The prevention of accidents is aided by having a strong ability to identify hazards. The objective and quantitative assessment of hazard identification ability through event-related potential (ERP) experiments is of significance for person-job safety matching in high-risk positions. In this study, we first designed and conducted an electroencephalogram (EEG) experiment related to the hazard identification process. Subsequently, two indicators reflecting the hazard identification process were extracted from the behavioral data obtained during the experiment: hazard identification speed and hazard identification accuracy. Finally, time-domain and frequency-domain analysis methods were employed to investigate the ERP characteristics and patterns in the hazard identification process. The results showed that: (1) the low and high hazard identification accuracy groups (L-HIA and H-HIA) demonstrated significantly different N100 and P200 components, as well as beta, theta, and alpha power; (2) the fast and slow hazard identification speed groups (F-HIS and S-HIS) demonstrated significantly different N100, P200, and P300 components and beta power; (3) the average power value of theta wave in the central frontal region (Plow < 1.22 µV², Phigh > 1.99 µV²) can be used as the grading standard for hazard identification accuracy; (4) the average peak voltage value of the P300 component in the occipital region (Ufast < 1.78 µV, Uslow > 5.67 µV) can be used as the grading standard for hazard identification speed. An independent validation sample further confirmed the internal reproducibility of these thresholds, achieving 85.7% classification accuracy under the same EEG system and task paradigm. It’s conducive for enterprises and individuals to master the hazard identification ability of employees to train and improve their ability.

To further explore the neural mechanisms underlying the impact of safety knowledge on risk perception, a questionnaire survey and behavioral experiment were first carried out, collecting 401 valid questionnaires. Then, event-related potential (ERP) experiments focusing on two key stages of risk perception, i.e., hazard identification and risk judgment, were conducted. The results revealed that: (1) During hazard identification, subjects with low safety knowledge exhibited significantly higher N200 and N300 amplitudes. This suggests that they consumed more attentional resources and experienced stronger negative emotional responses. (2) During risk judgment, subjects with low safety knowledge exhibited lower P300 amplitude, compared to those with high safety knowledge, indicating weaker integration of risk information and allocated fewer cognitive resources to the task. (3) A model was developed to illustrate the impact of safety knowledge on risk perception. This study provides a theoretical foundation for developing more targeted and effective safety education and training programs.

Risk perception failure is one of the primary causes of unsafe behavior in high-risk industries. While traditional studies hold value, they often fail to capture the real-time, dynamic cognitive processes involved. The emergence of cognitive neuroscience technologies has prompted a paradigm shift towards interdisciplinary research, yet a systematic synthesis of this evolving field is lacking. To address this gap, this review systematically examines the application of cognitive neuroscience technologies in risk perception research within high-risk industries, based on an analysis of 83 relevant studies. Results synthesize a multi-level analytical framework encompassing neural mechanisms, influencing factors, and computational modeling. Current research, from a cognitive neuroscience perspective, investigates: 1) the risk perception two-stage (hazard identification and risk assessment) model and the functions of the corresponding brain regions; 2) the effects of individual characteristics (e.g., expertise, traits, transient states) and external environmental factors, alongside pathways for targeted safety training interventions on risk perception; and 3) the role of machine learning models for risk perception in supporting theoretical construction and their translational potential for practical application. However, limitations persist, including an imbalance in research theme, insufficient exploration of mediating mechanisms, and a disconnect from real-world scenarios. Future research should prioritize causal analysis and the enhancement of ecological validity to better integrate risk perception theory with computational modeling, fostering the development of predictive models and intervention tools deployable in field settings.

