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An increasing number of ships have chosen the suitable route to transport in Arctic waters during summer. Seeking a suitable model for risk decision-making in route planning is a necessary research topic at present. Due to its complex natural environment, there is significant uncertainty regarding ship navigation safety in Arctic waters. The process risk-based decision-making method to support route planning is established based on the dynamic Bayesian network (DBN) risk assessment model for LNG carrier collision with ice or obstacles in Arctic waters. The decision-making process for ship navigation is dynamically associated with time. Therefore, a Markov Chain (MC) is built for each dynamic node in Bayesian belief network (BBN) to realize DBN associated risk assessment, which is called process risk and is applied to decision-making. Three possible routes for ships sailing from the Vikitsky Strait to the Long Strait in Arctic waters were selected in conjunction with the objective daily change data of wind speed, temperature, wave height, and ice condition. Simulations for risk decision-making in the ship navigation process are performed. Application examples show that the ship selected either ROUTE2 (Vikitsky Strait – Laptev Sea – Sannikov Strait – Eastern Siberian Sea – Long Strait) or ROUTE3 (Vikitsky Strait – Laptev Sea – Proliv Dmitriya Lapteva – Eastern Siberian Sea – Long Strait) in August as the best navigable route.
Zhuang Li; Shenping Hu; Guoping Gao; Chenyang Yao; Shanshan Fu; Yongtao Xi. Decision-making on process risk of Arctic route for LNG carrier via dynamic Bayesian network modeling. Journal of Loss Prevention in the Process Industries 2021, 71, 104473 .
AMA StyleZhuang Li, Shenping Hu, Guoping Gao, Chenyang Yao, Shanshan Fu, Yongtao Xi. Decision-making on process risk of Arctic route for LNG carrier via dynamic Bayesian network modeling. Journal of Loss Prevention in the Process Industries. 2021; 71 ():104473.
Chicago/Turabian StyleZhuang Li; Shenping Hu; Guoping Gao; Chenyang Yao; Shanshan Fu; Yongtao Xi. 2021. "Decision-making on process risk of Arctic route for LNG carrier via dynamic Bayesian network modeling." Journal of Loss Prevention in the Process Industries 71, no. : 104473.
Sustainable growth should not only be beneficial to the shipping industry in the future, but is also an urgent need to respond to resource and environmental crises and strengthen shipping governance. Maritime traffic in Arctic waters is prone to encounter dangerous ice conditions, and it is essential to study the mechanism of ice collision risk formation in relation to ice conditions. Taking the ship-ice collision risk in Arctic waters as the research object, we propose a dynamic assessment model of ship-ice collision risk under sea ice status dynamic association (SDA) effect. By constructing the standard paradigm of risk factor dynamic association (DA) effect, taking SDA as the key association factor. Combing with other risk factors that affect ship-ice collision accidents, the coupling relationship between risk factors were analyzed. Then, using the Bayesian network method to build a ship-ice collision accident dynamic risk assessment model and combing with the ice monitoring data in summer Arctic waters, we screen five ships’ position information on the trans-Arctic route in August. The risk behavior of ship-ice collision accidents on the selected route under SDA is analyzed by model simulation. The research reveal that the degree of SDA is a key related factor for the serious ice condition and the possibility of human error during ship’s navigation, which significantly affects the ship-ice collision risk. The traffic in Arctic waters requires extra vigilance of the SDA effect from no ice threat to ice threat, and continuous ice threat. According to the ship-ice collision risk analysis under the SDA effect and without SDA effect, the difference in risk reasoning results on the five stations of the selected route are 32.69%, −32.33%, −27.64%, −10.26%, and −30.13% respectively. The DA effect can optimize ship-ice collision risk inference problem in Arctic waters.
Zhuang Li; Shenping Hu; Guoping Gao; Yongtao Xi; Shanshan Fu; Chenyang Yao. Risk Reasoning from Factor Correlation of Maritime Traffic under Arctic Sea Ice Status Association with a Bayesian Belief Network. Sustainability 2020, 13, 147 .
AMA StyleZhuang Li, Shenping Hu, Guoping Gao, Yongtao Xi, Shanshan Fu, Chenyang Yao. Risk Reasoning from Factor Correlation of Maritime Traffic under Arctic Sea Ice Status Association with a Bayesian Belief Network. Sustainability. 2020; 13 (1):147.
Chicago/Turabian StyleZhuang Li; Shenping Hu; Guoping Gao; Yongtao Xi; Shanshan Fu; Chenyang Yao. 2020. "Risk Reasoning from Factor Correlation of Maritime Traffic under Arctic Sea Ice Status Association with a Bayesian Belief Network." Sustainability 13, no. 1: 147.
