A data-driven method for modeling human factors in maritime accidents by integrating DEMATEL and FCM based on HFACS: A case of ship collisions

Ma, Laihao; Ma, Xiaoxue; Lan, He; Liu, Yang; Deng, Wanyi

doi:10.1016/j.oceaneng.2022.112699

Cited by 34 publications

(5 citation statements)

References 51 publications

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“…Firstly, accurate and effective localization methods are the foundation for ensemble learning to establish high-precision soft sensing models. Currently, local clustering methods such as Fuzzy cmeans [13], K-means [14], and Hierarchical clustering [15] are commonly used to cluster data. Among them, hierarchical clustering methods are widely used because they are easy to implement without setting the number of clusters in advance, and can obtain a multi-level clustering structure with multiple granularities.…”

Section: Introductionmentioning

confidence: 99%

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Liu,

Li,

Zhang

et al. 2024

Meas. Sci. Technol.

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To address the adverse effects of nonlinearity and dynamic time-varying in complex chemical processes on the accuracy of the soft sensor model, a local-semi-supervised ensemble learning for soft sensor modeling (LS-SEL-LSTM) method is proposed in this article. Firstly, a hierarchical clustering method incorporating spatiotemporal criteria is proposed to reduce the influence of nonlinearity in global model prediction accuracy. The method considers the dynamic time-varying characteristics of temporal data and generates multiple local datasets. Then, to address the issue of multi-rate between auxiliary variables and dominant variables, a semi-supervised weight fusion mechanism based on temporal correlation is proposed, which effectively utilizes auxiliary variables to reconstruct local semi-supervised datasets and establishes local soft sensing models using LSTM. Concurrently, the parameters of the established model were optimized using the Flower Pollination Algorithm (FPA). Subsequently, a selective ensemble learning method based on sub-model prediction accuracy and an adaptive combination weight calculation method for sub-models were proposed to improve the prediction accuracy. Finally, the effectiveness of the proposed method was verified through the actual dataset of the sulfur recovery process. The results indicate that LS-SEL-LSTM performs well in handling complex chemical processes with nonlinear and dynamic time-varying characteristics.

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Section: Introductionmentioning

confidence: 99%

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Liu,

Li,

Zhang

et al. 2024

Meas. Sci. Technol.

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“…In 1999, to avoid neglecting or missing critical information on human factors in maritime investigations, IMO adopted the Amendments to the Code for the Investigation of Marine Casualties and Incidents (A.884 (21)), which provides maritime investigators with methods and procedures for investigating human factors in the maritime accidents [6]. Although the shipping industry continues to take proactive initiatives to improve the safety of seafarers' acts, the incidence of maritime accidents has not been controlled to the expected level [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Factors from organization, technology, environment, and individual traits are coupled and superimposed on individual seafarers, making the generation of seafarers' unsafe acts uncertain, sudden, and random. As a result, human factors are recognized as the dominant cause of maritime accidents, accounting for 75-96% of maritime casualties [3,4,7]. Particularly, 79% of tugboat groundings [12], 84-88% of tanker grounding accidents [13], and 89-96% of collisions [14] were demonstrated to be triggered by human factors.…”

Section: Introductionmentioning

confidence: 99%

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Lan,

2024

Applied Sciences

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Seafarers’ unsafe acts as the direct causes of maritime accidents are considered to be the result of the interaction between complex and dynamic influencing factors. Identifying the risk evolution characteristics and paths of seafarers’ unsafe acts has always been a challenge in maritime safety management. For this purpose, the present study introduces association rule technique into complex network to develop a directed weighted interaction network of seafarers’ unsafe acts and their influencing factors. Through global network topology analysis and local network community detection, the risk evolution characteristics of seafarers’ unsafe acts in maritime accidents are analyzed from a multidimensional perspective. The results indicate that the developed network has small-world characteristics, and the top 10 critical nodes all belong to seafarers’ unsafe acts, of which failure to make proper sound and light signals achieves the highest PageRank value. Results from this study would help maritime stakeholders to understand the evolution mechanism of seafarers’ unsafe acts and develop safety management strategies for interrupting the risk propagation of seafarers’ unsafe acts.

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“…The Lookout’s presence on the ship’s bridge and their presentation of thorough information about the surrounding area greatly assist the OOW in doing their tasks successfully [ 41 ]. Furthermore, in order to retain maximum attention, the person given watch duty must avoid engaging in any other activities.…”

Section: Introductionmentioning

confidence: 99%

Neurophysiological Assessment of An Innovative Maritime Safety System in Terms of Ship Operators’ Mental Workload, Stress, and Attention in the Full Mission Bridge Simulator

Ronca,

Uflaz,

Turan

et al. 2023

Brain Sciences

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The current industrial environment relies heavily on maritime transportation. Despite the continuous technological advances for the development of innovative safety software and hardware systems, there is a consistent gap in the scientific literature regarding the objective evaluation of the performance of maritime operators. The human factor is profoundly affected by changes in human performance or psychological state. The difficulty lies in the fact that the technology, tools, and protocols for investigating human performance are not fully mature or suitable for experimental investigation. The present research aims to integrate these two concepts by (i) objectively characterizing the psychological state of mariners, i.e., mental workload, stress, and attention, through their electroencephalographic (EEG) signal analysis, and (ii) validating an innovative safety framework countermeasure, defined as Human Risk-Informed Design (HURID), through the aforementioned neurophysiological approach. The proposed study involved 26 mariners within a high-fidelity bridge simulator while encountering collision risk in congested waters with and without the HURID. Subjective, behavioral, and neurophysiological data, i.e., EEG, were collected throughout the experimental activities. The results showed that the participants experienced a statistically significant higher mental workload and stress while performing the maritime activities without the HURID, while their attention level was statistically lower compared to the condition in which they performed the experiments with the HURID (all p < 0.05). Therefore, the presented study confirmed the effectiveness of the HURID during maritime operations in critical scenarios and led the way to extend the neurophysiological evaluation of the HFs of maritime operators during the performance of critical and/or standard shipboard tasks.

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A data-driven method for modeling human factors in maritime accidents by integrating DEMATEL and FCM based on HFACS: A case of ship collisions

Cited by 34 publications

References 51 publications

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Research on soft sensor modeling method for complex chemical processes based on local semi-supervised selective ensemble learning

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Neurophysiological Assessment of An Innovative Maritime Safety System in Terms of Ship Operators’ Mental Workload, Stress, and Attention in the Full Mission Bridge Simulator

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