Rule development for container stowage on deck

Wolf, van der Ach; Darie, Ionel; Rathje, Helge

doi:10.1201/b10771-87

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…Back in the 1950s, the first generation of containerships had only two tiers on deck. Today, containerships are routinely carrying containers on deck up to eleven (11) tiers high. As such, lashing on containers becomes increasingly important.…”

Section: Lashing In Containershipmentioning

confidence: 99%

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Lashing Force Prediction Model with Multimodal Deep Learning and AutoML for Stowage Planning Automation in Containerships

Lee

Lee²,

Shin

2020

Logistics

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The calculation of lashing forces on containerships is one of the most important aspects in terms of cargo safety, as well as slot utilization, especially for large containerships such as more than 10,000 TEU (Twenty-foot Equivalent Unit). It is a challenge for stowage planners when large containerships are in the last port of region because mostly the ship is full and the stacks on deck are very high. However, the lashing force calculation is highly dependent on the Classification society (Class) where the ship is certified; its formula is not published and it is different per each Class (e.g., Lloyd, DNVGL, ABS, BV, and so on). Therefore, the lashing result calculation can only be verified by the Class certified by the Onboard Stability Program (OSP). To ensure that the lashing result is compiled in the stowage plan submitted, stowage planners in office must rely on the same copy of OSP. This study introduces the model to extract the features and to predict the lashing forces with machine learning without explicit calculation of lashing force. The multimodal deep learning with the ANN, CNN and RNN, and AutoML approach is proposed for the machine learning model. The trained model is able to predict the lashing force result and its result is close to the result from its Class.

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Section: Lashing In Containershipmentioning

confidence: 99%

“…Besides this, deck containers may be loaded with dangerous goods. Thus, containers going overboard also pose significant environmental implications [11]. According to the report of the World Shipping Council, the industry loses as many as 10,000 containers a year at sea [12].…”

Section: Lashing In Containershipmentioning

confidence: 99%

Lashing Force Prediction Model with Multimodal Deep Learning and AutoML for Stowage Planning Automation in Containerships

Lee

Lee²,

Shin

2020

Logistics

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“…However, there are rare reports on the current research on lashing systems, especially on lashing bridges. For a complete overview about the state of the art of container stowage on deck, refer to the work of Wolf et al 1 Through the study of container stacking, the load condition of the lashing system can be effectively understood. In 2008, Kirkayak and Suzuki 2 first studied the dynamics of two-layer stacking, and the mathematical model of twist-lock was constructed under the action of vertical impulse.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Engineering–based method for containership lashing bridge optimization design and structural improvement with functionally graded thickness plates

Wang

2018

Proceedings of the Institution of Mechanical Engineers, Part M:

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This article presents a Knowledge-Based Engineering application method for the optimization design of containership lashing bridge. To reduce the structural vibration response of lashing bridges, a structural improvement method was proposed based on the results of optimization. The adoption of design specifications, expertise and other knowledge accumulated in design have been ignored in the development of structure design and optimization. To face the increasingly fierce competition in the marine market, the optimal solution is establishing a knowledge base containing expertise, design specification and successful design cases. In addition, the advanced techniques for design and optimization shall be incorporated with Knowledge-Based Engineering to automate new designs and optimizations. Furthermore, finite element method is employed to carry out the strength analysis. Lightweight design and vibration damping design of the lashing bridge are deemed as two critical issues having nagged designers. The Knowledge-Based Engineering–based design and optimization approach not only ensure the performance of the lashing bridge structure to be reasonable but also minimize the weight target and evidently improves the design quality and efficiency. The functionally graded thickness structure is applied to the improved scheme, which not only makes the design of the lashing bridge structure more reasonable but also reduces the vibration response of the structure.

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Experimental and numerical analysis of container multiple stacks dynamics using a scaled model

et al. 2013

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Rule development for container stowage on deck

Cited by 5 publications

References 1 publication

Lashing Force Prediction Model with Multimodal Deep Learning and AutoML for Stowage Planning Automation in Containerships

Lashing Force Prediction Model with Multimodal Deep Learning and AutoML for Stowage Planning Automation in Containerships

Knowledge-Based Engineering–based method for containership lashing bridge optimization design and structural improvement with functionally graded thickness plates

Experimental and numerical analysis of container multiple stacks dynamics using a scaled model

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