilding”, Busan, Korea, August 2005, pp 419 - 432.
- Andrews, D J, Pawling, R and Casarosa, L, “Integrating
Ergonomics into Ship Design”, CETENA Human Factors Conference, Genoa,
October 2005, Proceedings on CD; copy held by UCL.
- Andrews, D J, Pawling, R, Casarosa, L, Galea, E R, Deere,
S, Lawrence, P, Gwynne, S and Boxall, P, “Integrating Ship Design and
Personnel Simulation”, INEC, IMarEST WMTC, London, 6 - 10 March 2006,
Proceedings on CD, ISBN 1-902536-54-1
A
pdf version of the paper is available for download
here.
- Andrews, D J, Casarosa, L and Pawling, R, "Integrating
the Simulation of Operations Into Preliminary Ship Design", NAV
2006; International Conference on Ship and Shipping Research, Genoa,
21 - 23 June 2006, Vol. 1, pp 4.3.1-4.3.12.
- Andrews, D J, “Simulation and the Design Building Block
approach in the design of ships and other complex systems”, 2006 Proc.
R. Soc. Vol. 462, pp 3407-3433.
- Andrews, D J, Pawling, R, Casarosa, L, Galea, E R,
Deere, S and Lawrence, P, “Integrating Personnel Movement Simulation
into Preliminary Ship Design”, RINA Int. Conf. on Human Factors in Ship
Design, London, 21-22 March 2007, pp 117-128.
A
pdf version of the paper is available for download
here.
- Andrews, D J, Pawling, R, Casarosa, L, Galea, E R, Deere, S and
Lawrence, P, “Integrating Personnel Movement Simulation into
Preliminary Ship Design”, International Journal of Maritime
Engineering, Volume 150 Part A1 pp 19-34, ISSN 1479-8751, 2008.
http://www.rina.org.uk/ijme0801.html
A pdf version of the
paper is available for download
here.
- Galea, E R, “The next step in the rise of maritime human
factors simulation models : Optimising vessel layout using human factors
simulation”, Key Note Address, The Rise of Maritime Simulation, Ocean
Innovation 2007, Halifax, Canada, 21-24 Oct 2007. (http://www.oceaninnovation.ca/OI-2007_guide_low%20res.pdf)
A
pdf version of the presentation may be viewed by clicking
here.
- Deere, S J, Galea, E R and Lawrence, P, “A Systematic
Methodology to Assess the Impact of Human Factors in Ship Design”,
Applied Mathematical Modelling, Applied Mathematical Modelling, 33,
867-883, 2009.
<http://dx.doi.org/10.1016/j.apm.2007.12.014>
A pdf version of the paper is available for download
here
- Deere, S J, Galea, E R and Lawrence, P,
“Optimising Vessel Layout using human factors simulation”, Pedestrian
and Evacuation Dynamics 2008, 27-29 Feb 2008 Wuppertal Germany, To
appear in conference proceedings 2009.
A pdf version of the presentation may be viewed by clicking
here
- Deere, S J, Galea, E R and Lawrence, P, “Assessing Naval Ship
Design for Human Factors Issues Associated with Evacuation and
Normal Operations”, COMPIT08, Ed: V.Bertram and P Rigo, 21-23 April
2008 Liege Belgium, ISBN-10 2-9600785-0-0, pp 33-47, 2008.
A pdf version of the entire proceedings can be downloaded from here:
http://www.anast.ulg.ac.be/COMPIT08/
- Andrews, D J, Pawling, R and Casarosa, L, "Interactive Computer
Graphics and Simulation in Preliminary Ship Design", COMPIT08, Ed:
V.Bertram and P Rigo, 21-23 April 2008 Liege Belgium, ISBN-10
2-9600785-0-0, pp 407-421, 2008.
A pdf version of the entire proceedings can be downloaded from here:
http://www.anast.ulg.ac.be/COMPIT08/
|
FURTHER INFORMATION
Prof. Ed Galea
Fire Safety Engineering Group
University of Greenwich
Greenwich Maritime Campus
Old Royal Naval College
Queen Mary Building
Greenwich SE10 9LS
UK
Tel: +44 (020)
8331 8730
Fax: +44(020) 8331 8925
e-mail:
E.R.Galea@gre.ac.uk
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