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ICTIS.2017.8047843

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2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
Pre-warning System Analysis on Dynamic Risk of
Ship Collision with Bridge at Restricted Waters
C. Huang, S. Hu, F. Kong, Y. Xi
Merchant Marine College
Shanghai Maritime University
Shanghai, China
changhai406@126.com
Bridge during a vessel impact through numerical and
theoretical analyses.
Abstract—Risk pre-warning of ship collision with bridge
(SCB) for bridge safety and less SCB play a significant role in
prevention and control of marine traffic accident. The risk
elements of SCB are recognized, classified, and risk elements
system of SCB is also established. Risk pre-warning models of
SCB is developed based on fuzzy inference system (FIS),
including a pre-warning model of inherent risk, a model of risk
increasing correction factor and a model of risk-reducing factor.
A design proposal for the pre-warning system on the dynamic
risk of SCB is present, and the system is developed. The
simulated experiment is conducted with the pre-warning system
on the dynamic risk of ship collision with Shanghai Yangtze
River Bridge. The experiment shows that pre-warning system on
dynamic risk (PWSDR) based on FIS can assess the whole risk
situation of SCB.
Academia has also conducted much research on the risk
assessment model of SCB. For instance, Bae Y G (2013) [5]
built a risk assessment model for Incheon bridge. Huang C H
(2013) [6] introduced a risk assessment model of the safety
river-sailing in bridge-water areas.
In the literature, there have been a couple of researchers
associated with the design of SCB prevention devices. For the
sake of avoiding increasingly severe accidents caused by ship–
bridge collision, an energy-dissipating crashworthy device,
which consists of hundreds of steel-wire-rope coil (SWRC)
connected in parallel and series, has been developed by Wang
L (2008) [7]. Fang H (2016) [8] studied the Large-scale
Composite Bumper System (LCBS) which was made of Glass
Fiber- Reinforced Polymer (GFRP) skins, GFRP lattice webs,
Polyurethane (PU) foam cores and ceramic particles for bridge
piers against ship collision proposed at Nanjing Tech
University.
Keywords—marine traffic system; ship collision with
bridge(SCB); risk factor system; fuzzy inference system(FIS);
pre-warning system on dynamic risk (PWSDR)
I. INTRODUCTION
With so many rivers, the cross-river bridge building is
growing, frequent accident occurrence of ship collision with
bridge (SCB) cannot be ignored. According to relevant
statistics, there is more than 300 accidents of ship collision
with a bridge in the inland river since 1960 [1]. SCB will cause
to not only ship or bridge damage but also casualties and
pollution; therefore the design and performance of SCB
prevention catch a lot of attention.
Above mainly focus on the study of SCB prevention
equipment and statistic risk assessment of ship collision with
the bridge, these cannot make dynamic tracking and
pre-warning of SCB risk. Such professor as XU H (2011) [9]
put forward ship-bridge collision avoidance monitoring and
early warning system to forecast the SCB risk by using video
processing technology to test operating ship automatically.
However, this system doesn't consider the dynamically
changing navigation and management environment in the
forecast of SCB risk.
In the literature, there have been a couple of researchers
associated with the study of ship collision accidents with the
bridge. Several studies have been carried out to investigate the
SCB probability. Considering the limitation of AASHTO
model, Zhou L (2011) [2] proposed a ship-bridge collision
probability model in different wind and drifted conditions by
adding the drift amount of the yaw angle, wind-induced drift
amount, and flow induced drift amount of three normal
distributions.With the aim of evaluating the risk for ship
collision with Arch-bridge, Miao J (2014) [3] established a
mechanical model for ship-bridge collision based on finite
element method to analyze and study the deformation and
anti-collision capability of the central arch ring for the
particular arch bridge under different working conditions.
