A large number of river training works have been built in the inland waterway regulation projects to improve ship navigation conditions. However, water damages to river training works happen frequently in practice. Technical status evaluation of river training works is regarded as a fundamental content of inland waterway maintenance. Due to the various influencing factors and complex mechanisms, the content involved in the standard files is recognized as qualitative and no quantitative evaluation method is recommended so far. The technical status of river training works is currently evaluated through on-site investigation which is time-consuming and individual-dependent. By means of multi-source sensors, massive status data of river training works could be obtained instantaneously. Study on the technical evaluation model based on the multi-source information fusion theory attracts more attention in recent years. The classical DS evidence theory could fail as evidence conflict occurs. Thus, the Pearson’s correlation coefficient is calculated and utilized to update the probability distribution in the present study. A novel technical status evaluation model based on the improved DS evidence theory is established. The model is further verified through three case studies of traditional river training works (spur dike and flexible mattress belt) in the Yangtze River, China. The model outputs are consistent with the technical survey reports as well as the published research article. Quantitative and accurate evaluation of river training works could be accomplished by applying the proposed evaluation model. Moreover, the model could be embedded in the Inland Electronic Chart Display and Information System. The present study would provide theoretical basis for inland waterway maintenance and infrastructure monitoring in the future.

With the development of sustainable inland waterway transportation, maritime safety has become a matter of wide concern. Causation analysis of ship accidents is known as an important prerequisite for achieving waterborne transportation safety. A novel AcciMap–graph convolutional network (GCN)-based causation analysis model has been proposed for maritime traffic accidents in the main Yangtze River waterway. Mutual information, a grey wolf optimizer (GWO), a dropout layer and weakly supervised learning are introduced to obtain causal chains and the causal importance scores of accident causes. The results indicate that, compared with baseline models (e.g., GIN, GAT, APPNP, and GraphSAGE), the AcciMap–GCN model achieved the lowest values for all performance metrics, with an MAE of 0.0820, an RMSE of 0.1694, and a training convergence epoch of 74. The model robustness was comprehensively investigated through ablation experiments. Based on the causal importance scores of accident causes, maritime safety administration strategies are systematically proposed. The present study provides a novel perspective for analyzing the causal relationships of maritime accidents and shares useful insights into sustainable waterway transportation.

The rotating annular flume has been widely adopted to generate quasi-steady and uniform flow, thus serving for the investigation of sediment motion characteristics. This complex flow structure is significantly associated with the rotational speed ratio. The present study aims to explore the optimal speed ratio based on the OpenFOAM simulation. In this paper, the physical properties of a rotating annular flume with different speed ratios are investigated and analyzed in terms of bottom shear stress, turbulent velocity ratio, cross-sectional secondary flow, and vector field by interFoam, a built-in solver of the open-source CFD program OpenFOAM. The RNG k-epsilon model has been adopted to solve multiphase flow problems. The results demonstrate that the optimal speed ratio differs with the specific evaluation criterion. Given the uniform distribution of bottom shear stress, the turbulence velocity ratio, and the minimum secondary flow as the evaluation criteria, the corresponding optimal speed ratios are determined as 1.2, 1.7, and 1.7, respectively. The conclusion is generally consistent with the results derived by other scholars. Computational fluid dynamics programs have been proven as practical tools for investigating complex hydrodynamic characteristics. The present study shares useful insights into the optimal rotational speed ratio of a rotating annular flume. The OpenFOAM-based numerical model will provide guidance for experimental research using rotating annular flumes.