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 firstname.lastname@example.org 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)  built a risk assessment model for Incheon bridge. Huang C H (2013)  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) . Fang H (2016)  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 . 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)  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)  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)  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)  studied a possible progressive failure process of Jiujiang 698 978-1-5386-0437-3/17/$31.00 ©2017 IEEE 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  . 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 . 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 699 978-1-5386-0437-3/17/$31.00 ©2017 IEEE 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, 700 978-1-5386-0437-3/17/$31.00 ©2017 IEEE 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 701 978-1-5386-0437-3/17/$31.00 ©2017 IEEE 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 . 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 702 978-1-5386-0437-3/17/$31.00 ©2017 IEEE 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)  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. REFERENCES    It shows from Figure 10 that the system can judge high-risk elements and risk inference of all fuzzy subsystems; the information interaction of supervision at the lower right corner can correct dynamic risk (real-time) of the system. The system can draw real-time fluctuation curve of SCB and display its assessment results and release corresponding warning signal according to the assessment results.    VI. CONCLUSION  The study puts forward dynamic risk assessment system of SCB. This system identifies the characteristics of risk elements of SCB more comprehensively. First, the risk of SCB is constant; second, collected information of risk influencing element is real-time and dynamic. The risk assessment system based on the two dynamic information can be used to conduct warning management.    It establishes the risk of SCB assessment model based on FIS. It puts forward inherent risk model of SCB based on dynamic risk elements and static risk elements. 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