IF CRASC'15 - 14-16 MAGGIO 2015 ROMA The safety in road tunnels is a very delicate issue, since that a minor accident or a failure of a vehicle can degenerate into scenarios that can lead to a high number of victims. For example, on the 24 March 1999, 39 people died when a Belgian HGV carrying flour and margarine caught fire in the Mont Blanc Tunnel. In the first part of this study has been summarized the operation logic of a specific model for the risk analysis, the PIARC/OECD Quantitative Risk Assessment Model, and how it derives risk indicators. In the second part, a comprehensive risk analysis is performed in a long tunnel in South Italy, accounting for multifaceted aspects and parameters. The analysis is integrated with a sensitivity analysis on specific parameters that have an influence on the risk. In sections 2, 3, and 4 the concept of Risk and its assessment is dealt. In section 5, the proce-dure followed by the QRA model to derive societal and individual risk indicators is discussed, starting from a given number of possible accident scenarios. In section 6 conclusions are written regarding the application of the studied model.

1. IF CRASC’15
III CONVEGNO DI INGEGNERIA FORENSE
VI CONVEGNO SU CROLLI, AFFIDABILITÀ STRUTTURALE, CONSOLIDAMENTO
SAPIENZA UNIVERSITA’ DI ROMA, 14-16 MAGGIO 2015
RISK ANALYSIS FOR SEVERE TRAFFIC ACCIDENTS IN ROAD
TUNNELS (PART I)
C. Di Santo
Università degli Studi di Roma "La Sapienza"
K. Gkoumas
Università degli Studi di Roma "La Sapienza"
F. Bontempi
Università degli Studi di Roma "La Sapienza"
ABSTRACT
The safety in road tunnels is a very delicate issue, since that a minor accident or a failure of
a vehicle can degenerate into scenarios that can lead to a high number of victims. For example,
on the 24 March 1999, 39 people died when a Belgian HGV carrying flour and margarine
caught fire in the Mont Blanc Tunnel.
In the first part of this study has been summarized the operation logic of a specific model for
the risk analysis, the PIARC/OECD Quantitative Risk Assessment Model, and how it derives
risk indicators. In the second part, a comprehensive risk analysis is performed in a long tunnel
in South Italy, accounting for multifaceted aspects and parameters. The analysis is integrated
with a sensitivity analysis on specific parameters that have an influence on the risk.
In sections 2, 3, and 4 the concept of Risk and its assessment is dealt. In section 5, the proce-
dure followed by the QRAmodel to derive societal and individual risk indicators is discussed,
starting from a given number of possible accident scenarios. In section 6 conclusions are
written regarding the application of the studied model.
1. INTRODUCTION
QRAM software, created by the cooperation of OECD, PIARC and European Commission
is a tool whose purpose is to calculate the risk related to road traffic of heavy good vehicles.
In fact, heavy vehicles circulation in case of accident, especially for dangerous goods, implies
an additional risk to road users, for facilities, for the local population and the environment.
Therefore, through Quantitative Risk Analysis, the competent authorities may assess whether
to allow the transition of all types of goods through a given path or, simply, through a given
gallery. To help the authorities in this choice, with particular attention to the high extension
galleries, the PIARC (The World Road Association) and the OECD (The Organization for

