Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based on a single model; therefore they have low accuracy and robustness. In this study, we propose a novel hybrid fuel consumption prediction model based on sensor data collected from an ocean-going container ship. First, a data processing method is proposed to clean the collected data. Secondly, the Bayesian optimization method of hyperparameters is used to reasonably set the hyperparameter values of the model. Finally, a hybrid fuel consumption prediction model is established by integrating extremely randomized tree (ET), random forest (RF), Xgboost (XGB) and multiple linear regression (MLR) methods. The experimental results show that data cleaning, the size of the dataset, marine environmental factors, and hyperparameter optimization can all affect the accuracy of the model, and the proposed hybrid model provides better predictive performance (higher accuracy) and greater robustness (smaller standard deviation) as compared with a single model. The proposed hybrid model should play a significant role in ship fuel consumption real-time monitoring, fault diagnosis, energy saving and emission reduction, etc.

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.

This paper aims to study the low-velocity impact (LVI) response and compression after impact (CAI) performance of carbon/aramid hybrid woven composite laminates employed in marine structures subjected to different energy impacts. The study includes a detailed analysis of the typical LVI responses of hybrid woven composite laminates subjected to the impact with three different energies, as well as a comparative analysis of cracks and internal delamination damage within impact craters. Additionally, the influence of different impact energies on the residual compressive strength of hybrid woven composite laminate is investigated through CAI tests and a comparative analysis of internal delamination damage is also conducted. The results indicate that as the impact energy increases, the impact load and CAI strength show a decreasing trend, while impact displacement and impact dent show an increasing trend. The low-velocity impact tests revealed a range of failure modes observed in the hybrid woven composite laminates. Depending on the specific combination of fiber materials and their orientations, the laminates exhibited different failure mechanisms. Buckling failures were observed in the uppermost composite layers of laminates with intermediate modulus systems. In contrast, laminates with higher modulus systems showed early damage in the form of delamination within the top surface layers.

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 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.

In this paper, according to the water area of light buoy, the migration rule of light buoy in main channel is counted, and the frequency of light buoy passing through a certain position point in the process of migration is calculated, and the model is verified by buoy position data. An anomaly detection algorithm based on improved adaptive DBSCAN clustering is designed. The size of the ε neighborhood is adaptive according to the wind speed, wave height, and drift distance span of the water area where the light buoy is located. The experimental results show that the improved adaptive DBSCAN clustering algorithm can solve the problem that the common DBSCAN clustering algorithm takes the hot water area of the light buoy position or the most likely area in the light buoy migration process as the noise point.