A Hybrid Probabilistic Risk Analytical Approach to Ship Pilotage Risk Resonance with FRAM
Round 1
Reviewer 1 Report
OVERVIEW: The study addresses the critical issue of ship transportation risks, focusing on ship pilotage operations. Despite the presence of skilled pilots, ships continue to face accidents during pilotage due to complex navigation environments, limited channel conditions, and dense traffic. The study highlights the shift in maritime risk research from macro-static to micro-ship operational risks within the Formal Safety Assessment (FSA) framework. The authors propose a probability-based risk resonance analysis framework by integrating the D-S evidence theory, Monte Carlo simulation, and an improved Functional Resonance Analysis Method (FRAM) version. This integrated approach allows for both qualitative and quantitative assessment of complex system risks. The methodology is applied and validated through a resonance analysis of collision risks during the pilotage of a container ship in Shanghai Port waters. The results demonstrate the effectiveness of the proposed approach in identifying and understanding the dynamics of risk factors in ship pilotage operations. The paper concludes with a discussion of the method's potential and its application results, contributing to a comprehensive understanding of ship operational risks.
â—Ž Introduction section
â—‡ Problem 1: Lack of Clarity and Conciseness
[Sample] Original Sentence: Ship pilotage operation risk involves studying microcosmic changes in behavior and dynamic correlations during single-ship operations, which requires the consideration of more dynamic factors and coupling relationships of the pilotage system.
Reviewer's Perspective: The sentence is complex and lacks clarity in conveying its message.
â—‡ Problem 2: Redundancy and Repetition
[Sample] Original Sentence: Therefore, based on the qualitative analysis of the functional operation mechanism of complex systems, supplemented by other quantitative analysis methods [30], FRAM can achieve a quantitative expression of functional variability and reveal the mechanism of risk transmission by analyzing the coupling resonance effect of adverse functional variability.
Reviewer's Perspective: The sentence is repetitive and can be streamlined for better clarity.
|
Consequently, by integrating qualitative analysis of complex system functional mechanisms with complementary quantitative methods [30], FRAM facilitates quantifying functional variability and uncovering the mechanism of risk transmission through analysis of adverse functional coupling resonance. |
â—‡ Problem 3: Ambiguity and Wordiness
[Sample] Original Sentence: The focus of maritime risk research under the framework of the FSA has gradually changed from macro-static risk to micro-ship operational risk to achieve targeted control of ship operation process risk.
Reviewer's Perspective: The sentence is wordy and can be made clearer.
â—‡ Problem 4: Lack of Connection between Ideas
[Sample] Original Sentence: In addition to the changes in RIF research introduced by advanced technology and management modes, the perspectives of research problems also change the composition and mechanisms of RIFs.
Reviewer's Perspective: The connection between the influence of advanced technology and the shifting perspectives of research problems is unclear.
â—‡ Problem 5: Complex Sentence Structure
[Sample] Original Sentence: Accident causation theory, as the theoretical basis of risk identification, has undergone different developmental stages, such as accident tendency theory, causal chain theory, accidental energy release theory, and system causation theory, with the development of technology and safety theory [6].
Reviewer's Perspective: The sentence structure is complex, making it difficult to follow.
â—Ž Design section
â—‡ Clarity in System Function Identification (Step 1): While the hierarchical task analysis method (HTA) is employed for system function identification, providing more context on how this process is carried out would be valuable. Elaborate on the criteria for identifying meta-tasks and explain how they are translated into system functions within the FRAM model.
â—‡ Function Variability and Coupling Quantification (Step 2): Enhance the description of how functional variability is characterized in terms of timing and precision. Provide specific examples of how these aspects are measured and translated into quantitative assessments. Additionally, clarify how the discrete probability distribution is constructed, particularly in cases where historical statistical data and expert evaluations are combined.
â—‡ D-S Evidence Theory Application (Step 2): Elaborate on how the D-S evidence theory is employed to fuse the evaluation results of functional variability and coupling. Explain how the theory's principles are practically applied to combine expert evaluations and historical data, highlighting the steps involved and the rationale behind this fusion process.
