IMO (SLF 47/48) Passenger Ship Safety
2.2 RBD Case Story: Large Passenger Vessel
2.2.2 Early Implementation Results
2.2.2.3 Safety Level
With all the elements in place, there is still one step to make, namely to estimate the safety level using the framework and risk model presented in “RBD Overview”
and apply it to the example cruise vessel by considering flooding and fire hazards as outlined next.
0.0 0.2 0.4 0.6 0.8 1.0 1.2
0 400 800 1200 1600 2000 2400 2800 3200 3600 4000 4400
Fire Zone Area (m2)
Predicted Frequency of Fire Ignition (per year)
MVZ 8 MVZ 7 MVZ 6 MVZ 5 MVZ 4 MVZ 3 MVZ 2 MVZ 1 Max expected frequency of Ignition
for fire zones within the SOLAS size limits
Expected frequency of Ignition for fire zones exceeding 1600m2
Fig. 2.73 Fire incidence – correlation between frequency of fire and fire zone size
Night case
1E–10 1E– 09 1E– 08 1E– 07 1E– 06 1E– 05 1E– 04 1E– 03 1E – 02 1E– 01 1E+00
0 400 800 1200 1600 2000 2400 2800 3200 3600 4000 4400
Floor Area (m2)
Predicted Frequency of Fire Escalation (per year)
MVZ 8 MVZ 7 MVZ 6 MVZ 5 MVZ 4 MVZ 3 MVZ 2 MVZ 1 Maximum expected frequency of escalation
for fire zones within the SOLAS size limits
Expected frequency of escalation for fire zones exceeding 1600m
Fig. 2.74 Fire escalation – correlation between frequency of fire escalation and fire zone size
Day case
1E–10 1E– 09 1E– 08 1E– 07 1E– 06 1E– 05 1E– 04 1E– 03 1E– 02 1E– 01 1E+ 00
0 400 800 1200 1600 2000 2400 2800 3200 3600 4000 4400
Floor Area (m2)
Risk (Equivalent fatalities per year)
MVZ 8 MVZ 7 MVZ 6 MVZ 5 MVZ 4 MVZ 3 MVZ 2 MVZ 1 Maximum expected risk level
for fire zones within the SOLAS size limits
Expected risk level
for fire zones exceeding 1600m2
Fig. 2.75 Fire risk – correlation between risk and fire zone size
Flooding (Collision Only Events)
Using the formulation outlined earlier, the probability for N fatalities occurring due to collision∩f looding events can be expressed as shown in Eq. (2.4), where prN(N|f looding) = prN(N|hz1), with the probability mass function presented
Flooding
1.0E– 07 1.0E– 06 1.0E– 05 1.0E– 04 1.0E– 03 1.0E– 02 1.0E– 01 1.0E+ 00
0 500 1000 1500 2000
N
prN(N | flooding)
( )
max
0
=1.0
= N
i
Niflooding pr
Fig. 2.76 Probability distribution for N fatalities o due to flooding for the example cruise vessel
here in Fig. 2.76. To estimate the contribution to risk from flooding loss scenarios, Eqs. (2.1), (2.2), and (2.3) are also used together with the statistical value for the frequency of occurrence of collision∩f looding events per cruise ship year (2.58E-03).
Figure 2.76 displays a numerical result (bimodal distribution) that makes sense readily on account of statistics. It emphasises the fact that collision∩f looding events result in relatively few fatalities or a very large number of fatalities rather than a mid-range value.
Fire
A heuristic argument is used here to estimate the fire risk contribution that accounts for available numerical results for a cabin case fire scenario using the example cruise vessel, namely assuming (seriously on the conservative side) that the sample distri- bution for N number of fatalities is representative of such distribution for the whole ship. Hence, similar to flooding, to assess the contribution to risk from fire loss sce- narios, it is necessary to model both the frequency of fire occurrence per ship year, f rhz(hz2=f ire), as well as the probability distribution, pN(N|f ire), for N number of fatalities onboard the ship due to fire events.