In order to avoid SCB accidents, the author changes
management model from accident learning to prevention and
put forward management plan of bridge automatic SCB
prevention. Bridge automatic SCB prevention first finishes
dynamic SCB risk assessment, then identify the ship which has
a grave threat to the bridge and the statue where the bridge has
high SCB risk; that is to say to product a real-time evaluation
of dynamic SCB risk and publish pre-warning signals.
Different type of response management can be taken according
to a different type of pre-warning signals.
By identifying and classification of SCB risk factors, the
author establishes the risk elements system of SCB. Then the
risk assessment models of SCB is developed based on fuzzy
inference system (FIS), including assessment model of inherent
risk, a model of risk increasing correction factor and a model of
The investigation of mechanisms of the failure of the bridge
foundation under vessel impact has been an important topic in
the SCB accidents investigation studies. Lu Y E (2013) [4]
studied a possible progressive failure process of Jiujiang
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2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
risk-reducing correction factor. A design proposal for the
pre-warning system on the dynamic risk of SCB is present, and
the system is developed. The simulated experiment is
conducted with the pre-warning system on the dynamic risk of
ship collision with Shanghai Yangtze River Bridge.
III. RISK PRE-WARNING MODELS OF SCB
A. Pre-warning Model of Inherent Risk Based on Fuzzy
Inference System (FIS)
1) Fuzzy Inference System (FIS)
Fuzzy inference is an approximate reasoning method of
bionic behavior and mainly be used to settle complicate
reasoning problems with the fuzzy phenomenon. Because of
the ubiquity of fuzzy phenomenon, the fuzzy inference has
been used a lot. Currently, it has been successfully employed in
following areas, such as automatic control, data processing,
decision-making analysis, mode recognition and many others.
Functionally, fuzzy inference is composed of fuzzification,
fuzzy rule base, fuzzy reasoning method and defuzzification
[11]
.
II. IDENTIFICATION OF RISK ELEMENTS OF SCB
The risk of SCB is systematic, dynamic and consistent. The
composite element of SCB risk system is real-time changing so
that it can determine system risk analysis objects according to
the requirements of risk analysis. Therefore, risk analysis of
SCB is a systematic analysis of dynamic risk. Of course, its
systematic risk elements are dynamically characterized and
have collectability. Dynamic characteristics refer to real-time
changing elements in the bridge area including ship, seaman,
the meteorological, hydrological information of the waters and
relevant management elements. Though the bridge is also the
risk source of SCB, it is settled as a fixed object for a specified
period of time, so it doesn't be classified into dynamic
information. Collectability means information collected
through relevant monitoring equipment or on-spot report, such
as collectible ship information, meteorological, hydrological
information, and related management information and so on.
These factors can be shown in Figure 1.
Because vessel traffic system in bridge waters is a big
complicated system comprised of people, machine,
environment, and management. These factors have a different
effect on the vessel traffic safety in bridge waters, and there
will be some relations among these factors, both impact and
relationships can never be accurately quantitative. Also to
realize dynamic pre-warning, fuzzy inference method is used.
Using FIS is to realize quantitative assessment of vessel
navigation risk.
Unattended operation on the Bridge
Human
factor
Inaction
No action taken by the operator
Unintentional
Mis-action
Unawareness of the problem
Unfamiliar with bridge environment
and navigation requirements
Negligence or mistake
Intentional
Mis-action
Information communication barriers
Overestimate driving ability
Overestimate navigable dimension
Main engine or rudder failure
Vessel
Factor
the first class of hazard
2) Risk Assessment Models of SCB Based on Fuzzy
Inference
With the basis of not affecting evaluate results and
professors' opinions; this paper adopts appropriate and easily
recognizable evaluation indexes to build risk (inherent risk)
assessment model of SCB and real-time risk correction model,
both based on FIS. Figure 2 is the logic model of inherent risk
assessment based on FIS. The inherent risk ( RI ) assessment
model of SCB includes dynamic vessel parameters, bridge
impact factor, natural environment, transportation environment
and many other risk assessment subsystems.