The morphology of the rock-fill interface, formed during mining and filling operations in underground mines, is directly related to the destabilization and damage of the cemented backfill under dynamic blasting loads. Previous studies often simplify the rock-fill interface to a planar shape; however, exploration results of empty areas in the quarry often characterize the rock-fill interface with jagged undulations. Applying continuum mechanics and numerical simulation software based on the finite difference method, three models of cemented backfill with different morphologies of serrated rock-fill interfaces were established as the experimental group, and one model with a flat and straight rock-fill interface was established as the control group; The time-history curve of the explosive load on the walls of equivalent cavities after rock blasting was derived and incorporated into a numerical model to simulate the two-step perimeter hole blasting in quarries. The dynamic damage response of cemented backfill under blasting loads was investigated by combining it with the backfill's damage criteria, and the influences of factors such as sawtooth width (SW) at the rock-fill interface, cement-sand ratio (CSR), side hole distance (SHD), vertical stress (σh), and others on the damage extent and mode were determined. The results show that: The damage area of the cemented backfill at a planar rock-fill interface resembles a rectangle, whereas at a jagged rock-fill interface with larger sawtooth widths, the damage area tends to approximate a rhombus, making it more prone to wedge-shaped damage; When vertical stress (σh) is similar, between two adjacent cemented backfill layers with differing CSR, the layer with the higher CSR exhibits slower attenuation of the peak vibration velocity at each mass point, resulting in a larger damage area and an increased likelihood of interlayer misalignment due to inconsistent vibration velocities; With the CSR constant, a larger σh results in a smaller damage area; Furthermore, the damage area of the cemented backfill is inversely correlated with SHD, and in engineering practice, selecting a reasonable SHD is crucial to maintaining the stability of the cemented backfill when the quantity of explosives for side holes cannot be reduced.

Differences in risk perception between frontline construction workers and managers can create communication barriers and lower the efficiency of safety management. In this study, we focused on frontline construction workers and managers and used event-related potentials (ERPs) to examine discrepancies in risk perception across two processes: hazard identification and risk judgment. During hazard identification, workers identified fewer hazards correctly than managers (p = 0.009 < 0.05). Managers also showed larger N200 amplitudes than workers (p = 0.040 < 0.05), which suggests that managers engaged conflict monitoring and inhibitory control more strongly. During risk judgment, workers responded more slowly than managers (p = 0.012 < 0.05). They also showed lower P100 (p = 0.026 < 0.05) and LPP amplitudes (p = 0.024 < 0.05), indicating weaker early visual–attentional gating and less sustained evaluative engagement with hazardous scenes. These patterns indicate that workers rely more on irrelevant information, whereas managers respond more sensitively to potential hazards. By revealing when and how role-based differences emerge, our findings offer a neurocognitive explanation for the persistent gap in risk perception and highlight specific targets for training. These insights can guide risk communication between managers and workers, extend research on risk-perception differences beyond self-report measures, and illustrate the value of ERP as a time-resolved tool for studying risk perception.

In the realm of construction safety, disparities in risk perception among workers and frontline managers impede risk communication and undermine risk management endeavors. Building upon previous research, this study zeroes in on a pivotal aspect of risk perception: risk analysis. We executed a behavioral experiment to validate the presence of discrepancies in risk analysis between workers and managers. Event-related potential (ERP) experiments were undertaken to delve into the neural underpinnings of these discrepancies. The results revealed that workers displayed a pronounced bias, extended reaction times, and diminished accuracy rates in both probability and damage judgments, in comparison with managers. Furthermore, the ERP experiment findings suggested notable differences in brain information processing regarding risk analysis between the two groups. In the probability judgment task, workers exhibited higher average amplitudes of N100 and N130, but lower P100 amplitudes than managers. During the damage judgment task, workers showed greater N130 amplitudes and reduced P100 and late positive potential (LPP) amplitudes compared with managers. These results indicated that managers allocated more attentional resources and maintained a broader attention scope during the early attention stage, whereas in the subsequent cognitive stage, they experienced stronger emotional responses and employed more cognitive resources than workers. Moreover, a risk analysis distinction model was developed to discern between the worker and manager cohorts. Employing ERP technology in this investigation enhances our comprehension of the neural mechanisms contributing to risk analysis differences and broadens the scope of related research. This study uncovers the underlying cognitive neuroscientific reasons for the observed differences in risk analysis between workers and managers. The results provide essential references for the scientific development of safety training programs by focusing on three key areas: attention allocation, emotional arousal, and distribution of cognitive resources. Additionally, it furnishes recommendations for the establishment of improved risk communication strategies between managers and workers.