The first of these, annual frequency of fire events, is given in from historical data as 1.2e-2 occurrences per cruise ship year. To derive the second term, use is made of first-principles tools available at SSRC as described in Sect. 3.1. Using these tools, prediction of the probability distribution for the number of fatalities due to fire at the cabin in question is done through Monte Carlo simulation for a range of parameters relevant to the specific location considered. An example of prediction of the pN|f li (N|f ire∩cabin)based on some 800 runs for one location
Fig. 2.77 Probability distribution for occurrence of N fatalities due to fire in accommodation spaces of the example cruise vessel
in accommodation spaces (day and night scenarios) is shown in Fig. 2.77. This is a noteworthy result, displaying the known fact that the number of fatalities in fire events is more frequently a small fraction of the passengers/crew onboard.
To allow for risk integration, it is further assumed, as indicated above that this sample distribution for N is representative of such distribution for the whole ship weighted according to fire statistics onboard passenger ships, as shown in Table 2.7 below.
Risk Integration (Equations Refer to Lecture on RBD Overview)
To illustrate the relative contributions of fire and flooding loss scenarios to the risk integral Eq. (2.1), the probability mass functions given by Eqs. (2.4) and (2.10) for flooding and fire, respectively, can be compared, as shown in Fig. 2.78.
Table 2.7 Probability distribution for occurrence of fire onboard passenger ships (1990–2003) – Germanisher Lloyd, FP49/3/1
i f li pf l(f li)
1 Accommodation 0.14
2 Machinery A 0.54
3 Machinery other 0.10
4 Cargo spaces 0.08
5 Service spaces 0.12
6 Other 0.02
Fig. 2.78 Probability distribution for N number of fatalities occurring due to fire and flooding for the example cruise ship (version 1)
Alternatively, such comparison can be made using either of the models in Eqs. (2.1) and (2.2), being used separately or combined. Both results are shown in Fig. 2.79. Also drawn on Fig. 2.79 is the ALARP region using the societal risk evaluation criteria, recommended for cruise ships, (Skjong 2006).
Deriving from Fig. 2.79, the contribution to risk from flooding and fire as well as the risk as a summary statistic are now at hand. More specifically,
Riskcollision∩ f looding=1.14 fatalities per ship year
Riskf ire =0.11 fatalities per ship year
Total Risk =1.25 fatalities per ship year
On the basis of the foregoing the following remarks can be made:
• The “holly grail” is not really the absolute value of the risk numeral itself (for the uncertainty in its evaluation is too large for comfort) but the fact that this sum- mary statistic derives from a comprehensive model that links all contributions to risk in a way that a bottom up approach will never be able to achieve in as com- plex a system as a large passenger ship. In other words, a truly holistic approach has to be linked to total risk estimation and as such address safety top down, leading to identification of design vulnerability and cost-effective risk reduction measures.
• Having said this, as much as an aggregate risk numeral can convey a lot of in- formation, it could equally “hide” a lot of information; hence the need to apply safeguards at individual risk contributors, right down to individual scenario level of each hazard in question with performance criteria in place to ensure “fitness
1.00E– 05 1.00E– 04 1.00E– 03 1.00E– 02 1.00E– 01 1.00E+00
10000 1000
100 1 10
N
FN(N)
flooding fire fire & flooding
() ∑ () ()
= ×
=hz
n
j
j N j hz
NN fr hz pr Nhz
fr
1
()()=∑Ni=maxN N
NN fri
F
()fire=1.2×10–2 frhz
()flooding=2.58×10–3 frhz
11 .
=0 fire PLL 14
.
=1 flooding PLL
25 .
=1 PLL
Need to reduce flooding risk Need to reduce flooding risk
Fig. 2.79 The annual frequency of occurrence of N or more fatalities due to loss scenarios of fire and flooding for the example cruise ship (Version 1)
for purpose”. This however, must be seen as the second step in targeting cost- effective safety.