Mechanical
failure
Ship type &
dimensions
Electronic
Equipment
failure
Company
management
Natural Factors
Environment
Factor
Navigation
factors
Bridge factors
Management
Factor
the Second class of
hazard
Bridge
management
Maritime
management
Mooring system failure
Cable handling system failure
Radar, AIS, GPS failure
Other electronic equipment failure
Wind
Meteorological factors
Visibility(fog, rain, haze)
Vessel
length
Tonnage
Wave
Hydrological factors
Current
Traffic flow density
Ship location
Clear height, clear width, etc.
Vessel dimensions FIS
Vessel
age
Vessel
type
Load status
Angle between bridge axis and current course
Navigational aids
Vessel stateFIS
Wind
Angle
Server of management organization
Water supervision & management power
Surplus width/
Clearance width
Meteorological
condiftion-FIS
Inherent risk of SCB-FIS
Visibility
Natural
environment-FIS
Vessel in pre-warning area
Traffic density of
supervised area
Base construction
Bridge impact factorFIS
Wind-FIS
Current-FIS
Laws and regulations construction
Vessel dynamic FIS
UKC/Draft
Current
speed
Current
angle
Emergency capabilities
Vessel
speed
Surplus height/
Clearance height
Wind Scale
Management measures
Government
management
Vessel static-FIS
Traffic
environment-FIS
Fig.1. Risk Factors of SCB
Fig.2. Logic model of assessment on inherent risk of SCB based on FIS
Collected elements showed in Figure 1 includes two levels,
namely first level hazard, and second level hazard. First level
hazard includes people, ship and environment [10]. Second level
hazard includes management elements such as government
management, the management of bridge owner and
management unity, as well as marine management.
B. Risk Correction Model of SCB
1) Risk Reducing Correction Factor Model
The setting of SCB equipment, various management
measures and actions can actually decrease the risk of SCB. In
the model, above factors are called Boolean Parameter (that is
"yes" or "no"). The final results of adding up all risk reducing
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2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
factors multiply risk reducing ratio factor ( D R ), then it comes
to risk reducing correction factor f R . Normally, D R values
as [0, 0.2], and it is estimated at 0.05 in the text.
RI (1 f I f R ) .
IV. PWSDR DESIGN OF SCB
A. Data Collection and Input of PWSDR
∑
Risk reducing correction
factor
Communication
˄ship report˅
Anti-collision
device
Operation
supervision
RD
DR
Site supervision
Emergency
management
Course
Vessel length
Draft
AIS
UKC
Vessel type
Vessel speed
IMO/MMSI
Fig.3. Risk-reducing correction factor model
Vessel width
Clear width
Tonnage
Navigation
span
dimensions
Construction time
2) Risk Increasing Correction Factor Model
Lloyds Casualty Archive
Unexpected vessel
Vessel height
Clear Height
Channel depth
Dragging of Anchor
Perfect navigational
facilities
∑
Navigational facilities
maintenance
DI
Tug configuration
Meteorological &
hydrological
sensors/WIS/VHF
report
Risk increasing
correction factor
Cable Breaking
Wind scale
Wind direction
Wind angle
Current speed
Current course
Current Angle
Visibility
Aids to navigation
Base configuration
Unexpected vessel
Perfect laws &
regulations
Dragging of anchor
VHF/VTS/CCTV
Cable breaking
Communication and confirmation
Fig.4. Risk increasing correction factor model
Supervision of law enforcement
Unexpected vessel as well as imperfection of various
aid-to-navigation, emergency measures and related laws and
regulations both can increase the risk of SCB.
is “NOT”
operation, that is, if put in 0, it comes to 1 and vice versa;
is “OR” operation, that is, if not all input is 0, then it comes to
1. Summation of all factor value after their Boolean calculation
multiply risk increasing ratio factor ( D I ), it comes to risk
increasing correction factor f I . Normally, D I is valued at [0,
0.2] and it is valued at 0.05 in the text.
