IF CRASC'15 - Roma, 14-16 maggio 2015. 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. The section 2 of this paper describes the tunnel San Demetrio on which was carried out risk analysis applying the PIARC/OECD QRA model, and in the section 3 are reported the main analysis results. In section 4, conclusions regard to risk analysis applied to real case and about the sensitivity analysis are reported. In particular, the sensitivity analysis has highlighted the most influential parameters in the 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 II)
C. Di Santo
Università degli Studi di Roma "La Sapienza"
K. Gkoumas
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.
The section 2 of this paper describes the tunnel San Demetrio on which was carried out risk
analysis applying the PIARC/OECD QRA model, and in the section 3 are reported the main
analysis results. In section 4, conclusions regard to risk analysis applied to real case and about
the sensitivity analysis are reported. In particular, the sensitivity analysis has highlighted the
most influential parameters in the 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
Economic Co-operation 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. C. Di Santo, K. Gkoumas
2. SAN DEMETRIO’S TUNNEL
The San Demetrio tunnel, completed in 2007 and currently managed by ANAS s.p.a., is lo-
cated on the Catania - Syracuse motorway, between Passo San Martino and km 130+400 of
the SS 114.
It is a natural tunnel with polycentric circular section, realized through traditional excavation,
having a thickness of the concrete lining of 50÷130cm (so is been chosen an intermediate
value of 60 cm). For the road platform thickness is been used a value of 0.3 m. The maximum
transversal slope is of 4.12% and the cross section area is about 87.31 m2
(11.2m · 8.06m).
The gallery is located entirely on the curve: southbound it has a radius of curvature of R =
5000m, and northbound radius is R = 2200m. The road platform includes two lanes of 3.75m
and a 3m lane emergency. The two unidirectional traffic tubes have a length of 2895 m in
direction SOUTH (Syracuse), and 2949 m in direction NORTH (Catania).
Being the structure REI120 classified as response to the fire, has been considered a value of
120 minutes under fire before it loses its mechanical strength, the seal to flames and hot gases,
the heat insulation. The temperature, under which the structure withstands for this time inter-
val, it has been assumed to be 1350 °C.
Figure 1. San Demetrio tunnel’s cross section and Plan of the road platform (Central Design
Management ANAS S.p.A. 2009).
Following the instructions provided in the QRA manual, for a double-tube tunnel with one-
direction traffic, at least three sections are needed to form the model.
Sections 2 and 3 towards the south, and sections 1 and 2 towards the north, must have zero
length. These sections will be skipped in the calculation, but they are necessary for the proper
modeling of the gallery. In particular, the section 3 southbound allows entering data for the
north tunnel. Equally, section 1 northbound allows entering data on the south bore. The open
sections numered as "2" are needed because it is not possible to insert two adjacent tunnel
sections.
In case of accident, evacuating people can use one of 9 pedestrian by-passes arranged every
300m that lead to the parallel tube, which serves as an escape route.
The tunnel is equipped with a longitudinal ventilation system consisting of 18 southbound
jet fans and 19 northbound jet fans. Identified the location of the fire, is activated automati-
cally, or at the hands of the control center, a ventilation system that pushes the air with an
average speed (on the cross section) of 3 m/s in the direction of traffic, freeing the route

3. Risk analysis for severe traffic accidents in road tunnels (part II)
21 - TUNNEL 3
2 3 - TUNNEL1
SOUTH (Syracuse)
NORTH (Catania)
x
Figure 2. San Demetrio Tunnel Model.
upstream of the incident by the toxic fumes. In this way is facilitated evacuation. For the
representation of the fire-fighting systems behavior, it is assumed a time of fire detection (via
thermo sensitive cable) of 3 minutes from the ignition, and a further time of 5 minutes for the
emergency ventilation establishment.
So, in the QRAM has been considered only a system of longitudinal ventilation, the volume
flow of extracted air by each segment has been set equal to 0. As regards, instead, the air flow
along the tunnel, it is assumed that, in normal operating conditions, is 0. With this choice it
is assumed that the axial fans only come into operation in case of emergency, and was ignored
piston effect due to vehicular traffic and to the pressure difference at the portals.
Identified a fire, jet fans start working at full capacity after 5 minutes after sending the com-
mand. These accelerate the air with an average speed along the tunnel of 3 m/s, then the
volume air flow moved at each node is equal to:
𝑉𝑉𝑛𝑛𝑛𝑛 = 𝑣𝑣 ∙ 𝐴𝐴 = 3
𝑚𝑚
𝑠𝑠
∙ 87.31𝑚𝑚2
≅ 261
𝑚𝑚3
𝑠𝑠
(1)
For ventilation, has been given a positive sign to the flow in a southerly direction (+261 m3
/s)
and a negative sign in direction north (-261 m3
/s). In fact, the airflow must follow vehicles
direction, which is +1 southbound (for how the model was constructed) and -1 northbound.
The alarm is given automatically using CO sensors, opacimeters, linear thermal sensors (heat
sensing cable) and system of CCTV cameras, which detect the presence of flames along the
tunnel. Therefore, in the model has been considered, for emergency management systems, a
maximum level of allocations for communication and control over people fleeing. According
to this level of equipment, the tunnel must be equipped with at least a PA system (public
address system) and a CCTV cameras system. Regarding the first, there are no exact infor-
mation on the presence or not of such a system. However, the tunnel has, in addition to the
CCTV system, other allocations for the emergencies resolution, so it was decided however
to give the maximum score on safety equipment that limit the number of victims.
In case of flammable or toxic liquids leakage, there is a drainage system consists in drains
placed every 50m and with a capacity of 150 l/s. Then, in QRA model has been considered a
distance between the manholes of 50m. As opening area of each manhole, however, failing
to have more information, is been chosen a value of 0.075 m2
.
Traffic data were provided by an engineering company (Impresa Pizzarotti & C. S.p.A. 2007).
For each direction of travel, the Average Annual Daily Traffic (AADT) is 21190 [veh/day].

