On the causation of seafarers’ unsafe acts using grounded theory and association rule

Lan, He; Ma, Xiaoxue; Qiao, Weiliang; Ma, Laihao

doi:10.1016/j.ress.2022.108498

Cited by 32 publications

(8 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fa et al combined the HFACS model and intelligent algorithm to study the unsafe behavior of miners at both the individual and organizational levels [ 9 ]. Although the factors in the original HFACS-CM model are useful, some are not entirely applicable to deep coal mines, and the model should be improved according to the specific conditions of deep coal mines and the characteristics of miners [ 41 , 42 ]. This study aimed to identify the risk factors that specifically influence the unsafe behavior of deep coal miners.…”

Section: Methodsmentioning

confidence: 99%

“…Content and structural validity are often used to test the overall validity of a survey scale. Most of the items in the survey scale of unsafe behaviors and risk factors for deep coal miners developed in this study were adapted from a mature scale developed by domestic and foreign scholars [ 2 , 8 , 10 , 14 , 42 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. The self-compiled items were also modified several times based on literature analysis and expert consultation, combined with the actual production situation of deep coal mines.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD

Yang

Wang

Zhu

et al. 2022

IJERPH

View full text Add to dashboard Cite

The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners’ unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners’ unsafe behaviors in deep coal mining. First, the HFACS-CM model identifies the risk factors affecting miners’ unsafe behavior in deep coal mines. Second, SEM was used to analyze the interaction between risk factors and miners’ unsafe behavior. Finally, the SD model was used to simulate the sensitivity of each risk factor to miners’ unsafe behavior to explore the best prevention and control strategies for unsafe behavior. The results showed that (1) environmental factors, organizational influence, unsafe supervision, and unsafe state of miners are the four main risk factors affecting the unsafe behavior of miners in deep coal mines. Among them, the unsafe state of miners is the most critical risk factor. (2) Environmental factors, organizational influence, unsafe supervision, and the unsafe state of miners have both direct and indirect impacts on unsafe behaviors, and their immediate effects are far more significant than their indirect influence. (3) Environmental factors, organizational influence, and unsafe supervision positively impact miners’ unsafe behavior through the mediating effect of miners’ unsafe states. (4) Mental state, physiological state, business abilities, resource management, and organizational climate were the top five risk factors affecting miners’ unsafe behaviors. Taking measures to improve the adverse environmental factors, strengthening the organization’s supervision and management, and improving the unsafe state of miners can effectively reduce the risk of miners’ unsafe behavior in deep coal mines. This study provides a new idea and method for preventing and controlling the unsafe behavior of miners in deep coal mines.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD

Yang

Wang

Zhu

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…For instance, Wróbel [ 11 ] claimed that unsafe acts were responsible for 80 % of maritime accidents, facilitating increased studies in this field [ 12 , 13 ]. After examining 540 maritime accident reports, Lan et al [ 14 ] pointed out that different types of maritime accident include specific seafarers' unsafe acts. The main differences of unsafe acts between collisions and groundings are resource management deficiencies, communication failures, decision errors, skill-based errors, and violations [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting types of human-related maritime accidents with explanations using selective ensemble learning and SHAP method

Lan,

Wang,

Zhang

2024

Heliyon

Self Cite

View full text Add to dashboard Cite

“…With the deepening of generalized cognition of the “blue belief” of marine power proposed in the report of the 19th National Congress of the Communist Party of China, the issue of personnel safety in maritime traffic has attracted increasingly extensive attention. According to the “2021 Annual Overview of Marine Casualties and Incidents,” published by the European Maritime Agency (EMSA) [ 1 ], from 2014–2020 a total of 550 people went missing in accidents, and 367 people were identified as dead; 89.1% of the victims were seafarers. All but 9.8% of the casualties were due to falling into the water, whereas the rest were due to human slips, trips, and falls.…”

Section: Introductionmentioning

confidence: 99%

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Liu

et al. 2022

Sensors

View full text Add to dashboard Cite

Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose–LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer’s falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.

show abstract

On the causation of seafarers’ unsafe acts using grounded theory and association rule

Cited by 32 publications

References 58 publications

Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD

Comprehensive Evaluation of Deep Coal Miners’ Unsafe Behavior Based on HFACS-CM-SEM-SD

Predicting types of human-related maritime accidents with explanations using selective ensemble learning and SHAP method

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Contact Info

Product

Resources

About