â—‡ Quantitative Analysis Integration: Expand on how the MC simulation is integrated with FRAM and the D-S evidence theory for quantitative analysis. Describe the process of incorporating these methodologies and how they collectively contribute to revealing functional resonance paths and risk-causative mechanisms. Provide a detailed flowchart or diagram illustrating the integration of these techniques.
â—Ž Result and Conclusion section
â—‡ The comparative assessment between scenarios 1 and 6 further demonstrated the role of operation conditions in shaping system risks. The critical coupling 'F2-F7(I)' emerged as a consistent factor in both scenarios, emphasizing the significance of maintaining a proper lookout. In scenario 6, where complexities in traffic conditions prevailed, lookout negligence ('F2-F3(I)') became a critical coupling, showcasing the direct impact of operational challenges on risk factors.
â—‡ Furthermore, the study identified the necessity of combining 'changing speed' and 'changing course' methods during ship pilotage in narrow waters, as evidenced by the emergence of a new critical coupling ('F13-F12(I)') in scenario 6. This result underscored the alignment between MC simulation outcomes and real-world operational conditions, bolstering the method's effectiveness. In conclusion, the study's comparative analysis of different scenarios showcased the interplay between operational conditions and system risks, affirming the efficacy of the proposed quantitative FRAM approach. The findings provided valuable insights into collision risk management during ship pilotage operations and laid the groundwork for future studies addressing real-world complexities and temporal risk evaluation.
Comments for author File: 
Comments.pdf
OVERVIEW: The study addresses the critical issue of ship transportation risks, focusing on ship pilotage operations. Despite the presence of skilled pilots, ships continue to face accidents during pilotage due to complex navigation environments, limited channel conditions, and dense traffic. The study highlights the shift in maritime risk research from macro-static to micro-ship operational risks within the Formal Safety Assessment (FSA) framework. The authors propose a probability-based risk resonance analysis framework by integrating the D-S evidence theory, Monte Carlo simulation, and an improved Functional Resonance Analysis Method (FRAM) version. This integrated approach allows for both qualitative and quantitative assessment of complex system risks. The methodology is applied and validated through a resonance analysis of collision risks during the pilotage of a container ship in Shanghai Port waters. The results demonstrate the effectiveness of the proposed approach in identifying and understanding the dynamics of risk factors in ship pilotage operations. The paper concludes with a discussion of the method's potential and its application results, contributing to a comprehensive understanding of ship operational risks.
â—Ž Introduction section
â—‡ Problem 1: Lack of Clarity and Conciseness
[Sample] Original Sentence: Ship pilotage operation risk involves studying microcosmic changes in behavior and dynamic correlations during single-ship operations, which requires the consideration of more dynamic factors and coupling relationships of the pilotage system.
Reviewer's Perspective: The sentence is complex and lacks clarity in conveying its message.
â—‡ Problem 2: Redundancy and Repetition
[Sample] Original Sentence: Therefore, based on the qualitative analysis of the functional operation mechanism of complex systems, supplemented by other quantitative analysis methods [30], FRAM can achieve a quantitative expression of functional variability and reveal the mechanism of risk transmission by analyzing the coupling resonance effect of adverse functional variability.
Reviewer's Perspective: The sentence is repetitive and can be streamlined for better clarity.
|
Consequently, by integrating qualitative analysis of complex system functional mechanisms with complementary quantitative methods [30], FRAM facilitates quantifying functional variability and uncovering the mechanism of risk transmission through analysis of adverse functional coupling resonance. |
â—‡ Problem 3: Ambiguity and Wordiness
[Sample] Original Sentence: The focus of maritime risk research under the framework of the FSA has gradually changed from macro-static risk to micro-ship operational risk to achieve targeted control of ship operation process risk.
Reviewer's Perspective: The sentence is wordy and can be made clearer.
â—‡ Problem 4: Lack of Connection between Ideas
[Sample] Original Sentence: In addition to the changes in RIF research introduced by advanced technology and management modes, the perspectives of research problems also change the composition and mechanisms of RIFs.