Maritime pilotage is an important guarantee for the safety of water traffic in port. The pilot is affected by the complex port environment, the differences of crew and equipment of different ships, the physical and psychological pressure of the pilot himself, as well as the management factors from the pilot station and maritime safety administration. In order to avoid pilotage accidents (PAs), it is necessary to study the coupling effect of human-organizational factors (HOFs) on PAs. In this paper, from the perspective of HOF risk coupling in pilotage, the problem of HOF risk coupling in maritime pilotage is studied by using the hierarchical classification idea of the human factors analysis and classification system (HFACS) and the method of system dynamics (SD). First of all, HFACS is used to analyse the HOF risk causal elements (RCEs) in pilotage, and 70 RCEs are summed up in four layers; secondly, the SD coupling model of RCEs is constructed; finally, based on a dataset of PAs collected by the Shanghai Harbour Pilot Association, the coupling simulation of RCEs in pilotage is carried out, and the volatility is evaluated. In general, the safety situation of maritime pilotage has been improving in the Shanghai port. However, four RCEs (negligence, habit, pilotage experience, and violations) in unsafe acts and two RCEs (teamwork and personal safety awareness) in precondition for unsafe acts contribute the most to maritime PAs and need to be paid attention to.
Xinxin Zhang; Weijiong Chen; Yongtao Xi; Shenping Hu; Lijun Tang. Dynamics Simulation of the Risk Coupling Effect between Maritime Pilotage Human Factors under the HFACS Framework. Journal of Marine Science and Engineering 2020, 8, 144 .
AMA StyleXinxin Zhang, Weijiong Chen, Yongtao Xi, Shenping Hu, Lijun Tang. Dynamics Simulation of the Risk Coupling Effect between Maritime Pilotage Human Factors under the HFACS Framework. Journal of Marine Science and Engineering. 2020; 8 (2):144.
Chicago/Turabian StyleXinxin Zhang; Weijiong Chen; Yongtao Xi; Shenping Hu; Lijun Tang. 2020. "Dynamics Simulation of the Risk Coupling Effect between Maritime Pilotage Human Factors under the HFACS Framework." Journal of Marine Science and Engineering 8, no. 2: 144.
An approach based on the hidden Markov model (HMM) is proposed for risk performance reasoning (RPR) for the bauxite shipping process by Handy carriers. The unobservable (hidden) state process in the approach aims to model the underlying risk performance, while the observation process was formed from the time series of risk factors. Within the framework, the log-likelihood probability was used as the measure of similarity between historical and current data of risk reasoning factors. Based on scalar quantization regulation and risk performance quantization regulation, the RPR approach with different step sizes was conducted on the operational case, the performance of which was evaluated in terms of effectiveness and accuracy. The reasoning performance of the HMM was tested during the validation period using three simulated scenarios and one accident scenario. The results showed significant improvement in the reasoning capacity, and satisfactory performance for numerical risk reasoning and categorical performance reasoning. The proposed model is able to provide a reference for risk performance monitoring and threat pre-warning during the bauxite shipping process.
Jianjun Wu; Yongxing Jin; Shenping Hu; Jiangang Fei; Yuanqiang Zhang. Approach to Risk Performance Reasoning with Hidden Markov Model for Bauxite Shipping Process Safety by Handy Carriers. Applied Sciences 2020, 10, 1269 .
AMA StyleJianjun Wu, Yongxing Jin, Shenping Hu, Jiangang Fei, Yuanqiang Zhang. Approach to Risk Performance Reasoning with Hidden Markov Model for Bauxite Shipping Process Safety by Handy Carriers. Applied Sciences. 2020; 10 (4):1269.
Chicago/Turabian StyleJianjun Wu; Yongxing Jin; Shenping Hu; Jiangang Fei; Yuanqiang Zhang. 2020. "Approach to Risk Performance Reasoning with Hidden Markov Model for Bauxite Shipping Process Safety by Handy Carriers." Applied Sciences 10, no. 4: 1269.
Liquefied nature gas (LNG) is a green energy. LNG-fueled vessels are extremely complex engineering systems. In view of the inherent hazardous properties of LNG fuel, LNG fueling is not only an important part, but it is also full of high risks in the operation of LNG-fueled vessels (LNGFVs). Therefore, it is necessary to study the risk factors, and the intrinsic relationship among them between the LNG and the vessel, and to simulate the system dynamics in the process of LNGFV operation. During the process of fueling of LNGFV, at every moment the vessel interacts with the energy and information of the surrounding environment. First, the impact of the three interactions of the fueling operation process, ship factors, and environmental factors were analyzed on the risk of fueling operation, and a complete node system was proposed as to the complex system dynamics mode. Second, by analyzing the boundary conditions of the system, the relationship of factors was established via the tools of system dynamics (SD). Based on the catastrophe theory (CA), the dynamics model for the fueling of LNG is set up to study the system’s risk mutation phenomenon. Third, combined with the simulation results of the case analysis, the risk evolution mode of the LNGFV during the fueling process was obtained, and constructive opinions were put forward for improving the safe fueling of the LNGFV. Application examples show that formal description of risk emergence and transition is a prerequisite for the quantitative analysis of the risk evolution mode. In order to prevent accidents, the coupling synchronization of risk emergence should be weakened, and meanwhile risk control should be implemented.