4. C. Di Santo, K. Gkoumas
The daily distribution of traffic is divided into three time periods: QUIET, that corresponds
at 9 night hours with a traffic, NORMAL, corresponding to 10 hours of quiet daytime period
and PEAK (Figure 3).
Figure 3. Traffic Data of the San Demetrio Tunnel.
Finally, the percentage of each type of hazardous goods, compared to the total traffic of dan-
gerous goods, is reported in Figure 4.
Figure 4. Percentage of each type of hazardous good.
The probability to have accidents involving HGVs was calculated by estimating the expected
rate of accidents in the year 2027: 0.161 acc/(MVkm*year) in direction SOUTH, and 0.160
acc/(MVkm*year) in direction NORTH.
3. QUANTITATIVE RISK ANALYSIS RESULTS
In this section is reported the FN curve in direction south and its EV (Figure 5). For practical
reasons, it was decided to show only the cumulative curve of all scenarios. Also because, for
the purposes of risk assessment, it is interesting to assess the risk due to all types of dangerous
goods that cross the tunnel.
As seen in the graph, the curve falls within the ALARP region. Being away from the curve
Total Traffic HGV ratio HGV Traffic
- - - SOUTH NORTH SOUTH NORTH CAR HGV
[veh/h] [-] [veh/h] [-] [-] [veh/h] [veh/h] km/h km/h
Quiet 325 0.02 7 0.0644 0.0244 1 1 126.4 90.5
Normal 1050 0.1 105 0.0644 0.0244 7 3 126.4 90.5
Peak 1553 0.117 182 0.0644 0.0244 12 4 114.5 82
DG-HGV ratio DG-HGV Traffic Speed
AADT (Average Annual Daily Traffic)
veh/day
21190

5. Risk analysis for severe traffic accidents in road tunnels (part II)
Figure 5. F-N curve in the south direction.
of tolerability, the tunnel is safe, however, must be taken into consideration possible risk
mitigation measures, that will be taken only after a cost - benefit analysis to verify the eco-
nomic consistency of operations compared to the reduction of risk that we want achieve.
Furthermore, it was decided to study how the societal risk varies changing an input parameter
at a time. This leads to a better understanding on what data influence more in the risk calcu-
lation using the QRA model.
Moreover, since the changes on the input variables have produced similar results in both
directions, it was decided to report only the diagram relating to the south direction (Figure
6).
Below 4 cases in particular are reported. In the first the accidents frequency involving HGVs
is increased and decreased by a factor of 10 (Figure 7). This variation leads to a vertical
translation of the risk curve, that is, towards higher and lower cumulative frequencies, in
direct proportion to the changes made to the initial values. These results are explained when
considering that the frequency of occurrence of each scenario depends directly on the value
of the incidental frequencies.
In the second, the time necessary to block incoming traffic to the tunnel in case of emergency
is varied (Figure 8). The curves show that after 5 minutes from the occurrence of any accident
scenario no longer any risk reduction is possible, even if the incoming traffic is deviated. This
is because the model when calculates the number of victims considers as the main parameter
the linear density of users on the road section concerned. This value depends mainly on the
traffic jam length upstream of the accident, which, apparently, is formed in the first 5 minutes
following the occurrence of the scenario.
In the third case, the number of lanes is varied (Figure 9): increasing the number of lanes, the
curve moves to a higher number of victims. In fact, increasing this number also increases the
density of users in the area occupied by the traffic jam.
In the fourth case, the LPG traffic is moved from vehicles equipped with tanks in vehicles
carrying such DGs in cylinders (Figure 10). This variation results in a significant reduction
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
EV [Fat/year] 1.68E-02

