Ship waterway tunnels are a new and special type of navigation facility that has emerged in the construction of complex hubs in high mountain valleys and rivers, and they have demonstrated broad applications worldwide. Due to their characteristics of long length, a dim visual background, and enclosed space, waterway tunnels are prone to causing tension and cognitive fatigue in ship officers on watch, affecting their decision-making and control abilities. This study constructs the visual navigation environment of a typical waterway tunnel in China using a ship maneuvering simulator. By monitoring the physiological data of ship officers, such as through electroencephalograms (EEGs) and electrocardiograms (ECGs), the temporal and spatial patterns of their physiological and psychological characteristics are analyzed systematically. Based on this, a quantitative model of the cognitive load of a ship officer working in a waterway tunnel is constructed. At the same time, the navigation risk of waterway tunnels of different lengths is quantized based on the entropy weight TOPSIS method, and finally, high-risk sections in waterway tunnels are identified and visualized, providing theoretical support for the management of safety in waterway tunnels.

Safe navigation of liquefied natural gas (LNG) carriers is critical for maintaining efficiency and mitigating catastrophic accident risks in modern maritime logistics, with heightened risks and operational challenges under nighttime conditions, which constitute the focus of this study. This study develops a hybrid methodological framework by integrating decision-making trial and evaluation laboratory–interpretive structural modeling (DEMATEL–ISM) with Bayesian networks to systematically identify, structure, and quantify risk factors associated with nighttime LNG carrier operations. First, key risk elements, spanning human, vessel, environmental, and managerial dimensions, are extracted via expert surveys and accident case reviews. DEMATEL–ISM is then employed to establish a multi-level hierarchical model that elucidates the direct and indirect causal relationships among these factors. The findings indicate that diminished ambient illumination and restricted visibility are deep-level triggers that propagate through psychological stress, visual performance degradation, and operational errors, ultimately increasing accident likelihood. Subsequently, a Bayesian network model is constructed by mapping the hierarchical structure onto probabilistic dependencies, allowing forward and backward inferences that pinpoint critical risk propagation pathways. The quantitative results reveal that risk chains predominantly originate from environmental disturbances and escalate when coupled with insufficient training, suboptimal management procedures, and human fatigue. These insights highlight the necessity for targeted interventions, including enhanced port lighting designs to minimize glare, scientific scheduling to counteract circadian disruptions, and strengthened on-site supervision through refined Vessel Traffic Services management and emergency response protocols. Overall, this integrated analytical framework provides both theoretical underpinnings and practical guidelines for improving nighttime LNG carrier safety, offering a valuable reference for researchers and stakeholders in maritime transportation risk management.