There are inevitable multiphase flow problems in the process of subsea oil-gas acquisition and transportation, of which the two-phase flow involving gas and liquid is given much attention. The performance of pipelines and equipment in subsea systems is greatly affected by various flow patterns. As a result, correctly and efficiently identifying the flow pattern in a pipeline is critical for the oil and gas industry. In this study, two attention modules, the convolutional block attention module (CBAM) and efficient channel attention (ECA), are introduced into a convolutional neural network (ResNet50) to develop a gas–liquid two-phase flow pattern identification model, which is named CBAM-ECA-ResNet50. To verify the accuracy and efficiency of the proposed model, a collection of gas–liquid two-phase flow pattern images in a vertical pipeline is selected as the dataset, and data augmentation is employed on the training set data to enhance the generalization capability and comprehensive performance of the model. Then, comparison models similar to the proposed model are obtained by adjusting the order and number of the two attention modules in the two positions and by inserting other different attention modules. Afterward, ResNet50 and all proposed models are applied to classify and identify gas–liquid two-phase flow pattern images. As a result, the identification accuracy of the proposed CBAM-ECA-ResNet50 is observed to be the highest (99.62%). In addition, the robustness and complexity of the proposed CBAM-ECA-ResNet50 are satisfactory.

Safety barriers are widely accepted in various industries as effective risk management tools to prevent hazardous events and mitigate the consequences caused by these events. Studies on safety barriers have been increasing in recent decades; therefore, the general idea of this article is to present a systematic review of the field. The purpose of this article is threefold: (1) to map various networks for the barrier-related articles collected from WoS; (2) to summarize the advances of the safety barrier at both the individual level and barrier management level on the basis of six issues, and (3) to propose the research perspectives associated with safety barriers considering the latest theories and methodologies in the field of safety management. Based on the findings and insights obtained from the literature collected by a bibliometric and systematic review, studies on barrier management within the complex socio-technical system are analyzed, and the framework of “risk-barrier capacity” is proposed for future development, in which the challenges stemming from industrial intelligence may be solved through resilience theory. Meanwhile, intelligent technologies are also able to serve as health status monitoring devices for various barrier elements.

To investigate the human-related factors associated with suffocation on ships during docking repair, a comprehensive analysis model composed of a Bayesian network (BN) and a complex network (CN) is proposed in the present study. The principle of event tree analysis (ETA) is firstly applied to identify the hazardous events involved in the accident according to the accident report, based on which the CN would then be developed with the logic relationships among the hazardous events. The improved K-shell decomposition algorithm is utilized to determine the criticality of nodes in the CN, the results of which are then used to develop the BN model within the framework of a human factor analysis classification system (HFACS). Then, the developed BN model can be simulated with the probability distribution of all the nodes within the BN, which are obtained on the basis of node criticality. Finally, the results of the BN simulation are interpreted from the perspectives of a brief analysis, backward analysis and sensitivity analysis. The results are verified with existing studies and the accident investigation report issued by authority, which are presented as evidence to verify the effectiveness of the proposed methodology to evaluate the human-related risk involved in the suffocation on ships. The methodology proposed in this study integrates the advantages of BN and CN to investigate the human-related hazardous events involved in maritime accidents, which can be seen as the main innovation of this work.