A questionnaire survey and an event-related potential (ERP) experiment were used to reveal the impact of safety attitudes on risk perception. The results revealed that during hazard identification, the N130 amplitude of subjects with negative safety attitude was significantly higher, which implied that subjects with negative safety attitude were more likely to feel confused. During risk analysis, subjects with positive safety attitude were more inclined to overestimate the probability and damage degree of risks; subjects with positive safety attitudes displayed higher P150 and late positive potential amplitudes, which indicated that subjects with positive safety attitudes devoted more attention to risks in the early stage of risk analysis and had a more intense affective response in the later period. The risk judgment ability of subjects with positive safety attitude was affected by time pressure, and they exhibited higher risk judgment accuracy only under no time pressure.

Understanding how workers perceive risk is essential to construction safety management. Firstly, an event- related potential (ERP) experiment was conducted to investigate the relationship between risk, likelihood, and severity. Then, a linear model was developed to predict workers' risk perception based on ERP components and quantify the relative importance of severity to likelihood. Finally, an additive model was constructed to reflect the risk perception pattern. The results indicate: (1) Workers' emotional responses stem from the process of associating accident consequences in severity assessment, which is represented by the late positive potential (LPP) component. (2) Workers' risk perception relies more on severity compared with likelihood. (3) The additive model (risk = 0.203 * likelihood +0.758 * severity) better matches the risk perception patterns than the multiplicative model. The research results provide a new perspective for understanding workers' risk perception patterns and contributing to proactive safety management in the construction industry.

Unsafe behaviors may be aroused by inaccurate risk perception. Rather little is known about the measurement of risk perception ability in the construction industry. Based on the conception of risk perception ability, we proposed a measurement of risk perception ability, which conducted a questionnaire and a behavioral experiment. The specific measurement included an indicators system proposed to measure risk perception ability, which contained hazard identification degree, hazard identification accuracy, accuracy of the risk evaluation, deviation degree of the expected damage, deviation degree of the probability, and deviation degree of the risk evaluation. Based on the six indicators, the weighing values for each indicator were calculated, and a formula calculating risk perception ability (RPA) was proposed. Then, we applied the measurement to 327 construction professionals, which showed RPA differed from different construction professionals, and managers' RPA was significantly superior to workers (p=0.000***<0.05). The method can measure construction professionals' RPA conveniently and easily, which filled the lack of research regarding RPA in construction. Measurement results can help to provide targeted safety training recommendations, which is beneficial to reducing workers' unsafe behavior and accident rates.

At present, the research of safety science discipline is limited to the level of describing psychology and behaviors, because the cognitive neural mechanisms behind them are unknown. This paper introduces an emerging interdiscipline, namely neuro-safety science, which uses the neuroscientific methods to investigate the neural systems behind safely relevant behaviors. Qualitative methods such as literature review method and theoretical model construction method were adopted for this study. Based on the background of neuro-safety science, the definition of neuro-safety science was defined, its connotation was analyzed, and the research contents from two aspects of theoretical research and practical application research were proposed. Methodology system including research principles, research routes, research procedure and research methods, and the paradigm system of neuro-safety science were put forward. At last, the application research on neuro-safety science was forecasted. This paper opens up a new research perspective for the research of safety science, and provide guidance and reference to develop neuro-safety science.

