Ship Collision Research Articles

Assessment of the Crew On-Duty Status Based on the Dynamic Probabilistic Risk Platform

Shipping operations are high risk, complex, and uncertain activities. And a ship collision can have catastrophic consequences. The behavior and status of the crew on duty are key factors that affect the navigation safety of the ship. This paper proposes a dynamic probabilistic risk platform of Maritime Accident Dynamics Simulation (MADS) and Information, Decision, and Action in a Crew context cognitive model (IDAC). The platform simulates the cognitive and decision making processes of the crew on duty in maritime incidents and dynamically analyzes the reliability of human decisions. It can predict events and their probability values that may lead to decision failures in knowledge driven task failure scenarios. The effectiveness of the platform is verified through a case study of a ship collision. The results show that the method can simulate dynamic critical scenarios of ship collision avoidance and achieve intelligent decision-making in a human centered system.

Coordinated multi-stage and multi-objective optimization approach for ship collision avoidance decision-making

Appropriate decision-making is essential to prevent ship collisions and further ensure navigational safety at sea. Current research on collision avoidance decision-making (CADM) primarily aims to find limited feasible solutions based on a designated safe distance under the strong assumption that ships take no or little maneuvering actions. To overcome such limitations, a multi-stage and multi-objective CADM optimization approach is proposed to satisfy the requirements of safety and economy, while considering the ship path uncertainties and risk preferences of ship officers. First, a comprehensive collision risk estimation method is formulated by quantifying degree of domain violation and probability of domain violation (PDV) in dynamic and uncertain situations. Then, the CADM for each individual ship is treated as a multi-objective optimization problem, and a three-stage optimization approach outlines the process to find optimal collision avoidance path. Preliminary non-dominated solutions are obtained and filtered to enhance the robustness by an accepted threshold of the PDV. Optimal solutions are selected for ship officers with different risk preferences. Finally, the coordinated collision avoidance path for multi-ship encounter is determined based on the exchange of individual collision avoidance path through several rounds of coordination. The effectiveness of CADM method is validated through extensive case studies. The results show that it can provide feasible solutions for multi-objectives and adaptively determine the most appropriate scheme for ship officers with different risk preferences to make coordinated decisions.

Fast evaluation method of post-impact performance of bridges based on dynamic load test data using Gaussian process regression

Bridges occasionally suffer from the vehicle or ship collision accidents, leading to structural damage and bridge collapse, resulting in severe consequences such as casualties, ship sinking, and vehicle damage. After such accidents, the performance evaluation of bridge structures is significant for bridge maintenance. The bridge's structural performance should be assessed after a collision with a vehicle or ship before regular traffic is resumed. A gray correlation analysis technique was introduced for the swift and efficient assessment of bridge structural performance following impacts. This method aimed to identify the influential parameters associated with bridge structural performance. Utilizing outcomes from dynamic load tests along with the Gaussian process regression model, adjustments were made to the original finite element analysis model. This refinement facilitated precise scrutiny of structural damage and expedited accurate performance evaluations of the bridge. Subsequently, a practical examination was carried out following a ship collision with the Wanjiang Bridge to validate the viability and precision of the proposed approach. A comparison between performance evaluation outcomes derived from the bridge's structural response to ship collision and actual field test results demonstrated the substantial accuracy and computational efficacy of the suggested technique. The proposed method uses a dynamic load test combined with an intelligent algorithm to replace the static load test, effectively solving the expensive, time-consuming, traffic-impeding static load test problem.

Research on Dynamic Path Planning and Tracking Control for Ship Collision Avoidance

Ship collisions are prevalent every year, leading to significant maritime traffic accidents. This paper presents research on dynamic path planning and tracking control for ship collision avoidance by integrating ship automatic avoidance technology to address this issue. We conducted a comprehensive study on artificial potential fields, trajectory tracking, and route trajectory tracking in response to the current state of ship collision avoidance and trajectory tracking. The study employed vector decomposition and slider control as research methods to analyze, optimize, and modify ship collision avoidance methods. Additionally, we carried out collision avoidance simulations using MATLAB to verify the stability and safety of ship trajectory tracking under various methods to advance the research on ship collision avoidance and trajectory. The proposed approach has the potential to significantly reduce ship collisions and enhance ship trajectory safety.