• As can be seen in Figs. 2.76 and 2.77, the distinctive feature of the loss scenario involving hull breach and flooding is the high possibility of catastrophic scenar- ios involving very large number of fatalities. Although not entirely unexpected, this result demonstrates the importance that needs to be placed on ship stability deficiencies; the flooding risk contribution is an order of magnitude higher than that due to fire despite the fact that, according to statistical data, fire accidents are nearly 5 times more frequent than collision accidents. It is also to be noted that contributions to total risk from grounding-related flooding loss scenarios have not been accounted for; for large passenger ships, this is a large contribution according to (Skjong 2006).
• The derived F-N curve shows that with the example cruise ship used in this study, the likelihood for a catastrophic accident is unacceptably high; hence measures ought to be taken to reduce it irrespective of cost.
To demonstrate how this might be done, Version 5 of the example cruise ship (having an A-Index of 0.92) has been used as a first iteration. The outcome is rather interesting and is presented in Figs. 2.80 and 2.80, demonstrating emphatically the importance of targeting high Index-A values in cruise ship designs. Notably for a 15% increase in Index-A, total risk is reduced by 60%!
1.0E– 07 1.0E– 06 1.0E– 05 1.0E– 04 1.0E– 03 1.0E– 02 1.0E– 01 1.0E+ 00
0 500 1000 1500 2000 2500
N
prN(N | flooding)
( ) 1.0
max
0
∑= =
N
i
Niflooding pr
Nmax
Ver 1
Ver 5
Fig. 2.80 Probability distribution for N number of fatalities occurring due to fire and flooding for the example cruise ship (versions 1 and 5)
1.00E– 06 1.00E– 05 1.00E– 04 1.00E– 03 1.00E– 02 1.00E– 01 1.00E+ 00
1 10 100 1000 10000
N
FN(N)
Nmax Ver 1, PLL=1.14 [f/sy]
Ver 5, PLL=0.46 [f/sy]
Fig. 2.81 The annual frequency of occurrence of N or more fatalities due to loss scenarios of fire and flooding for the example cruise ship (versions 1 and 5)
Acknowledgements This chapter represents the collective thinking permeating within SSRC with many colleagues contributing to building the emerging picture. Special thanks are due to all our colleagues in SAFEDOR, the UK Maritime and Coastguard Agency, the European Commission, Color Line, Royal Caribbean Cruise Lines and Deltamarin for their contribution in the development of the tools and concepts presented in this paper through funding, data and valuable discussions.
References
Cooper L (2002) Compartment Fire-Generated Environment and Smoke Filling. SFPE Handbook of Fire Protection Engineering, 3rd Edition, 3–10.
Dogliani M, Vassalos D, Strang T (2004) Evacuation Notation – A New Concept to Boost Pas- senger Evacuation Effectiveness in the Cruise Industry. COMPIT 2004, 3rd International Euro- Conference on Computer Applications and Information Technology in the Marine Industries, Parador Siguenza, Spain, May.
Guarin L, Majumder J, Puisa R (2007a) Human Life Safety – Quantitative Risk Analysis. SAFE- DOR Report D2.5.2, June.
Guarin L, Majumder J, Shigunov V, Vassalos G, Vassalos D (2004) Fire and Flooding Risk Assess- ment in Ship Design for Ease of Evacuation. Proceedings of the 2nd International Conference on Design for Safety, Osaka, Japan, October.
Guarin L et al. (2007b) Design for Fire Safety. Proceedings of the 3rd International Conference on Design for Safety, San Francisco, USA, September.
HARDER (2003) Harmonisation of Rules and Design Rationale, EU Contact No. GDRB-CT-1998- 00028, Final Technical Report, 31 July.
Horvat A, Staples C, Sinai Y (2007) Passenger Safety – Data, Tools and Modelling. SAFEDOR Deliverable D.2.5.5, April.
HSE (2001) Marine Risk Assessment. Offshore Technology Report 2001/063, Prepared by DNV for HSE, UK.
IMO (2006) Guidelines on alternative design and arrangements for SOLAS Chapters II-1 and III.
MSC/Circ.1212, 15 December 2006.
Jasionowski A (1997–2005) An Integrated Approach to Damage Ship Survivability Assessment.