Incidental emotions would lead to accidents by influencing risk perception. However, few works of research further studied how incidental emotions affect risk perception at the neurological level. Before the experimental task, we used video clips for emotion elicitation. Then, the event-related potential (ERP) technique was used to obtain data on the risk perception process. The results showed that: compared to neutral emotion, the participants' average reaction time was significantly shorter in positive and negative incidental emotion. Under negative incidental emotion, individuals overestimated risk and had a more significant deviation in risk perception; the amplitude of P2 and N2 components increased, and the amplitude of LPP component decreased under negative incidental emotion. Under positive incidental emotion, individuals' correct-response rate was higher. These findings indicated that incidental emotions affected the mid-term risk analysis stage and the late risk judgment stage of risk perception. In the mid-term risk analysis stage, individuals processed high-risk information with a negativity bias which led to stronger cognitive conflict, while individuals assessed risks more accurately due to a larger attentional span under positive incidental emotions. In the late risk judgment stage, individuals under negative incidental emotion devoted few attentional resources to risk information which led to a risk judgment deviation. In contrast, individuals had a more detailed cognitive process of risk information under positive incidental emotion. On these bases, this paper confirmed the influence of incidental emotions on risk perception and established an emotional information-processing model. This study provided a reference for emotional interventions to facilitate accident prevention.

IntroductionPerceived benefits are considered one of the significant factors affecting an individual’s decision-making process. Our study aimed to explore the influence mechanism of perceived benefits in the decision-making process of unsafe behaviors.MethodsOur study used the “One Stimulus-Two Key Choice (S-K1/K2)” paradigm to conduct an EEG experiment. Participants (N = 18) made decisions in risky scenarios under high perceived benefits (HPB), low perceived benefits (LPB), and control conditions (CC). Time domain analysis and time-frequency analysis were applied to the recorded EEG data to extract ERPs (event-related potentials) and EROs (event-related oscillations), which include the P3 component, theta oscillations, alpha oscillations, and beta oscillations.ResultsUnder the HPB condition, the theta power in the central (p = 0.016*) and occipital regions (p = 0.006**) was significantly decreased compared to the CC. Similarly, the alpha power in the frontal (p = 0.022*), central (p = 0.037*), and occipital regions (p = 0.014*) was significantly reduced compared to the CC. Under the LPB condition, theta power in the frontal (p = 0.026*), central (p = 0.028*), and occipital regions (p = 0.010*) was significantly reduced compared to the CC. Conversely, alpha power in the frontal (p = 0.009**), central (p = 0.012*), and occipital regions (p = 0.040*) was significantly increased compared to the HPB condition.DiscussionThe high perceived benefits may reduce individuals’ internal attention and evoke individuals’ positive emotions and motivation, leading individuals to underestimate risks. Consequently, they exhibited a greater inclination toward unsafe behaviors. However, the low perceived benefits may reduce individuals’ memory review, resulting in a simple decision-making process, and they are more inclined to make fast decisions to avoid loss. The research results can help to provide targeted intervention measures, which are beneficial to reducing workers’ unsafe behaviors.

In order to study the application of the Cobb-Douglas production function on the optimization of safety inputs and further reduce accident losses, two safety input structures of a coal mine enterprise were constructed using literature, and the weight order of each safety input indicator was determined by the entropy weight method (EWM) and the analytical hierarchy process (AHP). The Cobb-Douglas production function was used to calculate the accident loss function of the safety input structure, and the accident loss function was obtained by multiple regression analysis. The optimal configuration of safety inputs was obtained by fitting the accident loss function. Finally, the optimal loss and mean squared error (MSE) of the corresponding functions of the two safety input structures were compared. The results show that the optimal configuration of Safety Input Structure 2 is better than that of Safety Input Structure 1, and the MSE of Safety Input Structure 2 is less than that of Safety Input Structure 1. The research results demonstrate that coal enterprises can find more significant indicators by refining the safety input structure and increasing monetary resources for more crucial indicators of safety input to effectively minimize accident loss and boost economic benefits, and to test the quality of safety input structures’ regression function using MSE.

