Evaluation of Paris MoU Maritime Inspections Using a STATIS Approach

Port state control inspections implemented under the Paris Memorandum of Understanding (MoU) have become known as one of the best instruments for maritime administrations in European Union (EU) Member States to ensure that the ships docked in their ports comply with all maritime safety requirements. This paper focuses on the analysis of all inspections made between 2013 and 2018 in the top ten EU ports incorporated in the Paris MoU (17,880 inspections). The methodology consists of a multivariate statistical information system (STATIS) analysis using the inspected ship’s characteristics as explanatory variables. The variables used describe both the inspected ships (classification society, flag, age and gross tonnage) and the inspection (type of inspection and number of deficiencies), yielding a dataset with more than 600,000 elements in the data matrix. The most important results are that the classifications obtained match the performance lists published annually by the Paris MoU and the classification societies. Therefore, the approach is a potentially valid classification method and would then be useful to maritime authorities as an additional indicator of a ship’s risk profile to decide inspection priorities and as a tool to measure the evolution in the risk profile of the flag over time.

Evaluation of Paris MoU Maritime Inspections Using a STATIS Approach

Port state control inspections implemented under the Paris Memorandum of Understanding (MoU) have become known as one of the best instruments for maritime administrations in European Union (EU) Member States to ensure that the ships docked in their ports comply with all maritime safety requirements. This paper focuses on the analysis of all inspections made between 2013 and 2018 in the top ten EU ports incorporated in the Paris MoU (17,880 inspections). The methodology consists of a multivariate statistical information system (STATIS) analysis using the inspected ship’s characteristics as explanatory variables. The variables used describe both the inspected ships (classification society, flag, age and gross tonnage) and the inspection (type of inspection and number of deficiencies), yielding a dataset with more than 600,000 elements in the data matrix. The most important results are that the classifications obtained match the performance lists published annually by the Paris MoU and the classification societies. Therefore, the approach is a potentially valid classification method and would then be useful to maritime authorities as an additional indicator of a ship’s risk profile to decide inspection priorities and as a tool to measure the evolution in the risk profile of flag over time.

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

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.

Noise report onboard of cargo vessel

"Regarding the increasing demands for a low noise environment onboard ships, the classification societies introduced the measurement of noise levels onboard every ship not only for the first ship, no matter the type and size above 1600 gross tonnage. That why is necessary a good collaboration of all the parties involved in the construction of ships from designers, shipbuilders, till owners. This article describes the measurements technique used for obtaining the noise levels onboard a cargo vessel, to obtain the approval of Classification society-"

Time to Rescue for Different Paths to Survival Following a Marine Incident

The time required for rescue is a critical factor for surviving a marine incident. The regulatory framework, International Maritime Organization (IMO) Polar Code, utilizes a risk-based approach. It states that the vessel operators are to define the time required for rescue but never less than 5 days. Based on experience from the classification society DNV GL, utilization of the minimum requirement of five days is the current industry standard when conducting risk assessments. The dimensioning of search and rescue resources is a national issue. There are no international requirements defining the adequacy of the resources for different geographical areas. The remoteness and lack of resources present within the IMO Polar Code area imposes a significant challenge for mariners in distress. The time required for rescue is highly dependent on multiple variables. Based on this study, the number of persons to be rescued, the number and type of evacuation platforms and the distance each evacuation platform must travel significantly impacts the time required for rescue. In addition, the meteorological and oceanographical (metocean) conditions play a significant role when determining the efficiency of a search and rescue operation.

The Coefficients of Equipment Number Formula of Ships

For their mooring on the sea, ships are equipped with mooring utilities such as anchors, anchor chains, and winches. To determine the specifications of these equipment, the equipment number of each ship must be calculated, and the formula used for calculating this number is provided by the classification society. However, as the equipment number formula was derived based on the tanker at the time of development, it may not be suitable for relatively recent types of ships, such as container ships and liquefied natural gas carriers. Therefore, in this study, the suitability of the equipment number formula was verified, and modified coefficients are proposed for the types of ships. First, the formula for calculating the drag force acting on the ship due to the wind and current was derived and compared with the equipment number formula. The meaning of each coefficient in the equipment number formula was analyzed using this process. Additionally, the mooring equipment, such as anchor and anchor chain, was selected based on the equipment number, and the holding power of the mooring equipment and the drag force acting on the ship was compared to verify the suitability of the equipment number formula for each type of ship. Based on this verification result, a modified coefficient was proposed for each term in the equipment number formula.

Calculation Method of Intermediate Bearing Displacement Value for Multisupported Shafting Based on Neural Network

The mutual influence between the bearings of a ship's multisupport shafting makes its installation and alignment very difficult. This article addresses the problem of the calculation of the precise displacement value of each intermediate bearing and proposes a method for fitting the shafting characteristic function by using the GA-BP (genetic algorithm-back propagation) neural network. The neural network uses the intermediate bearing reaction as input to calculate the theoretical height of the bearing, thereby accurately calculating the displacement value. Taking the installation and alignment of a ro-ro ship's propulsion shafting as an application example, a neural network of the ship's shafting is established with training samples based on finite element simulation, and the effect of network training is discussed. The accuracy of the method is verified by a comparative analysis with the measured data of the ship's shafting. The calculation results of this method are used as a guide for the installation and alignment of the ship's shafting and have passed the delivery inspection of the classification society.

Model Experimental Research of Ship Section on Structural Ultimate Strength

Focusing on the model design and experimental method research of ultimate strength of ship hull typical section, a large ship with complex section structure was taken in this paper as the research object. The nonlinear analysis on the failure mode and ultimate strength of the hull structure were carried out. The similarity model for ultimate strength experiment was designed, and the validity of the similarity model in reflecting the ultimate bearing capacity of the real ship was verified. According to the experimental results, the ultimate strength experimental value was obtained by numerical conversion. The ultimate bearing capacity of the example ship was then checked with reference to several classification society rules to verify the structural safety of the ship hull.

Development of Closed Formula of Wave Load Based Upon Long-Term Prediction: Heave Acceleration and Pitch Angle

In order to ensure the structural safety of a ship, the most severe sea states she is expected to encounter throughout her service life need to be given consideration. This is the reason why the maximum loads corresponding to such sea states are typically specified in classification society structural rules such as the Common Structural Rules (CSR) of the International Association of Classification Societies (IACS). The maximum loads used for the structural design of a ship can have a significant impact on not only her structural safety, but also her hull construction cost; therefore, it is very important that the loads be accurately estimated. The linear term of the maximum loads typically specified in some classification society rules is equivalent to a long-term predicted value with an exceedance probability of 10−8. Since the maximum loads specified in classification society rules such as the CSR were developed specifically for specific ship types, their effective application to other ship types may be somewhat limited. Aim of our larger study is to develop a closed formula of long-term prediction for maximum loads. The formula has high accuracy and can be applied to any ship size and type. This paper focused on the heave acceleration and pitch angle, which are used for the calculation of internal loads and so on. A formula which takes into account such as the standard deviation of the hull response in irregular waves and the directional distribution of irregular waves was proposed. Main ship parameters such as ship length L, breadth B, draft d, block coefficient Cb, and water line area coefficient Cw were used for formulating the long-term prediction. The accuracy and effectiveness of the proposed formula were confirmed through various numerical calculations using a linear seakeeping analysis code developed by ClassNK. The calculation covers 154 ship models (77 existing ships × 2 loading conditions per ship).