A method for the direct assessment of ship collision damage and flooding risk in real conditions

Zhang, Mingyang; Conti, Fabien; Sourne, Hervé Le; Vassalos, Dracos; Kujala, Pentti; Lindroth, Daniel; Hirdaris, Spyros

doi:10.1016/j.oceaneng.2021.109605

Cited by 101 publications

(35 citation statements)

References 45 publications

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“…It can also be extended to idealize ship collision analysis dynamics by applying spring elements on both striking and struck ships. Such capabilities may be useful for ship damage stability assessment using direct [40] and comparative analysis methods [41]. Simplified method (LS-DYNA)…”

Section: Discussionmentioning

confidence: 99%

A simplified fluid structure interaction model for the assessment of ship hard grounding

et al. 2021

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The structural damage of ships in navigational accidents is influenced by the hydrodynamic properties of surrounding water. Fluid structure interactions (FSI) in way of grounding contact can be idealized by combining commercial FEA tools and specialized hydrodynamic solvers. Despite the efficacy of these simulations, the source codes idealizing FSI are not openly available, computationally expensive and subject to limitations in terms of physical assumptions. This paper presents a unified FSI model for the assessment of ship crashworthiness following ship hard grounding. The method uses spring elements for the idealization of hydrostatic restoring forces in 3 DoF (heave, pitch, roll) and distributes the added masses in 6 DoF on the nodal points in way of contact. Comparison of results against the method of Kim et al. (2021) for the case of a barge and a Ro–Ro passenger ship demonstrate excellent idealization of ship dynamics. It is concluded that the method could be useful for rapid assessment of ship grounding scenarios and associated regulatory developments.

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Section: Discussionmentioning

confidence: 99%

A simplified fluid structure interaction model for the assessment of ship hard grounding

et al. 2021

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“…For example, [34] formulate the robot control as multi-step matrix game and optimized the collision avoidance task by dual linear programming. In terms of collision risk analysis, [35,36] proposed a real-time collision risk assessment method. In [37,38], collision risk factors and traffic complexity in some special navigational waters, such as polar regions and inland rivers, was analyzed.…”

Section: Literature Review 21 Intelligent Collision Avoidance Methodsmentioning

confidence: 99%

A COLREGs-Compliant Collision Avoidance Decision Approach Based on Deep Reinforcement Learning

Wang

Huang

Liu

et al. 2022

JMSE

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It is crucial to develop a COLREGs-compliant intelligent collision avoidance system for the safety of unmanned ships during navigation. This paper proposes a collision avoidance decision approach based on the deep reinforcement learning method. A modified collision avoidance framework is developed that takes into consideration the characteristics of different encounter scenarios. Hierarchical reward functions are established to assign reward values to constrain the behavior of the agent. The collision avoidance actions of the agent under different encounter situations are evaluated on the basis of the COLREGs to ensure ship safety and compliance during navigation. The deep Q network algorithm is introduced to train the proposed collision avoidance decision framework, while various simulation experiments are performed to validate the developed collision avoidance model. Results indicate that the proposed method can effectively perform tasks that help ships avoid collisions in different encounter scenarios. The proposed approach is a novel attempt for intelligent collision avoidance decisions of unmanned ships.

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“…In 2020, Split-Attention Networks (ResNeSt) was proposed ( Zhang M. et al, 2021 ). ResNeSt introduces the Split-Attention block, which consists of a feature map group and split attention operation.…”

Section: Data Analysis and Network Designmentioning

confidence: 99%

Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

Hao

Wang

Bai

et al. 2022

Front. Bioeng. Biotechnol.

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The intelligent monitoring and diagnosis of steel defects plays an important role in improving steel quality, production efficiency, and associated smart manufacturing. The application of the bio-inspired algorithms to mechanical engineering problems is of great significance. The split attention network is an improvement of the residual network, and it is an improvement of the visual attention mechanism in the bionic algorithm. In this paper, based on the feature pyramid network and split attention network, the network is improved and optimised in terms of data enhancement, multi-scale feature fusion and network structure optimisation. The DF-ResNeSt50 network model is proposed, which introduces a simple modularized split attention block, which can improve the attention mechanism of cross-feature graph groups. Finally, experimental validation proves that the proposed network model has good performance and application prospects in the intelligent detection of steel defects.

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A method for the direct assessment of ship collision damage and flooding risk in real conditions

Cited by 101 publications

References 45 publications

A simplified fluid structure interaction model for the assessment of ship hard grounding

A simplified fluid structure interaction model for the assessment of ship hard grounding

A COLREGs-Compliant Collision Avoidance Decision Approach Based on Deep Reinforcement Learning

Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

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