Reviewer's Perspective: The connection between the influence of advanced technology and the shifting perspectives of research problems is unclear.
â—‡ Problem 5: Complex Sentence Structure
[Sample] Original Sentence: Accident causation theory, as the theoretical basis of risk identification, has undergone different developmental stages, such as accident tendency theory, causal chain theory, accidental energy release theory, and system causation theory, with the development of technology and safety theory [6].
Reviewer's Perspective: The sentence structure is complex, making it difficult to follow.
â—Ž Design section
â—‡ Clarity in System Function Identification (Step 1): While the hierarchical task analysis method (HTA) is employed for system function identification, providing more context on how this process is carried out would be valuable. Elaborate on the criteria for identifying meta-tasks and explain how they are translated into system functions within the FRAM model.
â—‡ Function Variability and Coupling Quantification (Step 2): Enhance the description of how functional variability is characterized in terms of timing and precision. Provide specific examples of how these aspects are measured and translated into quantitative assessments. Additionally, clarify how the discrete probability distribution is constructed, particularly in cases where historical statistical data and expert evaluations are combined.
â—‡ D-S Evidence Theory Application (Step 2): Elaborate on how the D-S evidence theory is employed to fuse the evaluation results of functional variability and coupling. Explain how the theory's principles are practically applied to combine expert evaluations and historical data, highlighting the steps involved and the rationale behind this fusion process.
â—‡ Quantitative Analysis Integration: Expand on how the MC simulation is integrated with FRAM and the D-S evidence theory for quantitative analysis. Describe the process of incorporating these methodologies and how they collectively contribute to revealing functional resonance paths and risk-causative mechanisms. Provide a detailed flowchart or diagram illustrating the integration of these techniques.
â—Ž Result and Conclusion section
â—‡ The comparative assessment between scenarios 1 and 6 further demonstrated the role of operation conditions in shaping system risks. The critical coupling 'F2-F7(I)' emerged as a consistent factor in both scenarios, emphasizing the significance of maintaining a proper lookout. In scenario 6, where complexities in traffic conditions prevailed, lookout negligence ('F2-F3(I)') became a critical coupling, showcasing the direct impact of operational challenges on risk factors.
â—‡ Furthermore, the study identified the necessity of combining 'changing speed' and 'changing course' methods during ship pilotage in narrow waters, as evidenced by the emergence of a new critical coupling ('F13-F12(I)') in scenario 6. This result underscored the alignment between MC simulation outcomes and real-world operational conditions, bolstering the method's effectiveness. In conclusion, the study's comparative analysis of different scenarios showcased the interplay between operational conditions and system risks, affirming the efficacy of the proposed quantitative FRAM approach. The findings provided valuable insights into collision risk management during ship pilotage operations and laid the groundwork for future studies addressing real-world complexities and temporal risk evaluation.
Author Response
Please see the attachment
Author Response File: Author Response.pdf
Reviewer 2 Report
The authors investigated an important issue. This article clearly belongs to this journal. The proposed methodology is clear. The results are very well presented, and the conclusion is clear and concise. However, it has a few issues with grammar and spelling. The authors should review the whole manuscript and fix these issues. Moreover, the authors should have finished the abstract with a sentence with the outcome of the study briefly.
Comments for author File: Comments.pdf
Quality of English needs to be improved.
Author Response
Please see the attachment
Author Response File: Author Response.pdf
Reviewer 3 Report
The paper deals with the risk of ship collisions when navigated by a harbour pilot in a selected body of water, Figure 2. The authors gave a very interesting introduction in which they talked about the so-called human factor. They then ran simulations for six risk factor scenarios. One might wonder why the simulation did not take into account the change in the risk of reaction, as described in Table 3, due to the age and experience of the harbour pilot profession, Table 5. Perhaps the authors will add something about this, because the mere statement at the end that this is a theoretical simulation may be open to the accusation that the human factor has been omitted. And the human factor is often the weakest element in a port navigation system. Please add this.
Author Response
Please see the attachment
Author Response File: Author Response.pdf
