Search Results (6)

Coal mine gas explosions remain a major occupational hazard, driven by the interaction of multiple risk factors. In this study, a systematic framework was developed for accident causation analysis and prevention by integrating root–state risk identification (RSRI) theory with complex network modeling. An analysis of 102 accident reports identified 112 primary risk factors, which were incorporated into a causation network. Nodes were prioritized through entropy-weighted TOPSIS, and edge vulnerability analysis was applied to reveal dominant evolutionary pathways. The results indicate that gas accumulation in the heading face constitutes the most critical direct cause, while insufficient safety supervision is the principal indirect driver. The most hazardous pathway involves inadequate ventilation inspection, reduced air supply, gas accumulation, weak supervision, limited safety training, and unsafe blasting practices. These findings underscore the pivotal role of organizational and behavioral deficiencies in risk propagation. The proposed framework advances current approaches to risk assessment by systematically identifying key factors and critical paths, thereby providing actionable insights for enhancing supervision, strengthening preventive strategies, and reducing catastrophic accidents in coal mines. Full article

Most accidents during the construction of nuclear power plants are caused by human unsafe behavior. How to scientifically determine the risk management priority of human unsafe behaviors is the basis for effectively preventing accidents in under-construction nuclear power plants. Although employees are adopted for control in under-construction nuclear power plants, the records of unsafe behaviors are mostly recorded by inspectors, and the records of behaviors may have missing values. To overcome the above problems, this paper applies machine learning algorithms to construct an employee behavioral risk assessment model. Firstly, by analyzing the influencing factors of unsafe behaviors, the assessment indexes are proposed, then the Random Forest algorithm is used to obtain the characteristic importance of the proposed indexes and exclude those with smaller characteristic importance. Finally, the harmony search (HS) algorithm is used to optimize the back propagation (BP) neural network to construct an assessment model and compare with the BP evaluation model. The results show that the HS-BP model is more accurate and efficient. The results show that the method can comprehensively and effectively analyze workers‘ unsafe behaviors, and the BP neural network is optimized to construct the assessment model using the Harmonic Search algorithm, which is more accurate than the original model. The use of the machine learning method to assess workers’ behaviors can objectively output the risk level and overcome the one-sidedness and subjectivity of the traditional expert evaluation method. Full article

There are numerous risk factors across various dimensions that lead to unsafe behaviors among construction workers, and the interactions between these factors are complex and intertwined. Therefore, it is crucial to comprehensively explore the mechanisms of these risk factors across all dimensions to reduce the accident rate. This paper combines cascading failure and entropy flow models to construct a cascading trigger model for identifying key nodes and paths in a risk network. First, this paper identifies the risk factors in the individual, organizational, managerial, and environmental dimensions, dividing them into deep and surface factors. Based on this, a risk network is constructed, and cascading failure is introduced to simulate the dynamic evolution of risks. Then, the entropy flow model is introduced to quantify the risk flow in risk propagation. Finally, to address the uncertainty of risk occurrence, Visual Studio Code is used for coding, and a simulation platform is built using JavaScript. After conducting simulation experiments, the results are statistically analyzed. The results show that the key nodes of deep factors are mainly concentrated in the individual dimension (herd mentality, negative emotions, physical fatigue, fluke mindset), organizational dimension (poor cohesion, poor internal communication), and managerial dimension (abusive leadership style and insufficient/low-quality safety education and training); the surface factors are mainly the poor safety climate in the organizational dimension. The findings provide theoretical support for reducing the accident rate caused by unsafe worker behaviors, aiming to reduce accident risk losses by cutting off risk propagation paths. Full article

Unsafe behaviors among construction workers are a leading cause of safety accidents in the construction industry, and studying the mechanism of unsafe behavior propagation among construction workers is essential for reducing the occurrence of safety accidents. Safety attitude plays a pivotal role in predicting workers’ behavioral intentions. We propose a propagation model of unsafe behaviors based on a two-layer complex network, in which the upper layer depicts the change in construction workers’ safety attitudes, and the lower layer represents the propagation of unsafe behaviors. In this model, we consider the impact of individual heterogeneity and herd mentality on the transmission rate, establishing a partial mapping relationship based on behavioral feedback. After that, by building a probability transition tree, we establish the risk state transition equation in detail using the microscopic Markov chain approach (MMCA) and analyze the established equations to deduce the propagation threshold of unsafe behaviors analytically. The results show that enhancing the influence of individual heterogeneity and behavioral feedback increases the threshold for the spread of unsafe behaviors, thereby reducing its scale, while herd mentality amplifies the spread. Furthermore, the coexistence of safety education and behavioral feedback may lead to one of the mechanisms fails. This research enhances understanding of the propagation mechanism of unsafe behaviors and provides a foundation for managers to implement effective measures to suppress the propagation of unsafe behaviors among construction workers. Full article

The unsafe behavior of frontline workers at construction sites is the most important cause of construction accidents. This study proposed a comprehensive model of frontline workers’ unsafe behaviors based on a systems perspective and used structural equation modeling (SEM) to explore the influence mechanisms between the objective conditions (e.g., work environment, work climate, and task complexity), safety management (e.g., safety education and training, safety reward and punishment regulations, safety inspection, safety technology disclosure, and safety warning signs), group influence (propagation of unsafe behaviors among workers), personal perception (subjective judgment of operators on their safety knowledge and skills), and unsafe behaviors. Data from 460 frontline workers were collected through questionnaires and the correlation hypotheses were tested using SPSS 26.0 and Amos 26.0 software. The following conclusions were obtained: (1) objective conditions directly positively influence safety management, group influence, and personal perception but indirectly negatively influence unsafe behavior; (2) safety management not only directly positively affects personal perception but also directly negatively affects unsafe behavior. However, the direct effect of safety management on group influence is not significant; (3) group influence has a direct positive effect on unsafe behavior, but the direct effect on personal perception is not significant; (4) the direct effect of personal perception on unsafe behavior is insignificant. These findings can be used as preliminary data to guide decision makers or managers in construction companies to develop reasonable management plans to curb the unsafe behaviors of frontline workers. Full article

This paper analyzes the management system of safety partnership in coal mining enterprises through the methods of evolutionary game and optimized behavioral propagation of SEIR, considering the miners’ benefits and losses, as well as the influencing factors from miners and enterprises. It is found that, under the influence of the management system of safety partnership within miners, after the evolutionary game between miner partners, the behavioral strategies and personal benefits of the two miners are both consistent. Moreover, the benefits of individual miner and overall benefits of two miner partners, will affect the miners’ choice of safe behavioral strategies, as a result of which, the coal mines could improve the miners’ benefits through the management system of safety partnership to stimulate the implementation of miners’ safe behavior. Additionally, under the incentive of the management system of the safety partnership, the number of miners implementing unsafe behavior is decreasing, while the number of miners who are not easily affected by unsafe behavior is increasing. When the rewards and punishments of miners are strengthened, the propagation of miners’ safe behavior is accelerated. Finally, the propagation of miners’ safe behavior has a certain spillover effect within a certain range. The results of this paper provide a theoretical basis for the implementation of the management system of safety partnership in coal mining enterprises, which helps enterprises in guiding miners to take up safe behavior, which is better for enterprises’ safe development. Full article