Shaoyong Xuan; Shenping Hu; Zhuang Li; Wei Li; Boyin Li. Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models. Journal of Marine Science and Engineering 2019, 7, 299 .
AMA StyleShaoyong Xuan, Shenping Hu, Zhuang Li, Wei Li, Boyin Li. Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models. Journal of Marine Science and Engineering. 2019; 7 (9):299.
Chicago/Turabian StyleShaoyong Xuan; Shenping Hu; Zhuang Li; Wei Li; Boyin Li. 2019. "Dynamics Simulation for Process Risk Evolution on the Bunker Operation of an LNG-fueled Vessel with Catastrophe Mathematical Models." Journal of Marine Science and Engineering 7, no. 9: 299.
China imports a large quantity of bauxite each year. Bauxite in fine particles with high moisture has a high risk of liquefaction during the maritime transportation process, which is harmful to the stability and safety of the carrier. To ensure safe shipping, it is necessary to pay attention to the effects of the operation of cargo, the ship’s maneuvering and the ocean environment during the whole transportation process. The simulation of the process risk helps to develop measures to intervene with the cargo behavior to keep the risk to an acceptable level. This study examined the transportation process of a bauxite carrier using the Markov Chain method at different stages of loading, unberthing, departure and sea navigation. Based on the risk transfer matrix of the operational status at different stages of transportation, a cloud simulation model was developed to analyze the transportation process risk of a ship carrying bulk bauxite. Results: the research revealed that the risk evolution rule of the solid bulk cargoes with potential liquefaction during the transportation process, especially bauxite. The risk alteration during the prophase of the transportation process conforms to the rule of the “spoon curve”. Conclusions: a simulation model of the process risk based on the Markov Chain Cloud is suitable for the simulation analysis of the transportation risk of the bulk bauxite carrier. The outcomes of this study may contribute to better safety management to prevent the occurrence of ship capsizing.
Jianjun Wu; Shenping Hu; Yongxing Jin; Jiangang Fei; Shanshan Fu. Performance Simulation of the Transportation Process Risk of Bauxite Carriers Based on the Markov Chain and Cloud Model. Journal of Marine Science and Engineering 2019, 7, 108 .
AMA StyleJianjun Wu, Shenping Hu, Yongxing Jin, Jiangang Fei, Shanshan Fu. Performance Simulation of the Transportation Process Risk of Bauxite Carriers Based on the Markov Chain and Cloud Model. Journal of Marine Science and Engineering. 2019; 7 (4):108.
Chicago/Turabian StyleJianjun Wu; Shenping Hu; Yongxing Jin; Jiangang Fei; Shanshan Fu. 2019. "Performance Simulation of the Transportation Process Risk of Bauxite Carriers Based on the Markov Chain and Cloud Model." Journal of Marine Science and Engineering 7, no. 4: 108.
Many causal factors to marine traffic accidents (MTAs) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to accident mechanisms, the complex structural chains on causes to MTA systems were analyzed by combining the human failure analysis and classification system (HFACS) with theoretical structural equation modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of a MTA, and the constituent elements of the causes of the accident were conducted. Second, a hypothetical model of human factors classification was proposed by applying the practice of the structural model. Third, with the data resources from ship accident cases, this hypothetical model was discussed and simulated, and as a result, the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behavior. Application examples show that relationships in the HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.
Shenping Hu; Zhuang Li; Yongtao Xi; Xunyu Gu; Xinxin Zhang. Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling. Journal of Marine Science and Engineering 2019, 7, 96 .
AMA StyleShenping Hu, Zhuang Li, Yongtao Xi, Xunyu Gu, Xinxin Zhang. Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling. Journal of Marine Science and Engineering. 2019; 7 (4):96.
Chicago/Turabian StyleShenping Hu; Zhuang Li; Yongtao Xi; Xunyu Gu; Xinxin Zhang. 2019. "Path Analysis of Causal Factors Influencing Marine Traffic Accident via Structural Equation Numerical Modeling." Journal of Marine Science and Engineering 7, no. 4: 96.