Operation management
Fig.6. Data collection of PWSDR of SCB
The following information can be collected directly from
Automatic Identification System (AIS): ship length, ship width,
draught, ship type, ship speed, International Maritime
Organization number, Maritime Mobile Service Identification
and cause. Water depth, wind scale, wind direction, flow
velocity, flow direction, visibility and other information can be
collected via weather setting, hydrological sensor or connection
of World Meteorological Organization Information System
(WIS) or with the aid of Very High Frequency (VHF) to
consult with on-spot mariner. In Lloyds Maritime database,
through MMSI, can get relative tonnage, time of construction,
ship height and other information of relating the corresponding
ship.
3) Real-time Revised Risk Correction Model of SCB
Inherent risk of SCB
Risk reducing
correction
factor
Realtime risk
of SCB
According to the ship course and draught as well as wind
direction, flow direction, water depth collected via WIS/VHF,
it can conclude wind angle of the chord, flow angle of the
chord, additional depth and other information. According to
ship width in AIS, ship height in Lloyds Maritime database,
known width of the navigable hole, navigable clearance height
and so on, it can conclude additional width and additional
height.
Risk increasing
correction factor
Fig.5. Real-time revised risk assessment model of SCB
Real-time revised risk correction model of SCB is to
operate real-time correction about bridge inherent risk
according to the operation of supervision, communication
device and supervision measures by inherent bridge risk. The
dynamic risk evaluation value RD of SCB can be got by
operating real-time correction about the inherent risk of SCB
based on risk increasing correction factor f I and risk reducing
correction factor f R .
Aid to navigation equipment, ship out of control, dragging
ship, broken cable ship, communication and confirmation of
preventing ship, on-spot law enforcement and supervision
situation, operation and supervision of bridge and operator, all
these information can be collected via VHF report, consulting,
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2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
observation of Vessel Traffic Services or by observation of
Closed Circuit Television and can be put into the system by
hand. Data collection of PWSDR of SCB is shown in Fig 6.
background in the display area. Real-time risk assessment
results judgment, and visual warning module is to judge the
threshold value of system assessment results and to operate
visual warning by highlighting the assessment results which
belong to different threshold range in the red, orange, yellow
and green background.
B. Functional Design of PWSDR of SCB
Pre-warning
Pre-warning System
System on
on Dynamic
Dynamic Risk
Risk of
of
Ship
Ship Collision
Collision with
with Bridge
Bridge
Information
Info
f rmation
acquisition
acquisition &
& input
input
module
modu
d le
Risk
Risk assessment
assessment
module
modu
d le
Table 1. Total risk assessment value & response measures
Traffic
Traff
ffic flow
f ow data
fl
data
processing
processing module
modu
d le
essel identification
identifi
f cation in
in dangerous
dangerous area
area
Vessel
and pre-warning
pre-warning area
area
and
Traff
ffic density
density calculation
calculation of
of tracking
tracking
Traffic
area
area
Dynamic display
display of
of ships
ships in
in bridge
bridge area
area
Dynamic
Risk identification
identifi
f cation &
& visual
visual pre-warning
pre-warning of
of realrealRisk
time risk
risk assessment
assessment result
result
time
Risk identification
identifi
f cation &
& pre-warning
pre-warning of
of risk
risk
Risk
reasoning result
result of
of subsystem
sub
u system
reasoning
Risk identification
identifi
f cation of
of risk
risk factors
f ctors
fa
Risk
Risk reasoning
reasoning of
of subsystem
sub
u system &
& result
result
Risk
display
display
VTS
VTS
eal-time risk
risk assessment
assessment of
of SCB
SCB &
&
Real-time
result display
display
result
Database for
f r risk
fo
risk assessment
assessment
Database
VHF
VHF
Real-time rendering
rendering of
of risk
risk curve
curve
Real-time
Lloyds Casualty
Casualty Archive
Archive
Lloyds
AIS data
data receiving
receiving &
& processing
processing system
system
AIS
CCTV
CCTV
Risk
Risk pre-warning
pre-warning
module
modu
d le
1
[0-40)
Risk
level
Low Risk
2
[40-85)
Medium
Risk
3
[85-100]
High
Risk
Management
Features
Routine
Management
Pre-warning
Management
Emergency
Management
Response Model
No Response
[40-55)
Blue Warning
[55-70)
Yellow Warning
[70-85)
Orange Warning
Red Warning
4) The traffic flow information processing module: It is
mainly composed of bridge area ship dynamic display module,
tracking area traffic flow density calculation module, danger
area and warning area ship identification module. Bridge area
dynamic display can display Shanghai Yangtze River Bridge
and the ships in the bridge area. Tracking traffic flow density
calculation module will set and show tracking areas and also
calculate and process the ships in the tracking area. Danger and
warning area ship identification can establish and display the
danger areas and warning areas, as well as calculate and handle
the ships which are in the warning area and danger area, also
do compulsive red warning to the ships which are in the
warning area or danger area.