为提升安全警告标志传递的风险信息对个体的安全警示作用,从神经科学视角探究安全警示标志图案内容对个体风险感知的影响.首先从"人因"和"物因"视角探讨安全警告标志所传递风险信息的信息量与风险感知之间的关系,提出研究假设;然后通过问卷调查和事件相关电位(ERP)试验,获取单因素和双因素安全警告标志刺激下的脑电数据,并分析脑电ERP成分的差异.研究发现相较于单因素安全警告标志,双因素安全警告标志诱发的P2成分振幅更小、潜伏期更短,P3成分振幅更大,均呈显著性差异.结果表明:双因素安全警告标志更容易也更早被人注意,个体对双因素安全警告标志的风险感知程度更高,即同时包含"人因"和"物因"风险信息的安全警告标志更容易吸引人的注意,且传递的信息风险度更高,发挥的刺激警示作用更加明显.

The risks faced by the mining industry have always been prominent for every walk of life in China. As the direct cause of accidents, individual unsafe behaviors are closely related to their risk perception. So, it is important to explore the factors affecting miners’ risk perception and analyze the influencing mechanisms between these factors and risk perception. The questionnaire survey method was used to collect the data of risk perception from nearly 400 respondents working in metal mines in China. Exploratory factor analysis and confirmatory factor analysis were used to analyze and process collected data. The impact of four factors affecting miners’ risk perception was verified, namely: organizational safety atmosphere, organizational trust, knowledge level, and risk communication. Then, regression analysis, Pearson correlation analysis, and structural equation model analysis were used to examine the effect of the four influencing factors on miners’ risk perception. The four influencing factors all have a positive impact on miners’ risk perception; knowledge level has the largest explained variation of miners’ risk perception, followed by risk communication. Organizational trust and organizational safety atmosphere have an indirect and positive impact on miners’ risk perception intermediated by knowledge level and risk communication. The results offer four important aspects of mine safety management to help miners establish quick and accurate risk perception, thereby reducing unsafe behaviors and avoiding accidents.

Non-coal-mining accidents occur frequently in China, and individual unsafe behaviors are the direct cause. The cognitive diversity of practitioners in the non-coal-mining industry leads to various behaviors in work and hinders communication between groups. The aim of this study is to analyze the differences in risk perception (accidents and occupational diseases) between non-coal-mining practitioners (experts, miners, and managers) and to explore the contributing factors. The questionnaire survey method was used to collect the data on risk perception and influencing factors from 402 respondents working in non-coal mines and universities in China. Project analysis and exploratory factor analysis were used for preprocessing. A t-test and linear regression analysis were used to test the significance of the differences and assess the function of the factors, respectively. Regarding risk perception, two risks both have significant differences between the three groups. With the perceptions of accidents and occupational diseases ranked from high to low, the order of the practitioners is as follows: managers (3.88), experts (3.71), miners (3.55) and experts (4.14), miners (3.90), and managers (3.88). Regarding the influencing factors, risk attitude, risk communication, educational level, enterprise trust, and occupational satisfaction have great effects on the three groups. More precisely, three groups have different important predictors. Risk attitude has the greatest impact on miners (0.290) and experts (0.369), but sensibility preference has the greatest impact on managers (0.518). In summary, cognitive discrepancies are common among non-coal-mining practitioners, but the degree of deviation varies with the type and dimension of the risk. There are six factors that have a significant impact on all practitioners, but the effect is limited by specific risks and groups.

Leadership is a necessary element for ensuring workplace safety. Rather little is known about the role of leadership safety behaviours (LSBs) in the mining industry. Using regression analysis and structural equation modelling analysis, this study examined the cause-and-effect relationships between leadership safety behaviours and safety performance. Data were collected by questionnaires from 305 miners in China. Data were analysed using exploratory factor analysis and confirmatory factor analysis, which identified five main dimensions of LSBs: safety management commitment, safety communication with feedback, safety policy, safety incentives, and safety training; the analysis also identified three main dimensions of safety performance: employee's safety compliance, safety participation, and safety accidents. The results showed the overall effects of each LSB variable on safety compliance in descending order as: safety training (0.504), safety incentives (0.480), safety communication with feedback (0.377), safety management commitment (0.281), and safety policy (0.110). The overall effects of each LSB variable on safety participation in descending order were: safety training (0.706), safety incentives (0.496), safety management commitment (0.365), and safety policy (0.247). Furthermore, we found that safety management commitment and safety incentives increased employees' safety behaviours, but this influence was mediated by safety training, safety policy, and safety communication with feedback.