2. C. Di Santo, K. Gkoumas, F. Bontempi
Economic Cooperation and Development) have developed this model using risk assessment
methodologies used in the past in the chemical and nuclear industries. Accidents in this field
are rare, but can have major consequences.
2. THE CONCEPT OF RISK
In general the risk is given by
𝑅𝑅 = 𝐹𝐹 ∙ 𝐶𝐶 (1)
where F is probability of occurrence (frequency) of a given event, and C is the relative con-
sequence which can be measured in number of fatalities, injuries, cost of the structure damage
caused by the event and damage to the environment. In this study, consequence C refers to
the number of victims N. Furthermore, the risk can be Social or Individual.
The Societal Risk (SR) can be defined as the risk to which it is subjected a group of people
in case occurs a scenario s and it is given by
𝑆𝑆𝑆𝑆 = 𝐹𝐹(𝑁𝑁) ∙ 𝑁𝑁 (2)
where F(N) is the frequency [1/year] of a event that causes a number of victims ≥ N (i.e.
cumulative frequency), and N the number of fatalities [Fat]. The SR is represented through
F – N diagrams and Expected Values of victims (EVs).
The F-N diagrams may be applied to illustrate the risk profile for a specific hazard such as a
fire in a road tunnel. On the x-axis are shown in logarithmic scale, the number of victims N,
and on the ordinate axis, still on a logarithmic scale, the corresponding annual rate F(N) with
which events occur that cause a number of victims ≥ N .
The expected value (EV), which is the expected amount of victims in a certain time period,
can be calculated as the area under the F-N curve by using the following equation (Petelin S.
2009):
𝐸𝐸𝐸𝐸𝑠𝑠 = ∑ 𝐹𝐹(𝑁𝑁𝑖𝑖) ∙ 𝑁𝑁𝑖𝑖
∞
𝑖𝑖=0 = ∫ 𝐹𝐹(𝑁𝑁)
+∞
1
𝑑𝑑𝑑𝑑 (3)
where EVs is the expected value of victims in one year caused by the scenario s, and F(Ni)
the cumulative frequency of an accident that causes Ni fatalities [1/year].
The Individual Risk can be expressed as an annual frequency [1/year] or as the time interval
between two accidents that cause the same fatalities among the local population because of
an accident on the road network. Considering an accident scenario s, it indicates the proba-
bility (in 1 year) that an individual, located at some distance from the road network, dies. The
number of people exposed to the incident does not affect the value of the risk, thus, for this
reason it is called "individual". It can be represented as the risk distribution in the space sur-
rounding the road section through a 2D map.

3. Risk analysis for severe traffic accidents in road tunnels (Part I)
Figure 1. Example of a F-N curve.
3. GENERAL RISK ASSESSMENT PROCEDURE
The risk assessment procedure consists of different steps. In a first phase data need to be
collected. In case of a road route analysis, traffic data, accident frequencies, meteorological
conditions, tunnel equipment (in presence of a tunnel), etc., are prepared. Next, there is the
Risk Analysis phase, in which the risk indicators, through a quantitative model, such as the
QRAM, are calculated. In the final step, it must be assessed whether the risk indicator value
obtained with the analysis is acceptable or not. If the level of risk is not acceptable, it is
necessary to provide for measures to mitigate the risk, which can be of prevention if they
reduce the frequency of occurrence of accidents, or of protection if they act on the protection
provided to users once a scenario has occurred.
Data
Collection
Data
Preparation
Risk
Calculation
Using QRAM
Is Risk
acceptable?
NO
Additional
risk reduction
measures
START YES End
Figure 2. Typical risk assessment procedure using PIARC/OECD QRA Model.
In literature, there are several methods to evaluate the risk level, some of which are based on
the "absolute criterion" of risk acceptability, others on the "relative criterion". In this study,
the absolute criterion is used. The absolute criterion allows to identify the minimum level
safety that must be guaranteed for road users.
1.00E-06
1.00E-05
1.00E-04
1.00E-03
1.00E-02
1.00E-01
1.00E+00
1.00 10.00 100.00 1000.00
FCUM[acc/year]
N [FAT]
Tollerable Risk Line
Acceptable Risk Line
ALARP
Not Acceptable Area
Acceptable Area

4. C. Di Santo, K. Gkoumas, F. Bontempi
About the F-N curve, a method known in the literature is the As Low As Reasonably Practi-
cable (ALARP) principle, where the F-N plane is divided into three zones (Figure 1): Not
Acceptable Risk Area, Acceptable Risk Area and ALARP Area (prevention and/or mitigation
actions must be taken to reduce the risk, as far as reasonably practicable). In fact, if the curve
is in the middle between the two lines, then measures for risk reduction should be taken, but
the operations costs must not be disproportionate to the benefits obtained in risk reduction.
So, a cost – benefit analysis is needed.
4. THE COMBINATION OF QRAM MODELAND SCENARIO BASED ANALYSIS
The analysis carried out by the QRAM is at the macroscopic level. In fact it is possible to set
some of the parameters that characterize the various scenarios. To describe the evolution of
the physical phenomenon, the authors of QRAM have used simplified models whose results
(obtained for differents possible evolutions of the phenomenon) were included in contin-
gency tables. Then the QRAM during the analysis uses data provided by the final user, to-
gether with those that it picks up from the tables, to calculate risk indicators.
User's Inputs
QRAM
AnalysisExternal
Models Results
Quantitative
Risk Estimation
Figure 3. QRAM analysis.
So, it is difficult to evaluate the effects of risk mitigation measures on the specific scenario,
because is not possible to evaluate changes that occur in the evolution of the physical phe-
nomenon. For example, in case of fire in a tunnel, it is complicated to know how the flow of
gasses changes if it is decided to use a transverse ventilation system characterized by dynamic
dampers for the fumes extraction. For example, in order to evacuate toxic gases, it is impos-
sible to understand if opening a damper, instead than another, could modify the evolution of
the whole scenario and in which way.
Therefore, it is convenient to combine the QRAModel with CFD simulations (Computational
Fluid Dynamics) and evacuation models. An operating method to follow can be to identify
the critical scenarios that give the most significant contribution to the overall risk through the
QRAM, and then to simulate those scenarios in detail in order to define risk reduction
measures (Petelin S. 2009). An example of application of CFD simulations is related in the
paper “Computational flu-id dynamics simulations for the assessment of a road tunnel fire
safety” (Baroncelli et al., Proceedings IF CRASC’ 15), while the possibility of using evacu-
ation models fully coupled with fire simulations is described in the paper “Influence of panic
on human behavior during emergency egress for tunnel fires” (Gai et al., Proceedings IF
CRASC’ 15).