Fig.7. Functional design of PWSDR of SCB
PWSDR of SCB is composed of information collection and
the input module, risk assessment module, risk pre-warning
module, traffic flow information managing module, as in figure
7.
1) Information gathering and input module: Mainly is the
collection of man and machine factor, bridge factor and
environment factor as well as the input of management
measures.
According to the overall assessment value and the threshold
value, different colored risk levels can be shown; every risk
level should carry out responding risk warning measures. Total
risk assessment value & response measures can be referred in
Table 1.
2) Risk Assessment Module: Mainly according to the
collection and input risk element data, all subsystem risk
assessment is finished with the use of FIS; the risk results of
the evaluation can be got and displayed on the system interface.
It includes following components: subsystem risk assessment
and its results show, real-time rendering of risk volatility curve,
real-time assessment of SCB and display module. Subsystem
risk assessment and its results display can finish all subsystem
risk assessment and presentation of its results; real-time risk
assessment and display refer to operate real-time inherent risk
assessment of SCB and correct assessment results according to
supervision information in order to finish the dynamic risk
assessment and results in display of SCB; real-time rendering
of risk volatility curve draws the risk volatility curve of FIS
and the real-time risk assessment results.
C. Working Process of PWSDR
The working process of the system is shown in Figure 8;
the system inspects the update information of supervision
equipment and supervision measures at any time. If it doesn't
detect any change of supervision information, it will collect
information regularly. System transfer the collected
information to risk assessment module and assess the inherent
risk of SCB with the help of the realized inherent risk
assessment module. Then it comes to risk correction module to
collect real-time supervision information and correct the
inherent risk according to the confirmation of the
communication of preventing ship, on-spot law-enforcement
officers, supervision information of bridge operator, the
completion of aid-navigation measurements and information of
out-of-control ships, dragging ships, broken-line ships and
others. Then it comes to risk quantification results display and
record module to show inherent risk assessment subsystem and
risk assessment results, and also draw fluctuation curve of
system risk assessment value so that supervisors can know the
changing trend of the risk of SCB. Then it comes to risk
warning module which is to realize the visual warning of a
high-risk state of SCB by different risk assessment values.
3) Risk Warning Module: This module is composed of risk
element judgment module, subsystem risk assessment results
judgment and visual warning module and dynamic risk
assessment results judgment and visual warning module. Risk
element judgment module is to judge the threshold value of
real-time collected dynamic and static risk element information
and to operate visual warning in the display area by
highlighting the high-risk element which beyond set threshold
value in red. Subsystem risk assessment results judgment and
visual warning module are to realize visual warning by judging
the threshold value of all subsystem of assessment results and
to highlight subsystem assessment results which belong to
different threshold range in the red, orange and yellow
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NO.
