Assessment of Power Plant Based on Unsafe Behavior of Workers through Backpropagation Neural Network Model

Safety is an essential topic for electric power plants. In recent years, accidents caused by unsafe behaviors of electric power plant employees are frequent. To promote the sustainable development and safety of electric power plants, studies on the assessment of unsafe behavior are becoming increasingly important and urgent. In this study, accident statistical analysis, literature review, and expert survey are adopted to select more comprehensive and accurate assessment indicators of unsafe behavior of the workers in electric power plants. Data about indicator and unsafe behavior were obtained through a questionnaire survey, and 27 indicators were used as inputs, and the unsafe behavior was taken as the output of a backpropagation (BP) neural network based unsafe behavior assessment model. An assessment indicator system about power plant workers’ unsafe behavior composed of 4 first-level indicators and 27 second-level indicators was established and the weights of the assessment indicators were determined. A three-layer feedforward BP neural network assessment model of “27-13-1” layers was found to be a suitable model. The proposed model can demonstrate the nonlinear complex relationship between the assessment indicator and the unsafe behavior of power plant workers. The model can be helpful to evaluate, predict, and monitor the safety performance of electric power plants.

Diagnostic intervention program based on construction workers' internal factors for persistent reduction of unsafe behavior

PurposeThis study aims to show what interventions in human factors can effectively reduce construction workers' unsafe behavior.Design/methodology/approachA diagnostic intervention model targeted the construction workers' weakest internal factors. The workers' behavior and cognition data were collected via a questionnaire and a video camera system from two medium-sized construction sites. A safety supervisor accompanied each site supervisor to improve construction workers' internal factors by implementing the designed intervention measures.FindingsThe statistical analysis results confirmed a persistent positive effect on construction workers' safe behavior by improving internal factors. Among the intervention programs applied, those aimed to improve the subjective norms, safety knowledge and attitudes had the most significant effect sizes.Practical implicationsThe findings of this case study advise project managers to design a specific behavioral intervention that aims at improving construction workers' significant internal factors, including subjective norms, safety attitudes, habits and knowledge together with demographic characteristics to reduce construction workers' unsafe behavior.Originality/valueWhile the declining rate of construction accidents approaches an asymptote which is still high, this study suggests that targeting the individual internal factors through diagnostic interventions is the key to further reduce the rate by improving construction workers' behavior.

Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

Research on Unsafe Behavior of Construction Workers Under the Bidirectional Effect of Formal Rule Awareness and Conformity Mentality

At present, China’s engineering safety management has developed to a certain level, but the number of casualties caused by construction accidents is still increasing in recent years, and the safety problems in the construction industry are still worrying. For purpose of effectively reducing construction workers’ unsafe behavior and improve the efficiency of construction safety management, based on multi-agent modeling, this paper analyzes the influencing factors during construction workers’ cognitive process from the perspective of safety cognition, constructs the interaction and cognition of the agent under the bidirectional effect of formal rule awareness and conformity mentality model, and set behavior rules and parameters through the Net Logo platform for simulation. The results show that: Unsafe behavior of construction workers is related to the failure of cognitive process, and the role of workers’ psychology and consciousness will affect the cognitive process; The higher the level of conformity intention of construction workers, the easier it is to increase the unsafe behavior of the group; Formal rule awareness can play a greater role only when the management standard is at a high level, and can correct the workers’ safety cognition and effectively correct the workers’ unsafe behavior; Under certain construction site environmental risks, the interaction between formal rule awareness and conformity mentality in an appropriate range is conducive to the realization of construction project life cycle management. This study has certain theoretical and practical significance for in-depth understanding of safety cognition and reducing unsafe behavior of construction team.

Research on the Tournament Incentive Mechanism of the Safety Behavior for Construction Workers: Considering Multiple Heterogeneity

The unsafe behavior of construction workers is one of the most important and direct causes of safety accidents. Managers usually develop effective incentives aimed at regulating worker safety behavior. Due to the large number of workers in construction projects, there are multiple differences in fairness preference, risk preference and ability level, which will lead to the complex effect of the traditional mechanism to regulate workers’ safety behavior. In order to improve the effectiveness of incentive measures for worker safety behavior, this paper takes into account the multiple differences of individual workers’ fairness preference, risk preference and ability level, based on the tournament mechanism to construct a competition incentive model. By designing a tournament reward and salary distribution for heterogeneous workers, the occurrence of unsafe behaviors can be reduced. The study found that in terms of the optimal level of safety investment, workers with risk aversion attitude generally invest higher than that of workers with risk preference, no matter whether they have a strong fairness preference or not; In terms of the distribution of tournament rewards, workers with a risk aversion attitude and a higher level of fairness preference need to be given higher incentives.

Cascading vulnerability analysis of unsafe behaviors of construction workers from the perspective of network modeling

PurposeThe construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.Design/methodology/approachAn unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.FindingsComplex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.Originality/valueThis research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.

Design, Implementation, and Evaluation of an Information System to Enhance Road Safety Level for Pedestrians in China

Pedestrians’ unsafe behavior is one of the most critical factors causing traffic incidents in China. The primary objective of this study is to explore the cause of pedestrians’ unsafe behavior and provide possible solutions. We interviewed pedestrians and experts to investigate pedestrians’ unsafe behaviors. Results from interviews indicated that pedestrians were likely to exhibit unsafe behavior at intersections owing to use of smartphones, reluctance to obey the rules, and unawareness of risk. According to the experts, attracting the attention of pedestrians and guiding them to exhibit safe behaviors can improve their safety. Based on these results, we designed “LookMe,” which is a multimedia information system placed at the intersections, to guide pedestrians across the road and improve their experience of waiting in traffic. The results of user tests indicated that pedestrians had relatively high acceptance of LookMe. Moreover, participants wanted to see diverse multimedia information on the screen of LookMe such as news, videos, maps, and traffic information. Findings from this study can be useful in understanding why Chinese pedestrians exhibit unsafe behaviors and proposing effective solutions to enhance their safety.

Research on Management and Control of Miners’ Unsafe Behavior Based on Gray Theory

A large number of accidents and scientific researches show that miners’ unsafe behavior affects coal mine safety production seriously. In order to effectively reduce the incidence of miners’ unsafe behavior, to improve their safety level, and reduce accidents caused by it, this paper used gray relational analysis method to analyze the miners’ unsafe behavior of W mine and quantitatively calculated the risk value of miners’ unsafe behavior. The results showed that the risk value of unsafe behavior in violation of labor discipline was 0.4358, which was much higher than that of other miners’ unsafe behaviors. Therefore, unsafe behavior in violation of labor discipline was determined as the key point of control in the next stage. Then, GM (1, 1) method was used to establish a predicted model for unsafe behavior, to predict the number of unsafe behaviors in violating labor discipline in next quarter, and to determine reasonable unsafe behavior control target. This study plays a driving role in controlling unsafe behaviors of miners and improving safe production water of coal mine.