Autonomous Ship Collision Avoidance in Restricted Waterways Considering Maritime Navigation Rules

Autonomous Ship Collision Avoidance in Restricted Waterways Considering Maritime Navigation Rules

A multi-task deep learning model integrating ship trajectory and collision risk prediction

Trajectory prediction can provide position information for ship navigation behavior perception. This is very important for intelligent navigation. However, potential collision risk caused by other ships’ navigation behaviors will directly affect the navigation behavior of own ship. In order to incorporate the potential collision risk caused by the navigation behavior of other ships, this study proposes a Multi-Task Deep Learning Model to exploit the interaction between ships and predict the ship trajectory and potential collision risk simultaneously. Trajectory prediction task can exploit the knowledge contained in collision risk prediction task and the prediction of collision risk can utilize the navigational situation in the future. In the trajectory prediction task, we construct virtual channel center points in a data-driven way to capture the ship behavior in ship traffic flow and use them as constraints in our designed loss function. Furthermore, we design the soft collision risk classification label and the knowledge distillation module to improve the accuracy of the multi-task model. At last, we demonstrate that our method can not only improve the accuracy of trajectory prediction, but also gain reliable potential collision risk results. Our method improves the prediction time scale by an order of magnitude compared with other methods.

Impact of steel grade on a ship colliding with an offshore wind turbine monopile supporting structure

Offshore wind power is experiencing rapid development around the world. The number of installations and their size is growing. Thousands of monopile support structures with diameters of 10 m and weights of 2000 tons will be installed in the coming years. Offshore wind power installations, often located close to the shore and shipping routes, pose a new challenge to ship safety. The study analyses the damages from an accidental collision between a 6500-tonne displacement Supply Offshore Vessel and the monopile support structure with a 10 m diameter and a 15 MW wind turbine. The head-on collision cases are performed according to the DNV-ST-0126, showing the consequences for the ship and the wind turbine support structure. In addition, simulation cases with an offset between the ship's plane of symmetry and the monopile axis showed a new form of damage to the ship's plating. During a head-on sliding collision, extensive plate tearing can occur due to its cutting at the deck line and concertina tearing under the pressure of the monopile. As a result, the hull may open over a large area, including the threat of unsealing the ship's collision bulkhead. The S355 grade steel can significantly reduce collision damage by up to 50% (from 20.6 m2 to 10.5 m2 for the case studied here).

The analysis of crashworthiness and dissipation mechanism of novel floating composite honeycomb structure against ship-OWT collision

High-energy ship collisions can cause catastrophic damage to offshore wind turbines (OWTs). This paper proposes a novel floating composite honeycomb anti-collision structure to reduce damage caused by ship-OWT high-energy collisions. The typical collision scenarios are conducted to investigate the damage analysis of anti-collision structures and the dynamic response of OWT with and without anti-collision structures via ABAQUS/Explicit. Regarding the configuration of the composite honeycomb anti-collision structure, the elastic-plastic theory of sandwich beams is proposed and an elastic-plastic analysis on the sandwich wall honeycomb core structure is performed to investigate its dissipation mechanism and obtain homogeneous material parameters of the cells. The homogenization parameters are incorporated into the ship-OWT anti-collision analysis to evaluate their applicability. The collision simulation results demonstrate that the proposed anti-collision structure is capable of effectively absorbing 80% of the initial kinetic energy and significantly reducing the dynamic response of the structure. In elastic-plastic analysis, the proposed elastic-plastic theory of sandwich beams effectively addresses the elastic-plastic behavior of honeycomb with sandwich walls, yielding results consistent with the stress-strain curve obtained from finite element analysis (FEA). In the coupled analysis, the obtained parameters of the homogenized honeycomb validate its effectiveness and enhance computational efficiency. Valuable results are given for the engineering design.