6. C. Di Santo, K. Gkoumas
Figure6.Sensitivityanalysisresults(SOUTH).

7. Risk analysis for severe traffic accidents in road tunnels (part II)
of risk, because in the model it is assumed that in case of accident it is possible at maximum
the breaking of 2 cylinders simultaneously. This leads to an explosion that causes a number
of victims lower compared to the explosion that is generated after the rupture of a LPG tank.
Figure 7. Sensitivity of the model to the parameter "HGVs accident frequency".
Figure 8. Sensitivity of the model to the parameter "Delay for stopping approaching traffic".
1.00E-07
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
initial curve
facc x 10
facc x 10-1
Tollerable Risk Line
Acceptable Risk Line
FCUM[acc/year]
N [FAT]
1.00E-07
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
initial curve
Delayfor Stopping Approaching Traffic = 1 min
Delay for Stopping Approaching Traffic = 5 min
Tollerable Risk Line
Acceptable Risk Line
Delayfor Stopping Approaching Traffic = 10 min
Delayfor Stopping Approaching Traffic = 2 min
Delayfor Stopping Approaching Traffic = 3 min
Delayfor Stopping Approaching Traffic = 4 min
FCUM[acc/year]
N [FAT]

8. C. Di Santo, K. Gkoumas
Figure 9. Sensitivity of the model to the parameter "Number of Lanes".
Figure 10. Sensitivity of the model to the parameter "LPG ratio".
1.00E-07
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
initial curve
Number of Lanes (for every section and in both directions) = 1
Number of Lanes (for every section and in both directions) = 3
Tollerable Risk Line
Acceptable Risk Line
FCUM[acc/year]
N [FAT]
1.00E-07
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
initial curve
Propane in Bulk ratio =Propane in Cylinder ratio =0.15
Propane in Cylinder ratio = 0.30
Tollerable Risk Line
Acceptable Risk Line
FCUM[acc/year]
N [FAT]

9. Risk analysis for severe traffic accidents in road tunnels (part II)
4. CONCLUSIONS
The risk analysis carried out shows that the level of risk inherent of the studied gallery is
acceptable. However, because the risk curve falls within the ALARP region, it is necessary
to evaluate possible measures to mitigate risk, which will be applied only if is proven that the
benefits they bring in terms of reducing the risk are proportional to the cost needed to imple-
ment these measures.
Moreover, the tests carried out during sensitivity analysis show that the margin of safety is
high, since that in no test has been exceeded the limit of tolerability reported in the Legislative
Decree 264/06.
However, the sensitivity study shows the parameters that most affect the risk curve F-N:
• Parameters that define the value of the Road Users Density in the fluid traffic (DRUF) and
in the traffic jam (DRUJ) when a scenario “s” occurs: Total traffic [veh/h], Bus and Coaches
ratio (percentage of buses on the total traffic), Number of lanes, Average vehicle occu-
pancy.
• Parameters that influence the Traffic Jam Length (LJAM) before the scenario “s” occurs:
Delay for stopping approaching traffic (time needed to block the incoming traffic in the
specific section) [s].
• Parameters that define the value of the frequency of occurrence of the scenario s (involv-
ing DGs of type k) on the section i: DG-HGV traffic [veh/h], HGV ratio (percentage of
HGVs on the total traffic), Annual frequency of accidents involving HGVs on the section
i [acc/(MVkm*year)], Proportion of each DG type in the whole DG traffic. These param-
eters have a great influence on the expression used by the model to calculate the frequency
of occurrence of the scenario s:
𝑓𝑓𝑖𝑖𝑖𝑖 𝑖𝑖 = 𝑃𝑃𝑖𝑖𝑖𝑖 𝑖𝑖 ∙ 𝑓𝑓𝑎𝑎𝑎𝑎𝑎𝑎_𝐷𝐷𝐷𝐷,𝑖𝑖 ∙ (𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 ∙ 𝐿𝐿𝑖𝑖 ∙ 24 ∙ 365 ∙ 10−6) (2)
ACKNOWLEDGEMENTS
The authors thank: Franco Bontempi (Professor of Structural Analysis and Design at Univer-
sity of Rome “La Sapienza”), Giordana Gai (Student of PhD in Structural Engineering from
the University of Rome "La Sapienza"), Francesco Petrini (Researcher in Structural Engi-
neering 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
Central Design Management ANAS S.p.A.: Geometric and functional characteristics of the Road
Tunnels. 2009.
Impresa Pizzarotti & C. S.p.A.: Affidamento dell’opera di completamento del tratto stradale catania
siracusa con caratteristiche autostradali, compreso tra le localita’ passo s. martino ed Il Km 130+400
Della Ss. 114, Analisi Di Rischio. 2007.
PIARC, OECD: Safety in Tunnels, Transport of dangerous goods through road tunnels. OECD
Publications, 2001.