Assessment
Value
2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
two bridge spans with 258 m long-span on both sides of the
main span; navigable holes on both sides are measured with
146 m width; mid- fairways are located at 494 m away from
the main span center [12]. Danger area, warning area and areas
outside of passage area where are close to the bridge pier for
limited passage area which can be divided into danger area
within 200 m distance, warning area within 400 m distance and
tracking monitoring area within 2000 m, according to the
distance with the bridge pier. The division of different warning
areas is shown in Figure 9.
Start
Supervision &
management
information detection
Supervision &
management
Information change
Y
N
Risk information of
SCB
Risk information
acquisition
Chongming Island
Navigation area
Display of prewarning signal
Dangerous area
B. System Realisation and Simulation Test
With the example of ship collision PWSDR with Shanghai
Yangtze River Bridge, it introduces the realization and
simulation test of the system.
Fig.8. Work procedure of PWSDR of SCB
V. SIMULATION TEST OF PWSDR OF SCB
1) System Realization
On the platform of MATLAB 7.0, fuzzy inference system
at all levels can be realized according to the logic model in
Figure 2 [10, 13-14], confirm following items: linguistic values of
input and output variate of all subsystems and their
membership functions; fuzzy rules and different fuzzy
arithmetic method.
A. The Division of Risk Supervision Area of SCB in Shanghai
Yangtze River Bridge
According to the risk of SCB, supervision area can be
divided into passage area, danger area, warning area and
monitoring area. According to the position of beacon lights set
by the waterways department, central passage area is divided
into an upward channel and downward channel with 292.5 m
wide; fairways are located at the main bridge span. There are
Snapshot of PWSDR of SCB
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Changxing Island
Fig.9. Supervision area partition of Shanghai Yangtze River bridge
End
Fig.10.
Navigation area
Display & record of
risk assessment result
Tracking &
Monitoring
Area
Risk correction
Database of risk
assessment
Pre-warning area
Inherent risk
Assessment
Supervision &
management
information
2017 4th International Conference on Transportation Information and Safety (ICTIS), August 8-10, 2017, Banff, Canada
Realize the data development and input of respond data on
the SQL Server platform.
system operation need to be found and improved in a future
application.
On the platform of Visual Studio, the system interface
design is realized; MapX control can finish the display and
interaction of electronic chart [13,15]. Hybrid programming of
VC and MATLAB can be realized through engine connector to
call all fuzzy inference system on the MATLAB platform at
any time [13,16]. Develop serial port information collection
module to finish the real-time reception of partial information;
Active X Data Object (ADO) [17] is used to access to developed
data on the SQL Server platform so as to conduct data
interaction. The developed system interface is shown in Figure
10.
ACKNOWLEDGMENTS
The authors would like to thank the anonymous reviewers
and editors for their comments and suggestions. The research is
supported by the China Postdoctoral Science Foundation
(2016M591651), the Creative Activity Plan for Science and
Technology Commission of Shanghai (16040501700,
13510501600), the Innovation Foundation of SMU for Ph.D.
Graduates (yc2012067), and the Fostering Foundation for the
Excellent PhD Dissertation of SMU (2013bxlp006).
2) Simulation Test
Dynamic risk assessment and warning system of SCB of
Shanghai Yangtze River is operated to conduct simulation
analysis of dynamic risk assessment and warning of SCB
(real-time). Under the condition of adverse circumstances,
huge ship size, the existence of on-spot law-enforcement
officers and normal supervision of bridge operator, the risk of
SCB is at middle level with risk assessment value of 45 (blue
warning management), shown in Figure 10.
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can draw real-time fluctuation curve of SCB and display its
assessment results and release corresponding warning signal
according to the assessment results.
[4]
[5]
[6]
VI. CONCLUSION
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element is real-time and dynamic. The risk assessment system
based on the two dynamic information can be used to conduct
warning management.
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assessment results of SCB.
[11]
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the overall risk situation through the practical application of
simulation test of Shanghai Yangtze River.
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