COLREGs-compliant unmanned surface vehicles collision avoidance based on improved differential evolution algorithm

Unmanned surface vessel (USV) has a wide range of applications in oceanographic research, resource development, environment detection, and security rescue due to its advantages of maneuverability, flexibility, fast response, and intelligence. The ability of USVs to autonomously and effectively avoid obstacles in highly dynamic and uncertain marine environments is a prerequisite for the successful completion of their tasks. Therefore, in this article, a USV collision avoidance based on International Regulations for Preventing Collisions at Sea and the Collision Risk Model with the Improved Differential Evolution Algorithm (CRI-DE) has been considered. Based on the International Regulations for Preventing Collisions at Sea (COLREGs) and common practices of seafarers, an improved ship collision risk model is proposed. Specifically, the model is innovatively combined with the differential evolution algorithm (DE) as a constraint condition to further realize path planning in complex situations. Moreover, chaotic multi-population parallel optimization, parameter adaptive adjustment strategy, and the construction of fitness function based on individual path points are added to the DE. In this way, the ability to escape from local optima and enrich population diversity can be guaranteed. Finally, experiments based on the proposed CRI-DE are conducted and the results indicate the efficiency and effectiveness of the proposed method.

Analysing human error contribution to ship collision risk in congested waters under the evidential reasoning SPAR-H extended fault tree analysis

Human error plays a crucial role in maritime transportation risk analysis, as a significant percentage of accidents, including collisions, groundings, capsizing, fires, and explosions, can be attributed to human errors. However, obtaining a dataset that quantifies human error probabilities for maritime risk analysis is challenging due to commercial constraints. To address this issue, this paper proposes a conceptual framework that integrates evidential reasoning (ER) and the standardized plant analysis risk-human reliability analysis (SPAR-H) method to quantify human errors, while employing fault tree analysis (FTA) to predict risk. The specific focus of this study is ship collision risk in congested waters, which serves as a demonstration case to showcase the proposed method and illustrate a detailed analysis of collision risk. The findings reveal that “inadequate watchkeeping due to sole lookout”, “improper RADAR monitoring”, and “ineffective execution of COLREG-related actions” are the most significant human errors contributing to collision risk in congested waters. The outcomes of this research provide valuable insights for ship owners, safety professionals, ship masters, inspectors, and researchers in the maritime industry. The findings can assist in minimizing collision risk and improving navigational safety in congested waters.

Decision-making algorithm for ship collision avoidance with collision risk map

This paper presents a decision-making algorithm for ship collision avoidance. First, an algorithm for the planar distribution of collision risk is described. The level of collision risk was calculated as a function of the plane coordinates based on the predicted encounter state. It enables the visualization of all the potential collision risks as a collision risk map and the calculation of the integrative collision risk against several ships, considering COLREGs. Second, an optimal route-planning algorithm that can consider the maneuvering characteristics of the ship is proposed. Because a ship cannot change its course and speed suddenly, the nodes and their connections can be set to limit the course and speed change amounts to appropriate ranges. The optimal route was obtained as the minimum-cost route along node connections. Finally, a decision-making algorithm for ship collision avoidance was developed by combining the proposed collision risk mapping and optimal route-planning algorithm. It was applied to representative encounter situations and evaluated by an evaluation method. Further, the proposed algorithm was compared with state-of-art algorithms. Thus, safe and efficient decision-making for collision avoidance can be achieved even in congested situations by appropriately changing both a course and a speed depending on the encounter situation.