PhD, University of Strathclyde, Glasgow.
Jasionowski A, Bulian G, Vassalos D, Francescutto A, Pawlowski M, Maccari A (2006) Modelling Survivability. SAFEDOR Deliverable D2.1.3, www.safedor.org.
Jasionowski A, Vassalos D (2006) Conceptualising Risk. Proceedings of the 9th International Con- ference on Stability of Ships and Ocean Vehicles, Rio de Janeiro, September.
Jasionowski A, Vassalos D, Guarin L (2004) Theoretical Developments on Survival Time Post- Damage. Proceedings of the 7th International Ship Stability Workshop, Shanghai, November.
Mather P, Strang T (1997) Regulatory Requirements – Do These Adequately Reflect Actual Fire Experience? International Conference Fire at Sea, November, London.
Purser D A (2002) Toxicity Assessment of Combustion Products. Ch. 6, The SFPE Handbook of Fire Protection Engineering, 3rd Edition.
ROROPROB (1999) Probabilistic Rules-Based Optimal Design of Ro-Ro-Passenger Ships, EU- Funded Project, Contract GRD1-1999-10819.
SAFER EURORO I (1997) Design for Safety: an Integrated Approach to Safe European RoRo Ferry Design, EU-Funded Project, Contract ERB-BRRT-CT97-5015, www.safer-euroro.org.
SAFER EURORO II (2001) Design for Safety: An Integrated Approach to Safe European RoRo Ferry Design, EU-Funded Project, Contract G3RT-CT-2001-05050.
Skjong R (2006) FSA on Cruise Ships. SAFEDOR Subproject 54.1 Report (Restricted).
Skjong R, Vanem E, Oyvind E (2005) Risk Evaluation Criteria. SAFEDOR Deliverable, Public, D4.5.2, DNV, 21st October.
SLF 47/17 (2004) Sub-Committee on Stability and Load Lines and on Fishing Vessels Safety, 27th September.
Tillander K (2004) Utilisation of Statistics to Assess Fire Risk in Buildings. VTT Building and Transport, VTT Publications 537, ESPOO 2004, ISBN 951-38-6392-1.
Vanem E, Skjong R (2004) Collision and Grounding of Passenger Ships – Risk Assessment and Emergency Evacuations. Proceedings of the 3rd International Conference on Collision and Grounding of Ships, ICCGS 2004, Izu, Japan, 25th to 27th October.
Vassalos D (2004) A Risk-Based Approach to Probabilistic Damage Stability. Proceedings of the 7th International Ship Stability Workshop, Shanghai, China, November, 13p.
Vassalos D (2006) Passenger Ship Safety – Containing the Risk. Marine Technology Journal, Vol.
43, No. 4, pp. 203–212, October.
Vassalos D, Guarin L, Vassalos G, Bole M, Kim HS, Majumder J (2003) Advanced Evacuation Analysis – Testing the Ground on Ships. Proceedings of the 2nd International Conference on Pedestrian and Evacuation Dynamics, PED 2003, August, Greenwich, London, 12p.
Vassalos D, Jasionowski A (2007) SOLAS 2009 – Raising the Alarm. Proceedings of the 9th International Stability Workshop, Hamburg, Germany, August.
Vassalos D, Jasionowski A, Dodworth K, Allan T, Matthewson B, Paloyannidis P (1998) Time- Based Survival Criteria for Ro-Ro Vessels. RINA Spring Meetings 1998, London.
Vassalos D, Kim HS, Christiansen G, Majumder J (2002) A Mesoscopic Model for Passenger Evacuation Simulation in a Virtual Ship-Sea Environment and Performance-Based Evalua- tion. ‘Pedestrian and Evacuation Dynamics’, Springer-Verlag, Berlin; Heidelberg; New York, pp. 369–391.
Vassalos D, Konovessis D, Guarin L (2006) Progress on Risk-Based Ship Design Implementation.
Proceedings of the 9th International Marine Design Conference, Ann Arbor, Michigan, USA, May, 14p.