The efficiency of contact search is one of the key factors related to the computational efficiency of three-dimensional sphere discontinuous deformation analysis (3D SDDA). This paper proposes an efficient contact search algorithm, called box search algorithm (BSA), for 3D SDDA. The implementation steps and data structure for BSA are designed, with a case study being conducted to verify its efficiency. The data structure also has been improved for parallelizing the computation in contact search. For the demonstration of the proposed algorithm (BSA), six cases with various sphere numbers are simulated. Simulation results show that the time consumed in contact search using BSA (CTofBSA) is much less than that by the direct search algorithm (DSA) (CTofDSA). For the case with 12,000 spheres, CTofBSA is 1.1[Formula: see text]h, which is only 1.3% of CTofDSA (84.62[Formula: see text]h). In addition, the proportion of the computation quantity of contact search in the entire computation (Pcs) is 91.3% by using DSA, while this value by BSA is only 12.4%, which demonstrates the contribution of BSA. The efficiency brought about by BSA (time consumed and computation quantity) may enable 3D SDDA to simulate large-scale problems.

To gain a better understanding of the interaction between weak inclusions and jointed rock masses, a conceptual model containing a joint set and an opening is prepared, and cases involving unfilled and filled openings are considered. The influence of weak inclusions on the fracturing and fractal behavior of these models is investigated by using laboratory experiments, the rock failure process analysis (RFPA) code and the fractal geometry. An overhanging beam model is proposed to explain the initiation mechanism of tensile cracks around the unfilled and filled openings. The RFPA simulations provide deep and quantitative insight into the fracturing behavior. The coalescence patterns of the surrounding rock mass obtained from physical and numerical tests are in reasonable agreement, and can be classified into two categories: shearing along the joint set and cutting through the rock bridges. The inclusion with a low strength exerts little influence on the failure pattern but has an appreciable reduction in the stress values and an appreciable increment in the mechanical properties. The crack coalescence exhibits fractal properties, and there are good correlations between the fractal dimension and mechanical properties of these jointed rock specimens containing unfilled and filled openings.

This paper proposes a sphere-triangle contact model for three-dimensional sphere discontinuous deformation analysis to simulate complex boundary face problems often encountered in engineering. First, the detection of sphere-triangle contact and the calculation of corresponding sphere-triangle contact submatrices for building a sphere-triangle contact model are described. Then, the correctness of the proposed model is preliminarily verified by simulating the sphere-plane/edge/vertex contact, sphere-triangle friction problem and a complex boundary face problem. Furthermore, a rigorous open-close iteration is proposed to guarantee the correctness of setting penalty springs for sphere-triangle contacts, to ensure computational stability of three-dimensional sphere discontinuous deformation analysis. Meanwhile, two imperceptible fatal errors (i.e., direction deflection error and oscillation error) hidden in the sphere-triangle contact model are estimated, which reveals that a larger sphere or a higher collision velocity at a sphere-triangle contact leads to larger errors. In addition, an algorithm for excluding invalid contacts is proposed to avoid these fatal errors, enhancing the robustness of the proposed contact model. Finally, a landslide simulation with 80,974 spheres, 76,382 triangles and 150,0 0 0 calculation steps is conducted, further validating the proposed model in large-scale simulations. This work indicates the feasibility of the proposed model for simulating complex boundary face problems. (c) 2021 Elsevier Inc. All rights reserved.