Study on the Evaluation of Ship Collision Risk based on the Dempster－Shafer Theory

Study on the Evaluation of Ship Collision Risk based on the Dempster－Shafer Theory

Online Estimation of Ship Dimensions by Combining Images with AIS Reports

Ship dimensions are an important component of static AIS information, and are a key factor in identifying the risks of ship collisions. We describe a method of extracting and correcting ship contour information using inland waterway surveillance video combined with AIS information that does not depend on ship dimension data. A lightweight object detection model was used to determine the ship’s position in an image. Dynamic AIS information was included to produce multigroup control points, solve the optimal homography matrix, and create a transformation model to map image coordinates onto water surface coordinates. A semantic segmentation DeepLabV3+ model was used to determine ship contours from the images, and the actual dimensions of the ship contours were calculated using homography matrix transformation. The mAP of the proposed object detection model and the MIoU of the semantic segmentation model were 86.73% and 91.07%, respectively. The calculation error of the ship length and width were 5.8% and 7.4%, respectively. These statistics indicate that the proposed method rapidly and accurately detected target ships in images, and that the model estimated ship dimensions within a reasonable range.

A Hybrid Probabilistic Risk Analytical Approach to Ship Pilotage Risk Resonance with FRAM

Collision risk in ship pilotage process has complex characteristics that are dynamic, uncertain, and emergent. To reveal collision risk resonance during ship pilotage process, a hybrid probabilistic risk analysis approach is proposed, which integrates the Functional Resonance Analysis Method (FRAM), Dempster–Shafer (D–S) evidence theory, and Monte Carlo (MC) simulation. First, FRAM is used to qualitatively describe the coupling relationship and operation mechanism among the functions of the pilotage operation system. Then, the D–S evidence theory is used to determine the probability distribution of the function output in the specified pilotage scenario after quantitatively expressing the function variability, coupling effect, and the influence of operation conditions through rating scales. Finally, MC simulation is used to calculate the aggregated coupling variability between functions, and the critical couplings and risk resonance paths under different scenarios are identified by setting the threshold and confidence level. The results show that ship collision risk transmission is caused by function resonance in the pilotage system, and the function resonance paths vary with pilotage scenarios. The critical coupling ‘F2-F7(I)’ emerges as a consistent factor in both scenarios, emphasizing the significance of maintaining a proper lookout. The hybrid probabilistic risk analytical approach to ship pilotage risk resonance with FRAM can be a useful method for analysing the causative mechanism of ship operational risk.

An analytical method for predicting the structural response of ship side structures by bulbous bow in oblique collision scenarios

ABSTRACT As more and more ships sail on the sea, the probability of collision between ships also increases. At present, researches more on head-on ship collisions than oblique ship collisions. According to statistical data, the probability of oblique ship collision is higher than that of head-on ship collision, and oblique ship collision may cause a wider range of structural damage. Therefore, this paper studies the damage deformation of ship side structures in oblique collision scenarios. In this paper, a simplified analysis method is proposed to predict the structural response of the struck ship’s side structures by bulbous bow in oblique collision scenarios and the analytical results match well with the numerical simulation results, which verifies the accuracy of the simplified analysis method. The simplified analysis method can be used to assess the crashworthiness of ship side structures by bulbous bow in oblique collision scenarios.

A novel deep reinforcement learning for POMDP-based autonomous ship collision decision-making

A novel deep reinforcement learning for POMDP-based autonomous ship collision decision-making

Analysis of ship accident resulting from bad weather conditions in the port of Khor Al-Zubair, Iraqi crane accident Aba Thar: a case study