5. Risk analysis for severe traffic accidents in road tunnels (Part I)
Data Collection
Data Preparation
Risk Calculation
Using QRAM
Is Risk
acceptable?
NO
Additional
risk reduction
measures
START
YES
End
Idintification of
Critical Scenarios
Single Scenario
Simulation
CFD Simulation
(Fire, Ventilation)
Evacuation Model
(Evacuation, Rescue)
Qualitative Risk
Estimation
Measures
Included
in the model?
YES NO
Figure 4. Risk assessment approach that combines quantitative risk assessment and qualitative critical
scenario analysis.

6. C. Di Santo, K. Gkoumas, F. Bontempi
5. THE GENERAL FUNCTIONING OF THE QRA MODEL
The procedure followed by the model to calculate SR indicators (i.e. F-N curves and EVs) is
represented in Figure 5.
1 – Dangerous Goods and Accident Scenarios selection
2 – Effect j (due to the scenario “s”) and its Range from the epicentre: Ej=f(d) ↔ Rj
3 – Mortality Rate within the range Rj → %LETHj
4 – Mortality Rate corrected considering the possibility of escape → %LETHj=f(tevac)
5 – Scenario s Probability of occurrence → fs
6 – Number of victims due to the scenario s → N=∑jNj=f(Rj, Dru, Ljam, %LETHj)
7 – F-N curve for the scenario “s” and its relative Expected Value
Figure 5. QRA model functioning.
Firstly, the model considers certain Dangerous Goods (DGs):
• LPG (Liquefied Petroleum Gas), flammable mixtures of hydrocarbon gasses, but in this
model reference is made to propane;
• Motor Spirit: a mixture of volatile and flammable liquid hydrocarbons;
• Acrolein (C3H4O): a toxic liquid;
• Chlorine (Cl2) and Ammonia (NH3): toxic gases;
• Liquefied CO2 under pressure.
Each DG can cause different scenarios depending on the vessel type and on how the release
of the substance takes place (Figure 6).
For each scenario, up to three types of physical effects on road users and local population are
considered by the model:

7. Risk analysis for severe traffic accidents in road tunnels (Part I)
Figure 6. Accidents Scenarios in QRAM software.
• Thermal Effects (thermal radiation produced by the flames): measured through the Radi-
ative Heat Flux, qr [kW/m2
]. This value depends on the distance of the target from the
flames, on the Surface Emissive Power (SEP) [kW /m2] (maximum amount of heat flux
near the flames surface), on the orientation of the target with respect to the flames and on
the atmospheric transmissivity.
• Pressure Effects (the shock wave generated by the explosion): caused by the pressure
wave generated by an explosion, which propagates outward from the epicenter. When the
wave front arrives at a certain point of the space, the overpressure increases instantly from
zero to its peak value and immediately after the overpressure decreases. So, the overpres-
sure drops to zero in a short time, and this marks the end of the positive phase. The phys-
ical parameters used to represent the effects of pressure on people are the peak overpres-
sure ∆Ps [bar], and the positive phase duration t+[bar].
• Toxic Effects (poisoning due to the release of toxic substances): consist in poisoning of
users for exposure to a particular toxic substance. To define the level of poisoning, to
which is subject an individual, are used the concentration in air of the toxic substance in
question and the time of exposure to that particular concentration, C [ppmv].
With the aid of specific models, the QRAM establishes the relationship between the single-
effect and the distance from the epicenter of the incident. At this point, through the probit
analysis, the model derives the relationship between the mortality rate (probability of dying
occuping a certain position in relation to the incident) and the distance from the center of the
incident (Figure 7).
The probit analysis is a type of regression used to analysing the relationsheep between a
stimulus (dose) and “all or nothing” (such as death) response. This method was proposed by
Finney (Finney 1971). Exposing a biological organisms population to a number of different
doses of a toxic substance he plotted a Gaussian curve for each experience and the complete
dose-response curve using the cumulative mean response at each dose. For convenience, he
Dangerous Good Mode of Containment Diameter release hole Mass Flow Rate Scenario n°
- - - 20 MW HGV Fire 1
- - - 100 MW HGV Fire 2
Cylinder (50 kg) - - BLEVE 3
- - BLEVE 7
VCE 8
Torch Fire 9
Pool Fire (≥400MW Fire) 4
VCE 5
Bulk (30000 liters) 50 mm 24.8 kg/s Toxic Liquid Release 11
Cylinder (100 liters) 4 mm 0.02 kg/s Toxic Liquid Release 12
Chlorine (toxic gas) Bulk (20 t) 50 mm 45 kg/s Toxic Gas Release 6
Ammonia (toxic gas) Bulk (20 t) 50 mm 36 kg/s Toxic Gas Release 10
Liquified CO2 Bulk (20 t) - - BLEVE 13
Acrolein (toxic
liquid)
Bulk (18 t)
50 mm 36 kg/s
Bulk (18 t) 100 mm 20.6 kg/s
No DG
Liquefied Petroleum
Gas (LPG)
Motor Spirit

8. C. Di Santo, K. Gkoumas, F. Bontempi
Figure 7. 100MW “Fire Effect Intensity – Distance” and “Lethality percentage – Distance
relationsheep”.
plotted the response versus the logarithm of the dose, because this form provides a much
straighter line in the middle of the curve. In general, these curves can be drawn for different
exposures, including heat, pressure, radiation, and toxic gases. However, for computational
purposes, the response - dose curve is not convenient, so Finney developed a method to pro-
vide a straight-line equivalent to the response-dose curve. He established a relationship be-
tween the probability P that an individual dies under a given dose (i.e. lethality percentage)
and the PROBIT variable Pr (PROBability unIT):
P �or RATIO� =
1
√2𝜋𝜋
∫ 𝑒𝑒−
1
2
𝑢𝑢2
𝑑𝑑𝑑𝑑
Pr−5
−∞
(4)
Plotting the Pr – log (dose) curve using a linear probit scale, therefore, he transformed the
sigmoid shape of the normal response versus dose curve into a straight line. In QRA model,
the probit variable Pr is computed from:
𝑃𝑃𝑃𝑃 = 𝑎𝑎 + 𝑏𝑏 ln(𝐷𝐷) (5)
Where D is the load related to the single effect (Dose), a and b are probit parameters, the
values of which depend on the effect considered.
In the calculation of the mortality rate for increasing distances from the point of the accident,
the model also considers the possibility of escape or of find shelter, calculating the total dose
of a given effect that affects a subject during the escape. This is done by calculating the ef-
fective dose Dj that affects a road user:
𝐷𝐷𝑗𝑗 = ∫ �𝐸𝐸𝑗𝑗 ∙ 𝑡𝑡𝑒𝑒𝑒𝑒𝑒𝑒,𝑗𝑗�𝑑𝑑𝑑𝑑
𝑡𝑡̅
0
(6)
where 𝑡𝑡̅ is the minimum time between the duration of the scenario ts and the time necessary
for the user to evacuate the area tevac.

