Preventing collision accidents between merchant ships and fishing vessels has long been a significant challenge for maritime safety in coastal waters. To quantitatively analyze the relationship between the risk factors contributing to these collisions, identify the key factors leading to such accidents, and develop effective prevention strategies, the N-K model was employed to examine the risk coupling mechanisms involved. The model was based on an analysis of 132 collision incidents between merchant ships and fishing vessels in China’s coastal waters from 2013 to 2023. The characteristics of these collision accidents were investigated, and the risk factors were categorized into four distinct types: human, management, environmental, and ship factors. The coupling of collision risk factors between merchant ships and fishing vessels was mainly considered from the perspective of the overall system, and the N-K model was used to calculate the probability and risk values associated with the coupling of these four risk factors. Modeling results indicated that the coupling value of four factors was 0.1083, which was 1.5 times greater than the maximum coupling value of three factors and 2.1 times greater than the maximum coupling value of two factors. The risk of collision accidents between merchant ships and fishing vessels increases gradually with an increase in the risk coupling factors. Among the four categories of factors, the risk coupling between the ship factors and environmental factors is associated with a relatively large probability of accidents. Appropriate countermeasures were proposed to implement effective preventive measures at the source of collision accidents.

In a maritime environment, offshore structures are generally subject to cyclic shear stress from sources such as currents, waves, or offshore equipment loads, which may create vibration loadings on cohesive sediment around the foundations. In this study, the changes in shear strength, pore water pressure, and the microstructure of cohesive sediment under vibration loading were investigated through a series of laboratory tests, and the effect of the vibration intensity on the strength properties of the soft cohesive sediment surrounding a vibration pillar were analyzed. The soft cohesive sediment exhibited shear softening behavior in the experiments; the shear strength of the cohesive sediment significantly decreased under the vibration loading of the pillar. To explain the shear softening behavior of soft cohesive sediment, a formula was proposed for the attenuation of the undrained shear strength of cohesive sediment under vibration loading, which is related to the soil sensitivity and vibration intensity. Moreover, the formula revealed the mechanism of shear strength reduction or fluidization of cohesive sediment under mechanical vibrations, which may contribute in improving the penetration performance of vibratory piles or protecting the foundation around a pier.

Collision accidents involving merchant ships and fishing vessels are a crucial issue for maritime navigation safety, having received widespread attention due to their high frequency and serious effects. A statistical analysis of 142 collision accidents between merchant ships and fishing vessels from 2014 to 2025 was conducted to determine the regularity characteristics and causes of collisions in China’s coastal waters. This study investigated the characteristics of collision accidents, such as occurrence time, accident waters, ship length, and ship type, and used the grey relational analysis (GRA) method to analyze the accident causes. The causes of collision accidents involving merchant ships and fishing vessels were quite complicated, impacted not just by human factors but also by ship factors, environmental factors, and management factors. This study explored the characteristics of the collision accidents, including occurrence time, accident waters, ship length, and ship type, and analyzed the causal factors of the accidents using the grey relational analysis method. The causes of collision accidents between merchant ships and fishing vessels were relatively complex, being influenced not only by human factors but also by ship factors, environmental factors, and management factors. By ranking and comparing the relational degree values of the causal factors, three key factors were identified: X1 (failure to maintain a proper lookout) with the highest correlation degree of 0.93, X3 (improper emergency response) with the second highest correlation degree of 0.91, and X9 (complex navigation environment) with the third highest correlation degree of 0.84. Finally, based on the preceding research, suitable recommendations were made to provide a clear priority direction for accident prevention and control, as well as effective motivation for preventing or minimizing collision accidents involving merchant ships and fishing vessels.

Domestic ferry operations in Tanzania present a distinctive safety challenge, marked by a history of severe accidents, fragmented causal evidence, and scarce operating-exposure data, which together render conventional frequency-based risk assessment methods impractical. This study develops and applies a hybrid Fault Tree Analysis–Fuzzy Bayesian Network (FTA–FBN) framework to assess the risk of serious ferry accidents in Tanzanian domestic operations. A comprehensive review of documentary evidence from 1996 to 2025 was conducted to identify and harmonize causal factors and to construct a compact fault tree. This structure was subsequently validated by fifteen maritime experts, who also provided linguistic probability judgments. Expert competence was quantified using four established criteria, while linguistic assessments were represented as triangular fuzzy numbers, then competence-weighted, aggregated, and defuzzified to derive five root priors and 32 conditional probability conditions. The resulting 14-node Bayesian network was implemented in GeNIe and subjected to baseline, forward, backward, sensitivity, and behavioral validation analyses. The model yielded a baseline Serious Ferry Accident probability of 72.40%, interpreted as an expert-informed measure of model risk propensity rather than an observed voyage-level accident frequency. Forward analysis revealed that setting improper loading to “No” yielded the largest individual root-level risk reduction, while setting all five root risks to “No” reduced the top-event probability to 54.83%. Sensitivity analyses consistently emphasized the stability/capsize pathway and loading-related parameters as dominant contributors. In a retrospective consistency check using the MV Nyerere case, when overloading and capsize/sinking were used as evidence, the posterior probability of Loss of Stability rose to 96.8%. The present study provides a transparent and updateable approach for risk diagnosis and intervention prioritization within a data-constrained domestic ferry system.