The Arabian Gulf region is regarded as one of the world's most significant commercial areas, with large-scale and continuous marine shipments of commodities and goods to and from the Gulf. Iraqi ports are one of the most important ports in the Gulf, and they are a vital source of revenue for the Iraqi economy because of the money they bring in, as any stoppage of operations or damage to the ports may lead Iraq to lose a significant amount of money and halt shipping operations. Maritime accidents are one of the most significant concerns that have resulted in the suspension of maritime navigation and loading and unloading activities in Iraqi ports since there are various forms of marine accidents and many causes. With this study, we will look at maritime accidents caused by bad weather, as there have been many recent marine mishaps and ship collisions caused by bad weather, resulting in the loss of life and equipment and halting marine activities. We'll look at a few incidents, the meteorological conditions that contribute to them, and the link between them and the most likely cause of the accident. We will study an accident that occurred in the port of Khor Al-Zubair in 2018, which is considered one of the most important ports of Iraq, as it contains berths for importing and exporting goods, as well as on many of the Iraqi oil export berths, the accident of the Iraqi crane, Aba Thar. This report advises Iraqi port officials to limit activity in severe weather to reduce accidents that lead to loss of life and equipment and significant economic losses to the country. Keywords: Khor Al-Zubair, Maritime accidents, Aba Thar, Bad weather, The Arabian Gulf

Intelligent ship collision avoidance model integrating human thinking experience

With the maturation of intelligent ship collision avoidance technology, the acceptability of this technology has gained attention and the ship collision avoidance model is confronting interpretability and trustworthiness difficulties. Therefore, this paper integrates human thinking experience into actual collision avoidance decision-making to propose an intelligent collision avoidance model that is more in line with the operator ‘s understanding, thus improving trustworthiness and autonomy. The general architecture of collision avoidance model is divided into two modules: a basic model and an analytical model, by adopting from human thinking patterns of fast and slow. The basic model considers the COLREGS and navigation experience to solve the encounter situation of two ships. The analytical model addresses the collision avoidance issues in complex scenarios with multi-ships, by introducing the repulsive force and the driving force parts of the social force model, which are used to implement course mapping adjustment and gentle course fluctuation respectively. Additionally, a field theory-based effect evaluation model is designed to blend human experience with algorithmic benefits for making the optimal decision in complex scenarios. The results show that when the basic model and the analytical model work together, this architecture performs effectively in a variety of scenarios. It is worth mentioning that the collision avoidance model can potentially be developed to advance collision-free and human-like process trajectory navigation in multi-ship situations.

An Analytic Model for Identifying Real-Time Anchorage Collision Risk Based on AIS Data

With the increasing volume of ship traffic, maritime traffic safety is facing a great challenge because the traffic in port becomes more and more crowded and complicated, which will make ship collisions more likely to happen. As a special water area of the port, the anchorage is also threatened by collision risk all the time. For accurately assessing the collision risk in anchorage and its adjacent waters in real time, this paper proposed an analytic model based on Automatic Identification System (AIS) data. The proposed anchorage collision risk model was established in microscopic, macroscopic, and complexity aspects, which considered ship relative motion, anchorage characteristics, and ship traffic complexity, respectively. For validation, the AIS data of the anchorages near the Shandong Peninsular were used to carry out a series of experiments. The results show that the proposed model can identify the anchorage collision risk effectively and has an advantage in dealing with complicated scenarios. The proposed anchorage collision risk model can help maritime surveillance better monitor and organize the ship traffic near the port and provide mariners with a reference about the collision risk situation of the anchorage on their route, which are important to improving maritime traffic safety.

Ship collision avoidance control based on improved velocity obstacle method

The velocity obstacle method has extensive research in collision avoidance, which uses circles to represent robots or ships to find a collision-free velocity. To better describe the motion characteristics of ships, an improved velocity obstacle method based on elliptical ship domain is proposed in this paper. In the previous research on collision avoidance, the ship domains of the own ship and the target ship are seldom considered at the same time. This paper considers the ship domains of the own ship and the target ship at the same time and judges whether the ship domains of the two ships overlap based on the generalized characteristic polynomial. The heading angle is discretized to construct the velocity obstacle (VO) by calculating the collision velocity at different heading angles. Velocities on the boundaries of the velocity obstacle are used to find an optimal heading angle. The algorithm is tested in different encounter situations and successfully realizes collision avoidance.