9. Risk analysis for severe traffic accidents in road tunnels (Part I)
Figure 8. Response versus log dose curve (left), the relationship between percentages and probits
(centre) and the transformation that converts the sigmoidal response versus log dose curve into a
straight line (right), (Crowl D. 2011).
After, the model derives the frequency of occurrence of each scenario once an accident,
involving HGV without dangerous goods (20MW and 100MW Fires) or DG-HGV, has oc-
curred. For example, for scenarios involving DG-HGV:
𝑓𝑓𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖 ∙ 𝑓𝑓𝑎𝑎𝑎𝑎𝑎𝑎_𝐷𝐷𝐷𝐷,𝑖𝑖 ∙ (𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 ∙ 𝐿𝐿𝑖𝑖 ∙ 24 ∙ 365 ∙ 10−6) (7)
Where fijk is the frequency of occurrence of the scenario j (involving DG of type k) on the
section i in a year, Pijk is the conditional probability that occurs scenario j once the accident
has taken place, facc_DG,i is the annual frequency of accidents involving DG-HGVs
[acc/(MVkm*year)] and TDik is the traffic of DG-HGVs of type k passing through the section
i in one hour. As regards the Pij values, they are automatically fetched, during the calculation,
from precompiled tables within the software. In fact, these values were previously calculated
by the analysis of the Fault Tree.
At this point, the model calculates the number of victims following a scenario s, considering
as the main parameter the users density per linear meter of the road section. This density has
a different value depending on whether you are considering a portion occupied by a traffic
jam, or a portion characterized by fluid traffic. For example, the number of victims among
road users in a traffic jam is given by the following expression:
𝑁𝑁 = �𝑅𝑅 ∙ 𝐷𝐷𝑅𝑅𝑅𝑅𝑅𝑅� ∙ %𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 (8)
Where Ljam is the traffic jam length [m], DRUJ is the Road Users Density in a traffic Jam
(number of people in HGVs + cars on this section in direction A for each meter of a jam)
[users/m], and %LETH is the Lethality Percentage [-].
Finally, the model considers that each scenario s may appear as a different event Ei, varying
some parameters as the section of the path being considered, the accident location on the
section, the traffic direction, the reference period of the day (peak hour traffic), etc. Thus,
QRAM computes a different number of victims Ni for each one of these events Ei, and to
each one of them associates a probability of occurrence fi. From these frequencies, he calcu-
lates the cumulative frequencies Fi. Therefore, associating with each possible number of vic-
tims (Ni) the corresponding cumulative frequency (Fi), it is possible draw the risk curve on
the plane F-N (Figure 10).

10. C. Di Santo, K. Gkoumas, F. Bontempi
Figure 9. Number of Fatalities Calculation.
Scenario "s"
Event Event Frequency Fatalities Cumulative Frequency
Ei fi Ni Fi
[-] [1/year] [Fat] [1/year]
E1 f1 N1 F1 = f1
E2 f2 N2 F2 = f1+f2
E3 f3 N3 F3 = f1+f2+f3
E4 f4 N4 F4 = f1+f2+f3+f4
... ... ... ...
En fn Nn Fn = f1+f2+f3+f4+...+fn
Figure 10. F-N construction for a generic scenario s.

11. Risk analysis for severe traffic accidents in road tunnels (Part I)
6. CONCLUSIONS
This model allows to make a risk assessment in a road route along which there can be one or
more tunnels. It can also assess the intrinsic risk of a tunnel extrapolated from the route where
it is inserted.
In both cases, the risk indicators are calculated considering the main features:
• of the road route (total length, etc.),
• of the platform road (number of lanes, etc.)
• of the tunnel (cross-sectional area, etc.),
• of its safety equipment (system ventilation, drainage system, by pass, etc.) and
• of the type of traffic (total traffic, heavy vehicles, dangerous goods, etc.).
Therefore, the model, during the analysis, considers different aspects of road transport and
up to 13 possible accident scenarios. So, the risk assessment that results is at macroscopic
level, i.e. the model refers to the entire transport system as a whole.
ACKNOWLEDGEMENTS
The authors thank: Giordana Gai (Student of PhD in Structural Engineering from the Uni-
versity of Rome "La Sapienza"), Francesco Petrini (Researcher in Structural Engineering
from the University of Rome "La Sapienza"), Tiziano Baroncelli (University of Rome "La
Sapienza"), Eng. Luigi Carrarini (ANAS S.p.A.), Eng. Alessandra Lo Cane (M.I.T).
This work is partially supported by the spin-off company StroNGER s.r.l. which is gratefully
acknowledged.
REFERENCES
Crowl D., Louvar J.: Chemical Process Safety, Fundamentals with Applications. III, Prentice Hall, 2011.
Finney, D.J.: Probit Analysis, A statistical treatment of the sigmoid response curve. Cambridge
University Press, 1971.
Petelin S., Luin B., Vidmar P.: Risk Analysis Methodology for Road Tunnels and Alternative Routes.
Journal of Mechanical Engineering, 2009, pp. 41-51.
PIARC, OECD: Safety in Tunnels, Transport of dangerous goods through road tunnels. OECD
Publications, 2001.